AIDY
MDPI
Article
LCAT in Cancer Biology: Embracing Epigenetic Regulation, Immune Interactions, and Therapeutic Implications
Manzhi Gao 1,2(D, Wentian Zhang 1,2, Xinxin Li 1,2, Sumin Li 1,2, Wenlan Wang 1,2,*[D and Peijun Han 1,2,*
1 Department of Aerospace Hygiene, School of Aerospace Medicine, Air Force Medical University, Xi’an 710032, China; manzhi1535675200@163.com (M.G.); wentian509@163.com (W.Z.); lxx1628180098@163.com (X.L.); lisumin15229806900@outlook.com (S.L.)
2 Key Laboratory of Aerospace Medicine of Ministry of Education, Air Force Medical University, Xi’an 710032, China
* Correspondence: ypwl821@fmmu.edu.cn (W.W.); peijunhan@fmmu.edu.cn (P.H.); Tel .: +86-13379265744 (W.W.); +86-15094070246 (P.H.)
☒ check for updates
Academic Editor: Apostolos Zaravinos
Received: 8 January 2025
Revised: 31 January 2025 Accepted: 4 February 2025 Published: 10 February 2025
Citation: Gao, M .; Zhang, W .; Li, X .; Li, S .; Wang, W .; Han, P. LCAT in Cancer Biology: Embracing Epigenetic Regulation, Immune Interactions, and Therapeutic Implications. Int. J. Mol. Sci. 2025, 26, 1453. https://doi.org/ 10.3390/ijms26041453
Copyright: @ 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).
Abstract: Lecithin cholesterol acyltransferase (LCAT) is a crucial enzyme in high-density lipoprotein (HDL) metabolism that is often dysregulated in cancers, affecting tumor growth and therapy response. We extensively studied LCAT expression in various malignan- cies, linking it to clinical outcomes and genetic/epigenetic alterations. We analyzed LCAT expression in multiple cancers and used the Cox regression model to correlate it with patient survival metrics, including overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). We also examined the copy number variations (CNVs), single-nucleotide variations (SNVs), DNA methylation, and N6-methyladenosine (m6A) modifications of LCAT and their connections to tumor immune responses and drug sensitivity. LCAT expression varies among cancers and correlates with patient outcomes. Low expression is linked to poor prognosis in low-grade glioma (LGG) and liver hepa- tocellular carcinoma (LIHC), while high expression is associated with better outcomes in adrenocortical carcinoma (ACC) and colon adenocarcinoma (COAD). In kidney re- nal papillary cell carcinoma (KIRP) and uterine corpus endometrial carcinoma (UCEC), LCAT CNV and methylation levels are prognostic markers. LCAT interacts with m6A modifiers and immune molecules, suggesting a role in immune evasion and as a biomarker for immunotherapy response. LCAT expression correlates with chemotherapeutic drug IC50 values, indicating potential for predicting treatment response. In ACC and COAD, LCAT may promote tumor growth, while in LGG and LIHC, it may inhibit progression. LCAT expression and activity regulation could be a new cancer therapy target. As a key molecule linking lipid metabolism, immune modulation, and tumor progression, the potential of LCAT in cancer therapy is significant. Our findings provide new insights into the role of LCAT in cancer biology and support the development of personalized treatment strategies.
Keywords: LCAT; HDL; cancer metabolism; immune modulation; epigenetic alterations; personalized cancer therapy
1. Introduction
LCAT facilitates the esterification of free cholesterol and its storage in the core region of HDL particles, promoting the maturation and size expansion of HDL particles. This process not only provides substrates for cholesterol reverse transport mediated by cholesterol ester transfer protein but also enhances the functionality of HDL particles. Notably, over 90%
of cholesterol esters in plasma are generated through LCAT catalysis. In addition to its pivotal role in cholesterol esterification, LCAT also hydrolyzes phosphatidylcholine and oxidized platelet-activating factors, thereby effectively safeguarding platelet function and the antioxidant capacity of HDL [1]. Cholesterol, an essential lipid molecule in living organisms, plays a crucial role in maintaining cell membrane integrity, regulating mem- brane fluidity, and participating in bile acid and steroid hormone synthesis. Recent studies have revealed that tumor cells reprogram cholesterol metabolism pathways to meet the demands of rapid proliferation. This metabolic reprogramming not only directly influences the biological behaviors of tumor cells, such as proliferation, invasion, and metastasis, but also modulates immune cell function by altering cholesterol distribution within the tumor microenvironment, thereby affecting the body’s anti-tumor immune response. These find- ings provide new insights for developing tumor treatment strategies based on cholesterol metabolism regulation [2,3]. Recent research has begun to shed light on the role of LCAT in cancer biology, with findings indicating that LCAT activity may be disrupted in a variety of malignant tumors, potentially affecting tumor progression and response to therapy [4-6]. However, the exact mechanisms by which LCAT contributes to carcinogenesis, as well as its clinical significance across various cancer types, have yet to be fully understood. Because of its function in reverse cholesterol transport and antioxidant action, HDL is frequently referred to as “good cholesterol” [7]. According to new research, HDL malfunction may be linked to a higher risk of cancer and a worse prognosis [8]. An essential enzyme for the maturation and proper operation of HDL is LCAT [9].
Our knowledge of the function of LCAT in cancer is severely constrained by the paucity of thorough studies on LCAT expression and activity across different tumor types. This study uses systematic approaches to clarify the roles of LCAT in tumor growth in light of the diversity of cancer and the intricacy of lipid metabolic pathways. The aim of this study is to investigate LCAT expression patterns in different cancer types and their correlation with clinical outcomes, like survival. Additionally, we examined the genetic and epigenetic modifications of LCAT in cancer and their impact on the biological properties of tumors.
We examined LCAT expression in different cancer types using extensive genomic databases and cutting-edge bioinformatics methods, and we connected the findings to clinical characteristics. Our findings show intricate LCAT expression patterns. In some malignancies, prior research has demonstrated a strong correlation between LCAT levels and patient prognosis [8,10-14]. We further investigated the genetic underpinnings of LCAT dysregulation, including single nucleotide and copy number variations, and their possible effects on the development and spread of tumors. Additionally, we also looked at the potential of LCAT as a predictive biomarker of immunotherapy response and its connection to the immune response in the tumor microenvironment.
The goal of this research is to present a thorough analysis of the participation of LCAT in cancer while also shedding light on its therapeutic relevance and mechanisms of action. Understanding the tricky relationship between LCAT, lipid metabolism, and most cancers’ progression is fundamental for the improvement of centered treatments and might also pave the way for customized therapy techniques that harness the doable of modulating LCAT recreation in most cancer treatments.
2. Results
2.1. LCAT Expression Analysis in Normal and Tumor Tissues
In Figure 1A, our analysis of LCAT expression in normal tissues revealed that LCAT is most highly expressed in normal liver tissue and least in bone marrow tissue. Further analysis of LCAT expression in multiple tumors from the TCGA database found that LCAT expression is highest in Brain Lower Grade Glioma tissue (Figure 1B). Compared
THCA, and UCEC tissue samples.
Differential expression analysis of LCAT between tumor tissues and paired normal tissues yielded similar results (Figure 1D). LCAT is lowly expressed in BRCA, LIHC, LUAD, PRAD,
(HNSC), KIRC, KIRP, and stomach adenocarcinoma (STAD) tumor tissues (Figure 1C).
UCEC tumor tissues; LCAT expression is significantly higher in COAD, esophageal carci- noma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma
to normal tissues, LCAT expression is reduced in various tumors. Notably, LCAT expression is significantly lower in breast invasive carcinoma (BRCA), cholangio carcinoma (CHOL), kidney chromophobe (KICH), LIHC, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), thyroid carcinoma (THCA), and
A
200
nTPM
150
100
50
0
B
Liver
Choroid plexus
Skin
Cerebral cortex
10
Seminal vesicle
¿
Placenta
log2 (TPM+1)
Lung
6
Endometrium
₹
Heart muscle
<
Adipose tissue
Spleen
0
Thyroid gland
¡
Skeletal muscle
C
H
Smooth muscle
ACC
The expression of LCAT Log2 (TPM+1)
Adrenal gland Gallbladder
BLCA
·
&
BRCA
Prostate
S
0
Expression of LCAT across TCGA tumors
3 of 25
Ovary
0
CHOL
F.
Thymus
Epididymis
2
COAD
Tongue
2
DLBC
Pancreas
2
Y
ESCA
Appendix
D
G&M
FM
Parathyroid gland
HNSC
Fallopian tube
The expression of LCAT Log2 (TPM+1)
ACC
Cervix
BLCA
KICH
-
Lymph node
BRCA
$
KIRC
Stomach
a
CESC
KIRP
Urinary bladder
CHOL
·
LGG
Esophagus
₹
Salivary gland
Is
COAD
Ov
2
DLBC
**
1,
Small intestine
MESO
Colon
*
ESCA
TCGA samples
LIHC
Breast
ns
GBM
I
*
LUAD
Duodenum
HNSC
Figure 1. Differential expression analysis of LCAT in various tissues. (A). Expression of LCAT in normal tissues. (B). Expression of LCAT across TCGA tumors. (C). Differential expression of LCAT
BLCA
KICH
LUSC
Kidney
BRCA
KIRC
PAAD
Rectum
between normal and tumor tissues. (D). Differential expression of LCAT between tumor tissues and
A4
Testis
paired adjacent normal tissues. * p<0.05; ** p < 0.01; *** p < 0.001; ns p < 0.05.
1
PRAD
Tonsil
CESC
KIRP
¥
*
LAML
1
PCPG
Bone marrow
CHOL
LGG
READ
COAD
LINC
SARC
2
LUAD
.
SKCM
ESCA
1
LUSC
his
LAML
HNSC
MESO
TGCT
OV
KICH
THCA
PAAD
THYM
KIRC
PCPG
F
STAD
PRAD
KIRP
UCEC
READ
LIHC
ns
SARC
UCs
SKCM
*
M
UVM
LUAD
1s
STAD
LUSC
TOCT
PAAD
THCA
THYM
PCPG
5
UCEC
Tumor
Normal
PRAD
UCS
UVM
READ
TIS
SARC
SKCM
STAD
99
THCA
THYM
Tumor
Normal
UCEC
Comparing LCAT expression across different pathological stages in 33 tumors revealed that LCAT expression in COAD is significantly higher in stage 3 compared to stage 2; LCAT expression in LIHC is significantly lower in stages 2, 3, and 4 compared to stage 1 (Figure S1A,B).
As shown in Figure 2A, the immunohistochemical results from the HPA dataset in- dicate that LCAT is lowly expressed in BRCA, LIHC, LUAD, PRAD, THCA, and UCEC tumors, which is consistent with the findings from the Xiantao Academic website. Im- munofluorescence experiments showed that LCAT is located in the nuclei of cervical cancer cell line A431 and glioblastoma cell line U-251 MG and is almost not expressed in malignant bone tumor cell line U20S (Figure 2B). Protein localization data generated from the Human Protein Atlas also indicate that LCAT is primarily localized in the nucleus (Figure S2).
A
BRCA
LIHC
LUAD
PRAD
THCA
UCEC
Staining: low
Staining: low
Staining: low
Staining: low
Staining: low
Staining: low
Staining: low
Staining: low
Staining: low
Staining: low
Staining: low
Staining: low
B
A 431
Nucleus
Microtubules+LCAT
Merge
Nucleus
Microtubules+LCAT
Merge
U2OS
U-251MG
2.2. Prognostic Analysis of LCAT in Tumors
To further understand the prognostic value of LCAT in different tumors, we analyzed the correlation between LCAT expression and OS, DSS, and PFI in tumor patients using
univariate Cox regression analysis. The results indicated that in KICH, LGG, LIHC, and Thymoma (THYM) patients, low LCAT expression is associated with poor OS prognosis. In ACC, COAD, kidney renal clear cell carcinoma (KIRC), and mesothelioma (MESO) patients, high LCAT expression is associated with poor OS prognosis (Figure 3A). Figure 3B analyzed the correlation between LCAT expression and DSS in tumor patients, finding that the correlation between LCAT expression and DSS in ACC, COAD, LGG, LIHC, and MESO patients is the same as with OS. Figure 4 shows the correlation between LCAT expression and PFI in tumor patients; we find that the correlation between LCAT expression and PFI in ACC, COAD, KICH, LGG, LIHC, and THYM patients is the same as with OS. The prognostic data of LCAT expression and OS, DSS, and PFI in 33 tumor patients are shown in Figure S3.
A
* p < 0.05
LCAT - Overall Survival
log10(HR)
·
.
.
*
.
.
.
+
0.5
ACC BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
0.0
-0.5
LCAT - Low - High
LCAT - Low - High
LCAT - Low - High
LCAT
Low
High
1.0
1.0
1.0
1.0
ACC
COAD
A
KIRC
Survival probability
0.8
Survival probability
0.8
Survival probability
09
Survival probability
0.8
0.6
0.6.
0.6
0.8
++
0.4
#
+++++
Overall Survival HR = 4.55 (1.90
10.88)
Overall Survival HR = 1.76 (1.18
Overal Survival + HR = 0.11 (0.01
0.4
Overall Survival HR = 1.43 (1.05 … 1.92)
0.2
P < 0.001
0.4
P= 0.005
2.61}+
0.7
P= 0.036
0.87)
P= 0.021
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
Time (months)
Time (months)
150
Time (months)
Time (months)
LCAT - Low - High
LCAT - Low - High
LCAT - Low - High
LCAT - Low - High
1.00
1.00
1.00
1.0
LGG
LIHC
MESO
THYM
Survival probability
0.75
Survival probability
0.75
Survival probability
0.75
Survival probability
0.9
0.8-
0.50
0.50
0.50
0.7
0,25
0.25
Overall Survival HR = 0.03 (0.44
0.25
0.6
P=0.008
0.08)
Overall Survival HR = 0.51 (0.30
0.72)
Overall Survival HR = 1.61 (1.00-+-2.50)
Overall Survival
HR = 0.12 (0.02 … 0.90)
0.00
P < 0.001
0.00
P=0.048
1
0.5
P=0.048
0
50
100
150
200
0
30
60
90
120
0
25
50
75
0
50
100
Time (months)
Time (months)
Time (months)
Time (months)
150
B
* p < 0.05
LCAT - Dieeace Specific Survival
log10(HR)
*
*
*
+
*
5
ACC BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM HNSC
KICH
KIRC
KIRP
LGG
LIHC
LUAD
LUSC MESO
OV
PAAD
PCPG
PRAD READ
SARC
SKCM
STAD
TGCT
THCA THYM
UCEC
UCS
UVM
0
-5
LCAT - Low - High
LCAT - Low - High
LCAT - Low - High
1.0
1.0
1.00
ACC
COAD
LGG
Survival probability
0.8
Survival probability
0.9
Survival probability
0.75
0.6
0.8-
0.50
0.4
Disease Specific Survival HR = 4.25 (1.75
0.7
Disease Specific Survival HR = 1.82 (1.10 … 3.03)
0.25
Disease Specific Surviva HR = 0.60 (0.42 … 0.87)
P=0.001
10.31)
P=0.020
4
0.2
0.6
++
P=0.007
0
50
100
150
0
50
100
150
0
50
100
150
200
Time (months)
Time (months)
Time (months)
LCAT - Low - High
LCAT - Low - High
1.0
1.00
LIHC
MESO
Survival probability
0.8
Survival probability
0.75
0.6
0.50
0.4
0.25
Disease Specific Survival HR = 0.42 (0.27 … 0.67)
Disease Specific Survival HR = 1.88 (1.02 … 3.42)
< 0.001
0.00
P=0.044
0
30
80
90
120
0
25
50
75
Time (months)
Time (months)
* p < 0.05
log10(HR)
LCAT - Progress Free Interval
*
*
*
*
*
*
0.4
0.0
ACC BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
-0.4
LCAT - Low - High
LCAT - Low - High
LCAT - Low - High
LCAT - Low - High
1.0
1.0
1.0
Ne
1.00
ACC
COAD
KICH
LGG
Survival probability
0.9
0.8
Survival probability
Survival probability
0.8
0.9
Survival probability
0.75
0.7
0.6
0.8
0.50
0.6
0.4
Prograss Free Interval
HR = 218.(1.40 … 5.16)
0.5
Progress Free Interval HR = 1.56 (1.10 … 2:22)
0.7
Progress Free Interval
0.25
Progress Free Interval
P= 0.003
P = 0.013
HR = 0.18 (0.04 .
0.85)
HR = 0.62 (0,47 … 0:02)
0.4
P = 0.030
P < 0.001
0
50
100
150
0
50
100
150
0
50
100
150
0
40
80
120
160
Time (months)
Time (months)
Time (months)
Time (months)
LCAT - Low - High
LCAT - Low - High
1.0
1.0
LIHC
THYM,
Survival probability
0.8
Survival probability
0.8
0.6
0.6
0.4
Progress Free Interval
HR = 0.60 19.45
Progress Free Interval
P < 0.001
0.4
HR = 0.20 (0.07 … 0.60)
0.2
P = 0.004
0
30
60
90
120
0
50
100
150
Time (months)
Time (months)
2.3. CNV and SNV Genetic Analysis of LCAT in Tumors
CNV is a type of genomic variation where DNA segments exist in different copy numbers within an individual’s genome [15]. CNV can lead to the overexpression or loss of genes, thereby affecting gene function and phenotype [16]. The CNV pie chart shows the composition of the heterozygous/homozygous CNVs of the LCAT gene in 33 cancers (Figure 5A). The ACC samples have the highest percentage of samples with total copy number gain and heterozygous gain; the ovarian serous cystadenocarcinoma (OV) samples have the highest percentage of samples with total copy number loss, heterozygous loss, and homozygous loss; the acute myeloid leukemia (LAML) samples show no copy number gain; the THCA samples have the lowest percentage of samples with copy number loss. Additionally, the CHOL samples have the highest percentage of samples with homozy- gous gain. Our analysis of the correlation between LCAT CNV and mRNA expression in 33 tumors found that LCAT CNV and mRNA expression are positively correlated in blad- der urothelial carcinoma (BLCA), BRCA, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC), ESCA, GBM, HNSC, KIRP, LGG, LIHC, LUAD, LUSC, OV, SARC, skin cutaneous melanoma (SKCM), STAD, testicular germ cell tumors (TGCTs), THYM, UCEC, and uveal melanoma (UVM) tumors (Figure 5B). In other tumor types, there is no significant correlation between LCAT CNV and LCAT mRNA expression (Table S1). Further evaluation of the effect of LCAT CNV on cancers with the most affected person prognosis showed that LCAT CNV is associated with the prognosis of sufferers with COAD, KICH, KIRP, pheochromocytoma and paraganglioma (PCPG), PRAD, UCEC, UVM, CESC, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), ESCA, LGG, MESO, SARC, THCA, and THYM tumors (Figure S4). Compared to patients with LCAT copy number loss, patients with LCAT wild-type KIRP have better OS, PFS, DSS, and DFI prognosis (Figure 5C); compared to patients with LCAT copy number loss and gain, patients with LCAT wild-type UCEC have better OS, PFS, DSS, and DFI prognosis (Figure 5D).
A
CNV percentage in each cancer
ACC
KIRP
KICH
COAD
KIRC
READ
PAAD
HNSC
DLBC
THCA
BLCA
ESCA
PCPG
LGG
CESC
CHOL
LAML
MESO
THYM
LUAD
GBM
STAD
LUSC
SKCM
TGCT
UVM
UCEC
PRAD
LIHC
SARC
UCS
BRCA
Ov
Hete. Amp.
Homo. Amp.
Hete. Del.
LCAT
Homo. Del.
None
B
Spearman correlation between LCAT CNV and MRNA expression in BLCA
Spearman correlation between LGAT CNV and
mRNA expression in BRCA
Spearman correlation between LCAT CNV and MRNA expression in CESC
Spearman correlation between LGAT CNV and MRNA expression in ESCA
2
*
,
*
2
8
CNV
8
.
0
0
*
-
A
+
-1
-1
25
50
Expression log ?(R-SEM]
12.5
25
Expression log?( RSCM]
75
Expression log (R-SEM)
Expression loga(R-SEM)
Spearman comrelation between LCAT CNV and HRNA expression in GBM
Spearman correlation between LCAT CNV and
Cor. = D.27
mRUNNA expression in HNSC
Spearman correlation between LCAT CNV and BRNA expression in KIRP
Spearman correlation between LCAT CNV and
TRINA expression in LGG
2
2
Cor. = 0.15
as
FDRt - 1:2-18-
15
4
ao
AND
6
17
a
-10
.
-15
Expression loga(R:SEM)
10
11
Experirion lopd(RSEMI
Expression loga(SEM)
Exprimition logo( SEM)
Spearman correlation between LCAT CNV and #RNA expression in LIHC
Spearman correlation between LCAT CNV and MRNA expression in LUAD
Spearman camrelation between LCAT CNV and mRNA expression in LUSC
Spearman correlation between LCAT CNV and MRNA expression in OV
Car. = 0.2
1
Car. = 0.27
15
*
10
FOR = 4 7%10 -*
.
10
8
CNV
1 6
CNV
06
ONV
10
aa
OD
-05
-45
-10
-1.0
6
-10
Expression loga(R:SEM)
12
Expression log21RSEMI
6
Expression log RSEM)
Expression log(RSEM)
Spearman correlation between LCAT CNV and BRNA expression in SARC
Spearman correlation between LCAT CNV and mRNA expression in SKCM
Spearman comrelation between LCAT CNV and
Spearman correlation between LCAT CNV and
K
mRNA expression in STAD
MRNA expression in TGCT
FDA =1.2×108
FOR =9. 1x10-
2
1.0
3
ao
7
a
CNV
a
0
D
-1.0
-1
1
-1
Expression loga(R(SEM)
tb
Expression log@( RSEM)
Expression log R SEM)
B
Expression log2(R:SEM)
10
11
Spearman correlation between LCAT CNV and HRNA expression in THY’M
Spearman correlation between NANA expression in Legg 0-CAT CNV and
Spearman comrelation between LCAT CNV and RNA expression in UCS
10
Cor. = D.ZT
1.0
Cor. = D.45
ao
**
15
0.5
5
2 4
¿
ao
·
-46
45
:
-10
-1.0
-4
.
4
Expression loga(R:SEM)
3
Expression log 21R SEMI
5
Expression loga( R:SEM)
G
C
ÚS & LČAT CNV in KIRP
PFS af LCAT CNV in IORP
DSS af LCAT CNV in KIRP
DFI DILCAT CNV in IORP
1.00-
1.00
1.00
1.00
W
0.75
0.75
0.75
OS probability
PPS probilibility
DSS probability
0.75
DFI probability
50
-
50
50
0.25
9.2
ppkP value”
0 25
Lngrynk P value = 2.0x-07
0.25
0.00
1
0.00
I
0.00
1
0.00
I
1
0
Time [month]
150
u
50
Time (month)
150
D
50
Time [month]
150
ú
25
Time (mrionth]
SS
100
125
D
08 of LCAT CNV in UCEC
PFS of LOAT CNV in UCEC
DES of LCAT CNV in UCEC
DFI ofLCAT CNV in UCEC
1.00-
1.00-
1.00-
1.00-
0.75
0.75
0.75
0.75
OS probability
PFS probability
DBS probability
DFI probability
50
+ MA, I-TE
1.50
0.25
11
Logrank Pwalut
0.25
Logank Pyg 200
0.25
Logrank: Pwakat
0.0012
0.25
Logrank: P value = 0.083
1
1
0.00
11
A
0.00
0
50
100
150
Time [month]
200
290
0
50
500
Time (month)
200
250
1
50
100 Time [month]
200
290
D
100
150
Time [month]
200
250
Single nucleotide variant (SNV) mutations refer to variations where a single nucleotide, the basic unit of DNA, undergoes a change [17]. SNVs can lead to gene mutations and affect patient prognosis. Figure S5 summarizes the expression profiles of SNV mutations in various tumors, with UCSC having the highest proportion of harmful mutations. Figure 6A shows the mutation sites, types, and counts of the LCAT gene in UCEC, KICH, COAD, CESC, STAD, and LIHC. Figure 6B summarizes the SNV categories of the LCAT gene in the genomes of UCEC, KICH, COAD, CESC, STAD, and LIHC. Further analysis of the relationship between LCAT SNV and tumor patient prognosis found that LCAT SNV mutations are not significantly correlated with patient prognosis (Figure 6C).
A
UCEC
ab
KICH
Mutation Hate: 1.41%
COAD
*
4
1
-
*
-
-
-
-
-
-
-
Não
CESC
STAD
LIHC
1
1.
5
1.
1-
-
-
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-
-
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-
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-
100
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UCEC
KICH
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CESC
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LIHC
Variant Classification
Variant Classification
Variant Classification
Variant Classificatian
Variant Classification
Variant Classification
Missonse_Mutation
Museete_Moavnon
Miananse_Mutatie
Frame_Sbet_Del
Museote_Matation
Miananse_Mutatie
Minnenna_Mutation
Frame_She_Au
Frame_SAIR_Dal
FABRY_STIL_Ans
0 4
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UCEC
KICH
COAD
CESC
STAD
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Variant Type
Variant Type
Variant Type
Variant Type
Variant Type
Variant Type
SNP
SMP
SNP
INS
SNP
SNP
SNP
DEL
DEL
WS
0
N
0
a
9 9
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4
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3
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N
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0
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D
UCEC
KICH
COAD
CESC
STAD
LIHC
SNV Class
SNV Class
SNV Class
SNV Class
SNV Class
SNV Class
T>G
0
T>G
0
TOG
0
TOG
0
T>G
0
T>G
1
TSA
0
T>A
T>A
0
T>A
0
T>A
0
T>A
0
T>C
1
T>C
0
T>C
1
T>C
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8
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COT
3
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J
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*
C>G
0
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0
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E
Survival difference between mutant and WT(LCAT)
DFI
DSS
OS
PFS
LogIFORI
O 1
O
s
BLCA
CESC
COAD
LIHC
STAD
UCEC
BLCA
CESC
COAD
LIHC
STAD
UCEC
BLCA
CESC
COAD
LIHC
STAD
UCEC
BLCA
CESC
COAD
LIHC
STAD
UCEC
Còn P value
F
EPCAM
*
*
*
*
*
*
*
*
*
*
*
*
-
*
¥
*
*
*
*
* p < 0.05
MLH1
*
*
*
*
*
*
*
*
Cor
1.0
MSH2 *
*
*
*
*
*
*
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*
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-
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*
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PMS2
*
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4
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*
ACC BLCA
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BRCA
CESC
CHOL
COAD
DLBC
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GBM
HNSC
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KIRP
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LIHC
LUAD
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SARC SKCM
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THCA
THYM
UCSC
UCS
UVM
MMR genes play a vital function in DNA repair. When the expression of these genes is affected, it may lead to unrepaired DNA mismatches, causing genomic instability and the accumulation of mutations [18,19]. Therefore, we assessed the correlation between LCAT expression and the mutation levels of five MMR genes. The results confirmed that LCAT is significantly negatively correlated with MMR genes in BRCA, LIHC, PCPG, and PRAD tumors; LCAT is significantly positively correlated with MMR genes in ACC, GBM, OV, and UVM tumors (Figure 6D).
2.4. LCAT Methylation Analysis
Gene methylation refers to the process of adding a CH3 group to the cytosine residue in DNA [20]. This process usually occurs in the promoter regions of genes, and methylation
can lead to gene silencing, which can affect cellular function [21-23]. Our analysis of LCAT methylation level differences in different tumors found that compared to normal tissues, LCAT is hypermethylated in BRCA, COAD, HNSC, LUSC, PRAD, and UCEC and hypomethylated in KIRC, KIRP, and pancreatic adenocarcinoma (PAAD). In BLCA, LIHC, LUAD, and THCA, there is no big distinction regarding LCAT methylation between normal and tumor tissues (Figure 7A). Except for BLCA, CESC, CHOL, COAD, DLBC, ESCA, KICH, KIRP, LAML, OV, PAAD, rectum adenocarcinoma (READ), TGCT, and THYM, LCAT mRNA expression and methylation levels are significantly and drastically correlated in various other tumors (Figure 7B). We analyzed the correlation between LCAT methylation tiers and tumor patient prognosis and determined that in STAD, COAD, GBM, LIHC, LGG, KICH, SARC, and UVM, LCAT methylation is related to patient prognosis (Figure S6). Compared to the LCAT hypomethylation group, the LCAT hypermethylation group in LGG, LIHC, SARC, and UVM patients has poorer OS, DSS, and PFS (Figure 7C).
A
LCAT methylation across TCGA cancer types
-
1.00-
1
T
=
3
=
A
+
0.75-
11
Methylation (Beta value)
Types
Normal
0.50-
Tumor
0.25
ACC
BLCA
BRCA
CESC
CHOL
COAD
OLBC
ESCA
GBM
MINSC
KICH
KIRG
KIRP
LAML
LGG
LINC
LUAD
LUSC
MESO
BRAD
PCPG
PRAD
READ
SARC SKCM
ON
STAD
GCT
THCA
THYM
UCEC
UCS
UJVM
B
p < 0.05
Cor
1.0
LCAT
.
0.5
00
-0.5
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSO
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
-1.0
C
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D55 of LCAT methylthan in LOG
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DPI eILCAT milfplaton in Loo
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035
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DAI prabubility
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ZA
3
125
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025
Logrark P value =
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açının
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6
6
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1.25
Lagrank P value =
2
Logrank P value = @ in
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Log
F
5
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130
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5
30
Time orwonanı
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DSS ofi CAT methylasian in BARC
PFR ofLCAT methylation in SARC
DFI eti CAT methylation in SARC
1.00
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PFS probability
DFI probability
D.JS
08 pmbobilty
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5
6
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im-
0.75
PF8 probability
D.TE
90
6
1.25
Logrank P value = 0.00
Logrank P value = D.lla
1
Logran k P value = 0.0
00
A
0.00
I
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I
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0
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20
60
0
30
40
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80
D
* p < 0.05
DNMT1
*
.
.
.
.
.
.
.
*
*
.
.
.
.
.
Cor
1.0
DNMT3A
*
.
.
*
.
*
*
.
*
*
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0.5
DNMT3B
.
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BLCA
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CHOV
LOAD
DLBC
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LIHC
LUAD
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READ
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SKCM
STAD
-0.5
IGOT
THCA
THYM
VCSC
UCS
UVM
-1.0
DNA methyltransferases (DNMTs) can add methyl groups to cytosines, leading to chromatin compaction and preventing the binding of transcription factors, thereby silenc- ing related genes [24,25]. In tumors, this silencing may affect the expression of certain genes in tumors, thereby affecting tumor development [26]. We further analyzed the relationship between LCAT and four DNMTs. In ACC, BLCA, BRCA, CHOL, COAD, ESCA, GBM, KICH, KIRC, KIRP, LGG, LUAD, LUSC, OV, PAAD, READ, SKCM, STAD, THCA, and UVM tumors, LCAT expression is highly positively correlated with the four DNA methyl- transferases; in LIHC, PRAD, TGCT, and THYM, LCAT expression is highly negatively correlated with the four DNMTs (Figure 7D).
2.5. Correlation Analysis of LCAT with m6A Modification
The most common internal mRNA modification in eukaryotes is m6A, which has become a crucial regulator of gene expression and affects cellular functions like apoptosis, invasion, differentiation, and self-renewal [27,28]. There are three types of m6A regula- tory factors: writers, erasers, and readers. While erasers, such as demethylases (like FTO and ALKBH5), remove the modification, writers, such as the methyltransferase complex (MTC), catalyze m6A methylation [29]. Reader proteins recognize m6A and determine the fate of target RNAs, playing an essential role in RNA metabolism. The interplay among these modifiers is associated with the onset and progression of cancer [30]. We analyzed the co-expression of LCAT and different m6A modification regulators and found that in GBM, LUAD, LUSC, OV, and UVM, LCAT expression is significantly positively correlated with the expression of m6A modification regulators; in BLCA, BRCA, LIHC, PCPG, PRAD, and UCSC, LCAT and m6A modification regulators are significantly neg- atively correlated (Figure 8A). The expression of LCAT is significantly increased when multiple m6A readers (IGF2BP3, HNRNPC, RBMX, YTHDC1, YTHDC2, YTHDF3, ZC3H13) and m6A writers (RBM15) are mutated. Similarly, when m6A readers (IGF2BP1) or m6A writers (METTL13) are mutated, LCAT is significantly highly expressed in GBM and KIRC (Figure 8B). Conversely, in HNSC, LIHC, LUAD, OV, PAAD, and PRAD, the expression of LCAT is significantly reduced when certain m6A regulators are mutated (Figure 8C). We also predicted m6A modification sites in the LCAT mRNA sequence using the SRAMP web tool. Figure 8D and Table 1 show the m6A modification sites on the LCAT gene sequence: sites 1724 (TGGGACCCTGGGATGTTTGGGGACTTTACTATCTAGCACCCCAGT), 2847 (GACCTATCTGTTCCCACCTTGGACTTTGGCAATAAAGGAGCGCCA), and 2871 (TTTG- GCAATAAAGGAGCGCCAGACTGGG) have the highest m6A modification scores. The results suggest that m6A regulators can affect tumor progression by regulating the expres- sion of LCAT.
| Position | Sequence Context | Score |
|---|---|---|
| 1072 | CGCAGATGCTGCGGCAGATGAGA CTGACCAAGACTGAGCGGGAGC | 0.704 |
| 1212 | ATCCAGATGACGTGGACCAGGG ACAAGTACATGACTGAGACCTGG | 0.603 |
| 1223 | GTGGACCAGGGACAAGTACATG ACTGAGACCTGGGACCCCAGCCA | 0.582 |
| 1724 | TGGGACCCTGGGATGTTTGGGG ACTTTACTATCTAGCACCCCAGT | 0.903 |
| 1991 | GAGACAGCTGAGCTGAGGCCTG ACTTTTTCAATAAAACATTGTGT | 0.584 |
| 2205 | CCCACTCCCACACCAGATAAGG ACAGCCCAGTGCCGCTTTCTCTG | 0.579 |
| Position | Sequence Context | Score |
|---|---|---|
| 2593 | TCCCTTCTCCCACCACACTGTGA CTCTCAGTTGTCTAACCCAGGG | 0.559 |
| 2694 | TGGTCAGTCACAGCCACACCAGA CTCTGGGCCAAGCCCCACCACT | 0.61 |
| 2743 | CCTTGGCCCCCACCCACCAAGGA CAAGATGCCCAGCCCAGGATCG | 0.641 |
| 2847 | GACCTATCTGTTCCCACCTTGGA CTTTGGCAATAAAGGAGCGCCA | 0.76 |
| 2871 | TTTGGCAATAAAGGAGCG CCAGACTGGG | 0.563 |
A
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METTLIS
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BRCA-LowB In#219)
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2
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.
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WT IGP2BP3
Mutabud (GP26,P3
WT HMRNPC
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MILIMED POMP5
WT REMIX
MuMEG MEND
FRICA (196)
M
X
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UCEC
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PCPO Miten)
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S
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Mutated KGF 28.09
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Noutsted METERS
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PROGON, j- OGP
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PHiyyy,p = 000
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25
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Mitsted METTE12
WT YTMOOR
Mutated Y7HDC2
WT ROMY50
Mutsted ABAY50
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Mutytnd.F70
WT POMPS
MUMCU POMP5
WT JOYZEPP
MUIMED JOYZEPP
PRAD
D
Prediction Score Distribution along the Query Sequence
s
9
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2847
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287
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7
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3
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1000
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Position
2.6. LCAT Expression and Immune Correlation Analysis
As shown in Figure 9A, compared to the LCAT low expression group, the LCAT high expression group has lower immune cell enrichment scores in DLBC, KIRC, KIRP, TGCT, THCA, THYM, UCEC, MESO, and OV tumor samples. Conversely, in PRAD, the LCAT high expression group has higher immune cell enrichment scores. In the remaining tumors, there is no difference in immune cell enrichment scores between the LCAT low expression group and the high expression group (Figure S7). The distribution of multiple immune cell scores in the LCAT high and low expression groups in DLBC, KIRC, KIRP, TGCT, THCA, THYM, UCEC, MESO, OV, and PRAD is shown in Figure 9B.
A
DLBC
KIRC
KIRP
PRAD
TGCT
0.4
0,6-
E
Enrichment score of aDC
Enrichment score of aDC
0.3
0.6
T
Enrichment score of aDC
0,3
1
Enrichment score of aDC
0.4
I
Enrichment score of aDC
0,5-
T
0.3
0.4
0,4
0.2
0.2
0,3
0.2 -
0.1 -
0.1
0.2-
0.2
0.1
0,0
Low
High
0,0
Low
High
0,0
LCAT
LCAT
Low
LCAT
High
0,0
0,0
Low
High
LCAT
High
Low
LCAT
THCA
THYM
UCEC
MESO
OV
0.5
.
0,5
0,5
.
Enrichment score of aDC
0,5
=
0.4
Enrichment store of aDC
Enrichment store of aDC
Enrichment score of aDC
0.6
T
Enrichment score of aDC
T
0,4
0.4
0.3
0.4
0.3
0.4
0.3
0.2
0.2
0.2-
0.2
0.2
0.1
0.1
0,1
0.0
0.0
0.0
Low
LCAT
High
Low
LCAT
High
Low
LCAT
High
0.0
0.0
Low
LCAT
High
Low
LCAT
High
B
DLBC
KIRC
KIRP
Low
High
LOW
High
High
100
75
75
75
Proportion (7%)
Proportion (%)
Proportion (%)
SO
1
25
25
25
0
0
4
group
-
PRAD
TGCT
THCA
High
LOM
High
100
100
75
75
Proportion (9%)
Proportion [%]
Proportion (4%)
0
go
25
3
2
0
LII
D
a
-
-
-
THYM
UCEC
MESO
Low
Low
High
VỚIH
100
100
4
76
76
75 -
Proportion (%)
Proportion (%)
Proportion (%|
a
10
1
25
25
25
0
Det CD4 memory renting
0
a
-
-
-
OV
Low
High
100
75
Proportion [9%)
NO
25
0
-
Immune checkpoint molecules play a key role in regulating the activity of the immune system, especially in preventing excessive autoimmune responses [31]. However, in the tumor microenvironment, the abnormal expression of these molecules may suppress anti- tumor immune responses, thereby promoting tumor growth and development [32,33]. We analyzed the correlation between LCAT expression and immune checkpoints in multiple tumors using the TIMER 2.0 database. The results showed that in ACC, LCAT expression is significantly negatively correlated with multiple immune checkpoint molecules; in CESC, COAD, ESCA, PRAD, READ, and STAD, LCAT expression is significantly positively corre- lated with multiple immune checkpoint molecules (Figure 10A). The MHC, also known
as the human leukocyte antigen (HLA) system, is responsible for presenting antigens to T lymphocytes, initiating a specific immune response [34,35]. Some tumor cells can evade recognition and attack by the immune system by downregulating MHC expression or altering its structure [36-38]. The heatmap in Figure 10B shows the correlation between LCAT expression and MHC molecules. In BRCA, COAD, LUAD, and PRAD, LCAT is significantly positively correlated with almost all MHC molecules. In THCA, LCAT is significantly negatively correlated with almost all immune stimulatory factors. Further- more, we analyzed the relationship between LCAT expression and immune suppressors and determined that LCAT expression is positively correlated with the expression of immune suppressors in various tumors. LCAT showed a substantial and positive correlation with the expression of nearly all immunosuppressive factors in BRCA, COAD, ESCA, LUAD, LUSC, PRAD, READ, and STAD (Figure 10C).
A
CD274
CTLA4
* p < 0.05
HAVCR2
Cor
LAG3
1.0
0.5
PDCD1
0.0
PDCD1LG2
-0.5
SIGLEC15
-1.0
TIGIT
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KRC
KIRP
LGG
LHC
LUAD
LUSC
MESO
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCSC
UCS
UVM
B
D
82M
C10ORF54
HLA-A
HLA-B
CD27
HLA-C
CD276
HLA-DMA
CD28
CD40
HLA-DMB
HLA-DOA
CD40LG
HLA-DOB
p = 0.05
CD48
HLA-DPA1
Cor
CD70
HLA-DPB1
1.0
CDOO
HLA-DOA1
0.5
CD88
HLA-DOA2
0.0
CXCL 12
HLA-DQB1
CXCR4
HLA-DRA
-0.5
ENTPD1
HLA-DRB1
-1.0
HHLA2
HLA-E
ICOS
HLA-F
ICOSLG
*
HLA-G
IL2RA
TAP1
IL6
TAP2
LER
TAPBP
KLRC1
p < 0.05
ACC
BLCA
BPCA
CESC
CHOL
COA
DLBC
ESCA
GBM
HNSC
KCM
KRC
NRA
LUAD
LUSC
PAAD
POPC
PPAD
REAL
SARC
SKCH
STAD
THỜI
THYM
UCSC
UCS
KLRK1
Cor
V
LTA
1.0
MICB
0.5
C
NTSE
0.0
ADORA2A
PVR
BTLA
RAETIE
-0.5
TMEM173
-1.0
CD160
CD244
TMIGD2
CD274
TNFRSF13B
CD95
CUBO
TNFRSF13C
CTLA4
TNFRSF 14
HAVCR2
p = 0.05
TNFRSF 17
DO1
TNFRSF 18
IL10
Cor
ILIORD
1.0
TNFRSF25
KDR
0.5
TNPRGr4
KIR2DL1
0.0
TNFRSF8
KIRZDL3
-0.5
TNFRSF9
LAG3
TNF5F13
GALS9
-1.0
TNFSF13B
POCD1
TNFSF14
PDCDILG2
TNFSF15
PVRL2
TGFB1
TNFSF18
TGFBR1
TNFSF4
TIGIT
TNFSF9
VTCN1
ULBP1
0
3
0
n
W
A
GECA
GRA
MIRC
400
UAD
READ
SKCM
O
4
{
W
TH
UVA
0
d
UCSC
Immune stimulatory factors are a class of substances that can activate and enhance the body’s immune response. These factors include cytokines, chemokines, and other signaling molecules. They promote the activation and function of immune cells by binding to specific receptors, thereby improving the body’s ability to recognize and clear tumor cells [39]. As shown in Figure 10D, in BRCA, COAD, ESCA, KIRC, LUAD, LUSC, PRAD, READ, SKCM, STAD, and UCSC, LCAT expression is positively correlated with almost all immune stimulatory factors.
2.7. TMB and MSI Analysis Related to LCAT
The quantity of somatic mutations present in tumor cells is known as the tumor mutation burden (TMB), and it is typically represented as the number of mutations per megabase [40]. TMB, as a biomarker, has potential value in predicting the response of certain cancer patients to immunotherapy [41,42]. In KICH, HNSC, ESCA, LAML, LIHC, UVM, LUAD, STAD, THCA, BRCA, PCPG, CHOL, LGG, PRAD, and uterine carcinosar- coma (UCS), LCAT expression is negatively correlated with TMB. In ACC, READ, GBM, and UCEC, LCAT expression and TMB are positively correlated (Figure 11A).
A
B
ACC
LUSC
READ
LUAD
GBM
KICH
UCEC
HNSC
MESO
BRCA
KIRP
THCA
LUSC
DLBC
OV
OV
SKCM
BLCA
COAD
SKCM
DLBC
CHOL
PAAD
CESC
-log10(p-value)
GBM
CESC
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10.0
KIRC
7.5
UVM
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THYM
5.0
PRAD
0.3
BLCA
2.5
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SARC
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-log10(p-value)
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Correlation
0.1
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ESCA
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5.0
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2.5
LAML
UCEC
LIHC
MESO
UVM
TGCT
LUAD
ESCA
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PCPG
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BRCA
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PCPG
READ
CHOL
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ACC
PRAD
LIHC
UCS
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0.0
Correlation(TMB)
0.2
-0.1
0.0
Correlation(MSI)
0.1
0.2
0.3
Microsatellite instability (MSI) is a form of genomic instability that typically occurs in cells with defective DNA repair mechanisms [43]. In UCS, READ, PAAD, ACC, LIHC, and THYM, LCAT expression and MSI are negatively correlated. In LUSC, LUAD, KICH, HNSC, BRCA, THCA, DLBC, OV, BLCA, SKCM, CHOL, GBM, CESC, and UVM, LCAT expression and MSI are positively correlated (Figure 11B).
2.8. Correlation Analysis of LCAT Expression and Drug Sensitivity
We used the “GDSC” and “CTRP” modules of the GSCA online tool to analyze the correlation between LCAT expression and the IC50 of various anti-cancer drugs. As shown in the bubble chart in Figure 12A, in GDSC, the IC50 of almost all anti-cancer drugs is significantly positively correlated with LCAT mRNA expression. Among them, the positive correlation between LCAT expression and the IC50 of BRD-K01737880 is the strongest. The negative correlation between LCAT expression and the IC50 of BRD-staurosporine is the strongest. In the CTRP database, LCAT mRNA expression is significantly correlated with the IC50 of various anti-cancer drugs. The positive correlation between LCAT expression and the IC50 of NPK76-II-72-1 is the strongest. The negative correlation between LCAT expression and the IC50 of CGP-60474 is the strongest (Figure 12B).
A
Correlation between CTRP drug sensitivity and mRNA expression
B
Correlation between GDSC drug sensitivity and mRNA expression
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2.9. Molecular Mechanisms by Which LCAT Affects Tumor Progression
Based on the results of Figures 3 and 4, we can conclude that LCAT expression sig- nificantly affects the progression of ACC, COAD, LGG, and LIHC tumors. To further understand how LCAT promotes the progression of ACC and COAD tumors and how it inhibits the progression of LGG and LIHC tumors, we divided ACC, COAD, LGG, and LIHC into two groups based on LCAT expression and performed functional enrichment analysis on the differential genes between the two groups.
The GO enrichment analysis shows that in ACC, these differential genes are mainly in- volved in DNA replication, fibrillar collagen trimer, and CXCR chemokine receptor binding (Figure S8A-C). In COAD, these differential genes are mainly involved in humoral immune response mediated by circulating immunoglobulin, immunoglobulin complex, and antigen binding (Figure S8D-F). In addition, the signaling pathways involved via these differential genes in ACC and COAD are consistent. In LGG, the differential genes between the high and low LCAT expression groups are mainly involved in humoral immune response me- diated by circulating immunoglobulin, immunoglobulin complex, and immunoglobulin receptor binding (Figure S8G,I). In LIHC, the differential genes between the high and low LCAT expression groups are mainly involved in the carboxylic acid catabolic process, HDL particles, and oxidoreductase activity (Figure S8J-L).
The KEGG enrichment analysis results indicate that LCAT is most likely to promote ACC tumor progression through the IL-17 signaling pathway (Figure 13A); LCAT is most likely to promote COAD tumor progression through complement and coagulation cascades (Figure 13B). In addition, in ACC and COAD tumors, LCAT is likely to promote tumor progression through cytokine-cytokine receptor interaction (Figure 13A,B). LCAT is most likely to inhibit LGG tumor progression by affecting focal adhesion formation (Figure 13C); LCAT is most likely to inhibit LIHC tumor progression through glycine, serine, and threo- nine metabolism (Figure 13D). In LGG and LIHC tumors, LCAT is likely to inhibit tumor progression through complement and coagulation cascades (Figure 13C,D).
The Reactome enrichment analysis results show that LCAT is most likely to promote ACC tumor progression through collagen degradation (Figure 13E); LCAT is most likely to promote COAD tumor progression by affecting cornified envelope formation (Figure 13F). In addition, in ACC and COAD tumors, LCAT is likely to promote tumor progression by regulating cell cycle checkpoints (Figure 13E,F). LCAT is most likely to inhibit LGG tumor progression by affecting peptide chain elongation (Figure 13G); LCAT is most likely to inhibit LIHC tumor progression through complement cascades (Figure 13H).
A
TCGA-ACC (GSEA): LCAT-High vs LCAT-Low
E
TCGA-ACC (GSEA): LCAT-High vs LCAT-Low
IL-17 signaling pathway
Collagen degradation
TNF signaling pathway
Cell Cycle Checkpoints
Rheumatoid arthritis
DNA Replication
p53 signaling pathway
KEGG (TOP10)
p.adjust
Reactome (TOP10)
Mitotic G1 phase and G1/S transition
pacurit
Legonerous
0.002
Synthesis of DNA-
0.001
0.004
0.002
Coll cycle
0.006
Cell Cycle, Mitotic
0.003
0.000
Cytokine-cytokine receptor Interaction
G1/S Transition
Platelet activation
Cell Cycle
Bilo secretion
Mitotic Prometaphase
Neutrophil extracellular trap formation
Formation of Fibrin Clot (Clotting Cascade)
-10
-5
0
Enrichment distribution
5
-10
-5
Enrichment distribution
0
5
B
TCGA-COAD (GSEA): LCAT-High vs LCAT-Low
F
TCGA-COAD (GSEA): LCAT-High vs LCAT-Low
Formation of the comifed envelope
Complement and coagulation cascades
Keratinization
Neuroactive Egand-receptor interaction
Extracellular matrbc organization
ECM-receptor Interaction
Class AM (Rhodopsin-like receptors)
Diated cardiomyopathy
KEGG (TOP10)
pacjust
Reactome (TOP10)
p.adjust
GPCR Igand binding
2.0×10€
Calcium signaling pathway
1×10+
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Developmental Biology
3.5×10
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Spliceosome
Mitotic Anaphase
HExisome Biogenicas in eukaryotes
Cell Cycle Checkpoints
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Translation
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2
4
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0
2
Enrichment distribution
Enrichment distribution
4
6
C
TCGA-LGG (GSEA): LCAT-High vs LCAT-Low
G
TCGA-LGG (GSEA): LCAT-High vs LCAT-Low
Focal adhesion
Peptide chain elongation
Cytokine-cytokine receptor Interaction
Selonocysteine Synthesis
Calcium signaling pathway
Viral mRNA Translation
KEGG (TOP10)
Proteoglycans In cancer
p.adjust
Reactome (TOP10)
Formation of a pool of froo 408 subunits
p.adjust
0.0025
Viral protein interaction with cytokine
0.0050
1.13k-mecated translational silencing of Coruloplasmin expression
and cytokine receptor
0.0075
9.214209×100
GABAergio synapse
0.0100
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Neuroactive ligand-receptor interaction
GTP hydrolysis and joining of the 605
rihnenmal mitunk
Complement and coagulation cascades
Signaling by GPCR
Protoin digestion and absorption
GPCR downstream signaling
ECM-receptor interaction
GPCR: ligand binding
-
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Enrichment distribution
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Enrichment distribution
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TCGA-LIHC (GSEA): LCAT-High vs LCAT-Low
H
TCGA-LIHC (GSEA): LCAT-High vs LCAT-Low
Complement cascade
Glycine, serino and threonine metabolism
Valine leucine and isoleucine degradation
Peroxisomal protein Import
Peroxisome
Regulation of Complement cascade
Głyoxylate metabolism and glycine
Faty acid dogradation
p.adjust
Reactome (TOP10)
dogradation
KEGG (TOP10)
pacjust
0.00025
Mitochondrial Fatty Acid Beta-Oxidation
0.001
Arginine biosynthesis
0.00050
0.002
Glyoxylato and dicarboxylate metabolism
0.00075
Respiratory electron transport
0.003
0.004
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0.005
Primary bile acid biosynthesis
Protoin localization
Respiratory electron transport, ATP
Propanoate metabolism
Synthesis by chemicomotic coupling, and
heat production by uncoupling proteins.
Complement and congelation cascades
Phase I - Functionalization of compounds
Pancreatic secretion
Biological oxidations
-10.0
-7.5
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-5.0
-2.5
0.0
25
0.0
0.5
1.0
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3. Discussion
Elevated cholesterol levels are considered a prerequisite for cancer cell proliferation and tumor progression [44]. Mitochondrial cholesterol levels can induce resistance to apoptotic signals, and cholesterol also regulates the physicochemical properties of the cell membrane, including lipid rafts and signaling receptors such as the Epidermal Growth Factor Receptor [2]. The role of LCAT in the maturation of HDL and the conversion of free cholesterol into cholesterol ester may affect the cholesterol content and homeostasis
in cancer cells [45]. Therefore, there is a growing recognition of the multifaceted roles of metabolic enzymes such as LCAT in tumorigenesis and immune regulation. Our compre- hensive analysis of the role of LCAT in various cancers reveals its potential as a biomarker and therapeutic target, emphasizing the necessity of understanding its mechanisms of action.
The role of LCAT in cancer biology is complex and context-dependent, reflecting its dual nature as both a tumor suppressor and a potential promoter of tumor progression. This study provides a comprehensive analysis of the expression, epigenetic regulation, immune interactions, and therapeutic implications of LCAT across multiple cancer types. Our findings highlight the importance of understanding the multifaceted roles of LCAT in tumor biology, particularly its involvement in lipid metabolism, immune modulation, and epigenetic regulation. The dual nature of LCAT is evident in its contrasting roles across dif- ferent cancer types. In low-grade glioma (LGG) and liver hepatocellular carcinoma (LIHC), low LCAT expression is associated with poor prognosis, suggesting a tumor-suppressive function. This aligns with previous studies showing that LCAT inhibits tumor progression by modulating cholesterol metabolism and enhancing HDL functionality, which may sup- press tumor growth and immune evasion [4,5]. Conversely, in adrenocortical carcinoma (ACC) and colon adenocarcinoma (COAD), high LCAT expression correlates with worse outcomes, indicating a potential oncogenic role. This duality underscores the importance of context-specific mechanisms, where LCAT may either promote or inhibit tumor progres- sion depending on the tumor microenvironment and genetic background. For instance, in LGG and LIHC, LCAT likely exerts its tumor-suppressive effects by regulating lipid metabolism and immune responses. Our functional enrichment analysis revealed that LCAT inhibits tumor progression in these cancers through pathways such as complement and coagulation cascades and oxidoreductase activity, which are critical for maintaining cellular homeostasis and suppressing tumorigenesis. In contrast, in ACC and COAD, LCAT may promote tumor progression by enhancing DNA replication and cytokine- cytokine receptor interactions, which are essential for tumor cell proliferation and survival. These findings suggest that the role of LCAT in cancer is not uniform but rather depends on the specific molecular and cellular context of each tumor type.
The expression of LCAT in tumor cell lines and the website-based predictions of its subcellular localization both indicate significant nuclear expression of LCAT. Previous studies have also shown that gene expression in the nucleus is closely related to tumor progression. The aberrant expression of genes in the nucleus can lead to the overproduc- tion of key oncoproteins or the loss of tumor suppressor proteins, thereby affecting cell cycle control, DNA repair, apoptosis, and other processes, promoting the occurrence and development of tumors [46].
In terms of genomic alterations, our investigation into the relationship between CNVs and SNVs with LCAT expression reveals more layers of genetic complexity affecting cancer progression. In various tumors, LCATs CNV and mRNA expression show a positive correlation, impacting the survival of patients with multiple tumors, especially KIRP and USEC. The correlation between CNVs and LCAT expression underscores the necessity of understanding how genomic alterations lead to the dysregulation of metabolic enzymes in the tumor environment. Notably, there is a lack of significant association between SNV mutations in LCAT and survival outcomes. Our study results suggest that LCAT expression is influenced by DNA methylation patterns. In various cancer types, hypermethylation is associated with reduced expression, and LCAT methylation levels significantly impact tumor progression in LGG, LIHC, SARC, and UVM. These insights suggest that future research should investigate whether demethylating drugs can enhance LCAT expression, potentially restoring its tumor-suppressing function.
N6-methyladenosine (m6A) modification affects the progression of various cancers by regulating the expression of tumor-associated genes [27,47-50]. For instance, in bladder cancer, the upregulation of METTL3 enhances the methylation of CDCP1 mRNA, promoting its translation and tumor progression [51]. In colorectal cancer, METTL3 promotes the stability of SOX2 mRNA by catalyzing its m6A modification, thereby promoting tumor development [52]. The dual role of m6A in cancer further highlights its ability to promote or inhibit tumorigenesis, depending on the context. The interaction between LCAT and m6A regulatory factors provides a new avenue for understanding post-transcriptional regulatory mechanisms in cancer. Identifying m6A modification sites on LCAT mRNA may help develop targeted therapies that alter RNA structure, thereby enhancing the anti-tumor effects of existing therapies. Our analysis indicates that in GBM, LUAD, LUSC, OV, and UVM, LCAT expression is significantly positively correlated with the expression of m6A modification factors; in BLCA, BRCA, LIHC, PCPG, PRAD, and UVM, LCAT expression is significantly negatively correlated with the expression of m6A modification factors. LCAT expression is significantly elevated when multiple m6A readers and writers are mutated. The distribution of LCAT expression is related to m6A modification sites.
Immune cells in the tumor microenvironment can regulate the behavior of tumor cells by secreting cytokines and metabolic products, including promoting tumor angiogenesis, invasion, and metastasis [53]. An in-depth study of these complex interactions is crucial for developing new immunotherapeutic strategies and predicting tumor treatment responses. Our immunological analysis suggests that high LCAT expression is associated with a reduction in immune cell infiltration in several cancers, indicating that LCAT may contribute to immune evasion mechanisms. As depicted in Figure 10, there is a significant correlation between LCAT expression and various immune regulatory factors. Furthermore, we analyzed the correlation between LCAT expression and TMB as well as MSI in different tumors and found that LCAT expression is significantly associated with the levels of TMB and MSI across various cancers. These correlations are crucial, suggesting that LCAT may influence anti-tumor immunity by modulating immune regulatory factors. Therefore, targeting LCAT could potentially alter lipid metabolism and enhance the efficacy of immune checkpoint blockade in drug-resistant tumors.
Our analysis of the correlation of LCAT with drug sensitivity revealed its potential as a predictive biomarker for chemotherapy response. In the GDSC and CTRP databases, LCAT expression was significantly correlated with the IC50 of various chemotherapeu- tic drugs, suggesting that LCAT may influence drug resistance or sensitivity in cancer cells. For example, high LCAT expression was associated with increased resistance to BRD-K01737880 and NPK76-II-72-1, while low LCAT expression correlated with sensitivity to BRD-staurosporine and CGP-60474. These findings suggest that LCAT could be used to stratify patients for personalized therapy, particularly in cancers where it modulates drug sensitivity.
In ACC and COAD, LCAT expression levels significantly affect tumor progression. The GO enrichment analysis of the LCAT high and low expression groups in ACC and COAD suggests that LCAT may promote tumor progression in ACC and COAD by af- fecting DNA replication and immune responses. The KEGG enrichment analysis further supports this view, showing that LCAT may promote ACC tumor progression through the IL-17 signaling pathway and promote COAD tumor progression through complement and coagulation cascades. Additionally, the interaction between cytokines and cytokine receptors may be regulated by LCAT in both cancers, explaining the common mechanism of action of LCAT in different cancers. Unlike ACC and COAD, LCAT may play a role in inhibiting tumor progression in LGG and LIHC. The GO enrichment analysis shows that in LGG, LCAT-related differential genes are mainly involved in humoral immune responses
and immunoglobulin receptor binding, whereas in LIHC, they involve carboxylic acid metabolism, HDL particles, and oxidoreductase activity. These results suggest that LCAT may inhibit the progression of these two cancers by regulating immune responses and metabolic processes. Complement and coagulation cascades may be regulated by LCAT in LGG and LIHC, explaining the common mechanism of action of LCAT in inhibiting tumor progression. The Reactome enrichment analysis further emphasizes the role of LCAT in regulating peptide chain elongation and complement cascades, which may be key molecular mechanisms for its inhibitory effect on LGG and LIHC tumor progression.
Although our study provides valuable insights into the role of LCAT in cancer, the mechanism by which LCAT has a dual nature in different cancer types requires further investigation. Future studies should also explore the interactions between LCAT and other metabolic enzymes in the tumor microenvironment, as well as the role of LCAT in regulating immune cell function and immunotherapy response.
4. Materials and Methods
4.1. LCAT Expression Profile Data Analysis
The Human Protein Atlas (HPA) website furnished us with records on LCAT ex- pression in healthful tissues. The UALCAN (https://ualcan.path.uab.edu/) database provided us with records on LCAT expression in tumor tissues (accessed on 11 October 2024). From the TCGA database, we extracted medical records and raw RNAseq data from tumor and normal tissues, which were obtained from the Xiantao Academic website (https://www.xiantaozi.com/) (accessed on 5 October 2024). By changing counts to Tran- scripts Per Million (TPM) and the usage of log2 (TPM+1) transformation, the uncooked records were normalized. We examined the variations in LCAT expression between the tumor and adjoining regular tissues. We examined the expression of LCAT in quite a number of pathological ranges of tumors for sufferers with medical records in order to reap a higher appreciation of LCAT expression in tumors. The expression level of LCAT in tumors was divided into high and low expression groups based on the median value of LCAT expression as the cut-off point. The Wilcoxon signed-rank check was used to consider the statistical differences.
4.2. Human Protein Atlas (HPA)
HPA provided the expression data for LCAT in tumor cell lines and tumor tissues (https://www.proteinatlas.org) (accessed on 18 October 2024) [54]. LCAT subcellular localization information was found using the”CELL ATLAS” module of HPA.
4.3. Survival Analysis
Survival evaluation was performed using tumor-affected person RNA-seq records from the TCGA database, obtained via XianTao Academic (retaining samples with scientific information). Patients with a range of tumor types were analyzed for OS, DSS, and PFI by using univariate Cox regression analysis. The R software program (3.6.3) was used to analyze the data. The R package deal “survival (3.2-10)” was used once for the statistical evaluation of the survival data, and the R bundle “survminer (0.4.9)” was used for visualization.
4.4. CNV Mutation Analysis
We input “LCAT” into the search template of the Gene Set Cancer Analysis (GSCA) (https://guolab.wchscu.cn/GSCA/ (accessed on 25 October 2024)) [55] online tool. All tumor types were selected, and the CNV summary, CNV & Expression, and CNV & Survival modules were checked before initiating the search. The GSCA online tool downloaded
CNV data from 11,495 samples and processed them using GISTIC2.0. Pie charts illustrate the percentage distribution of different types of CNV mutations in 33 different tumor types (total copy number gain, total copy number loss, heterozygous gain, heterozygous loss, homozygous gain, and homozygous loss). The percentage of heterozygous gain is shown in red, the percentage of heterozygous loss in brown, the percentage of homozygous gain in light green, the percentage of homozygous loss in dark green, and the percentage of no gene CNV mutation in gray. The Spearman correlation between LCAT CNV and mRNA expression in 33 tumors is shown in scatter plots with FDR-adjusted p-values. The survival times and statuses within the wild-type (WT), copy number gain, and copy number loss groups of the samples were modeled using the R software package “survival.” To evaluate variations in the groups’ survival rates, logrank tests were used, as generated and analyzed by the GSCA website. The OS, PFS, DSS, and PFI survival characteristics of LCAT CNV in KIRP and UCEC are shown using survival curves.
4.5. SNV Mutation Analysis
SNV information from 10,234 samples for 33 different cancer types was gathered from the TCGA database via the GSCA web tool. Missense mutation, nonsense mutation, frame shift insertion, splice site mutation, frame shift deletion, in-frame deletion, and in-frame insertion are the seven types of mutations that we examined. The amount of detrimental LCAT variants in the chosen cancer types is known as the variant classification. Variant type: the quantity of SNPs and DELs found in the chosen cancer kinds’ LCAT concentrations. SNV categories: the number of each SNV category found in the chosen cancer types that are concentrated in LCAT. The survival differences between wild-type and mutant LCAT in 33 cancer types were summarized using bubble charts.
4.6. MMR Mutation Analysis
Spearman correlation data between LCAT expression and five MMR genes were assessed using TIMER 2.0 (http://compbio.cn/timer2/) (accessed on 21 October 2024) [56], with the results visualized using R package “ggplot2 (version 3.3.3)”. http://timer.cistrome.org/.
4.7. Methylation Analysis
The TCGA database was used to retrieve the Illumina methylation and mRNA ex- pression data used in the GSCA online tool. Various methylation levels resulted from the acquisition of multiple methylation sites within a gene region. The relationship between gene mRNA expression and methylation levels was examined using Spearman correlation analysis. Clinical data on tumor samples from 33 extraordinary cancer types was acquired once by way of GSCA from TCGA and posted research. Clinical survivl and methylation data were combined based on sample size. The tumor samples were divided into excessive and low methylation groups based totally on the median methylation level. The “survival” R software program package deal was used once to suit the survival instances and statuses of the two groups, as generated and analyzed by the GSCA website. A Cox proportional- hazards mannequin was used to decide the hazard ratio between the excessive and low methylation groups, and logrank was employed to see whether or not the editions in survival quotes between the two agencies were statistically significant. Spearman correla- tion between LCAT mRNA expression and methyltransferase genes (DNMT1, DNMT3A, DNMT3B, and DNMT3L) in 33 tumors was retrieved from the TIMER 2.0 database.
4.8. M6A Modification Analysis
Spearman correlation analysis between LCAT mRNA expression levels and 19 m6A regulatory factors in different cancers was conducted using the “Exploration-Gene_Corr” module in TIMER 2.0. The “Prediction” module of the sequence-based RNA adenosine
methylation site predictor (SRAMP) web tool (http://www.cuilab.cn/sramp/ (accessed on 23 October 2024)) [57] was used to predict the m6A modification sites in LCAT. The specific operation is as follows: (1) Input the FASTA LCAT mRNA sequence in Mature mRNA mode; (2) analyze RNA secondary structure-NO; (3) tissue selection is universal; (4) show query sequence as RNA; (5) finally, click “submit”.
4.9. Immune-Related Analysis
Data on LCAT expression and immune cell expression in different tumor types were obtained from the TCGA database through XianTao Academic. The immune cell expres- sion data included the infiltration levels of various immune cell types, such as naive B cells, memory B cells, plasma cells, CD8+ T cells, naive CD4+ T cells, memory rest- ing CD4+ T cells, memory-activated CD4+ T cells, follicular helper T cells, regulatory T cells (Tregs), gamma delta T cells, resting NK cells, activated NK cells, monocytes, M0 macrophages, M1 macrophages, M2 macrophages, resting dendritic cells, activated dendritic cells, resting mast cells, activated mast cells, eosinophils, and neutrophils. The ssGSEA algorithm provided by the R package GSVA [version 1.46.0] was used to calculate the immune infiltration levels corresponding to these immune cell types based on the transcriptomic data. The ggplot2 package deal was used once to visualize the statistics; the appropriate statistical techniques were utilized for statistical evaluation (statistics bundle and auto package). Spearman correlation data between LCAT expression and immune- related genes (immune checkpoints, immune stimulatory factors, immune inhibitors, and major histocompatibility complex (MHC) molecules) were downloaded from the TIMER 2.0 online website. The results were visualized using the R software package “ggplot2 (version 3.3.3).” Finally, correlation data between LCAT mRNA expression and MSI and TMB expression were obtained from the ASSISTANT for Clinical Bioinformatics website (https://www.aclbi.com/static/index.html (accessed on 28 October 2024), with correlation analysis performed using Spearman analysis.
4.10. Drug Sensitivity Analysis
We used the online tool GSCA to analyze the correlation between LCAT mRNA expres- sion and drug sensitivity in multiple tumors. The GSCA online tool collected the IC50 of various small molecule drugs in cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) and Genomics of Therapeutics Response Portal (CTRP) databases, along with corresponding mRNA gene expression. The mRNA expression facts and drug sensitivity information were merged using the GSCA online tool. The correlation between gene mRNA expression and drug IC50 was analyzed once using Pearson correlation. The p-values were adjusted via FDR. We examined the relationship between LCAT mRNA expression and treatment sensitivity in a number of malignancies using the web application GSCA. The GDSC and CTRP databases provided the GSCA online tool with the IC50 of several small molecule medicines in the cell lines, as well as the related mRNA gene expression. The GSCA online tool combined the data on medication sensitivity and mRNA expression. Pearson correlation was used to examine the relationship between medication IC50 and gene mRNA expression. FDR was used to alter the p-values.
4.11. Functional Enrichment Analysis
Comprehensive Analysis on Multi-Omics of Immunotherapy in Pan-cancer (CAMOIP) (https://www.camoip.net/) (accessed on 3 November 2024) [58] is a comprehensive analy- sis tool that is specifically designed for processing and analyzing expression and mutation data in TCGA and immune checkpoint inhibitor treatment projects. This study used the “Pathway Enrichment Analysis” module of CAMOIP; we clicked on “GSEA,” selected
“TCGA-Cohort” in Step 1, and subsequently input the tumor and gene of interest. In Step 2, KEGG, GO-BP, GO-CC, and GO-MF were chosen for analysis.
5. Conclusions
This study unveils the multifaceted roles of LCAT in cancer, particularly its key in- volvement in tumor immune modulation and progression. The expression levels of LCAT are closely associated with the prognosis of patients across various cancers, potentially serving as a biomarker for predicting treatment response. The impact of genetic and epi- genetic variations on LCAT function offers new insights for cancer therapy. The role of LCAT in regulating immune responses in the tumor microenvironment and drug sensitivity underscores its potential in cancer treatment. These findings provide a scientific basis for developing personalized therapeutic strategies targeting LCAT, highlighting its significant role in oncology.
Supplementary Materials: Supplementary material-figure can be downloaded at https://pan.quark. cn/s/4ce2fde984c8 (accessed on 3 February 2025). supplementary material-table1 can be downloaded at https://pan.quark.cn/s/018517336c51 (accessed on 3 February 2025).
Author Contributions: Conceptualization, M.G .; methodology, M.G .; software, M.G .; validation, M.G., W.Z. and S.L .; formal analysis, M.G .; investigation, M.G .; resources, P.H. and W.W .; data curation, M.G., W.Z. and X.L .; writing-original draft preparation, M.G .; writing-review and editing, M.G., P.H. and W.W .; visualization, M.G., X.L. and S.L .; supervision, M.G .; project administration, P.H. and W.W .; funding acquisition, P.H. and W.W. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Shaanxi Provincial Key Research and Development Program, grant number 2024SF-YBXM-6676.
Institutional Review Board Statement: Ethical review and approval were not required for this study, as all data were obtained from publicly available sources and did not involve human participants or animals.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Raw data1-2 can be downloaded at https://pan.quark.cn/s/55bb023 9bb45 (accessed on 3 February 2025). Raw data3-12 can be downloaded at https://pan.quark.cn/s/ 884b9fb0313f (accessed on 3 February 2025).
Conflicts of Interest: The authors declare no conflicts of interest.
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