Journal of Clinical Medicine
MDPI
Article
A Systematic Pan-Cancer Analysis of MEIS1 in Human Tumors as Prognostic Biomarker and Immunotherapy Target
Han Li 1,+, Ying Tang 2,+, Lichun Hua 2,4, Zemin Wang 1, Guoping Du 3, Shuai Wang 1, Shifeng Lu 4,* and Wei Li 5,*
1 Key Laboratory of Environmental Medicine Engineering, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China
2 Department of Ultrasound Diagnostic, Children’s Hospital of Nanjing Medical University, Nanjing 210008, China
3 Department of General Practice, Southeast University Hospital, Nanjing 210018, China
4 Department of Hematology and Oncology, Children’s Hospital of Nanjing Medical University, Nanjing 210008, China
5 Department of Clinical Research, Children’s Hospital of Nanjing Medical University, Nanjing 210008, China
* Correspondence: maclsf@163.com (S.L.); weili126@126.com (W.L.)
+ These authors contributed equally to this work.
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Citation: Li, H .; Tang, Y .; Hua, L .; Wang, Z .; Du, G .; Wang, S .; Lu, S .; Li, W. A Systematic Pan-Cancer Analysis of MEIS1 in Human Tumors as Prognostic Biomarker and Immunotherapy Target. J. Clin. Med. 2023, 12, 1646. https://doi.org/ 10.3390/jcm12041646
Academic Editor: Emanuela Anastasi
Received: 20 January 2023
Accepted: 13 February 2023 Published: 18 February 2023
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Copyright: @ 2023 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: Background: We intended to explore the potential immunological functions and prognostic value of Myeloid Ecotropic Viral Integration Site 1 (MEIS1) across 33 cancer types. Methods: The data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Gene expression omnibus (GEO) datasets. Bioinformatics was used to excavate the potential mechanisms of MEIS1 across different cancers. Results: MEIS1 was downregulated in most tumors, and it was linked to the immune infiltration level of cancer patients. MEIS1 expression was different in various immune subtypes including C2 (IFN-gamma dominant), C5 (immunologically quiet), C3 (inflammatory), C4 (lymphocyte depleted), C6 (TGF-b dominant) and C1 (wound healing) in various cancers. MEIS1 expression was correlated with Macrophages_M2, CD8+T cells, Macrophages_M1, Macrophages_M0 and neutrophils in many cancers. MEIS1 expression was negatively related to tumor mutational burden (TMB), microsatellite instability (MSI) and neoantigen (NEO) in several cancers. Low MEIS1 expression predicts poor overall survival (OS) in adrenocortical carcinoma (ACC), head and neck squamous cell carcinoma (HNSC), and kidney renal clear cell carcinoma (KIRC) patients, while high MEIS1 expression predicts poor OS in colon adenocarcinoma (COAD) and low grade glioma (LGG) patients. Conclusion: Our findings revealed that MEIS1 is likely to be a potential new target for immuno-oncology.
Keywords: MEIS1; pan-cancer analysis; tumor; biomarkers; immunotherapy; prognosis
1. Introduction
Cancer is a disease that seriously affects human health, and it is also the most con- cerning disease by global research [1]. Globally, 18.1 million cases of cancer were newly diagnosed in 2020, and this number is expected to be 28.4 million in 2040; with the burden growing in almost every country, the prevention and treatment of cancer are significant public health challenges [2]. Currently, cancer treatment approaches primarily include chemotherapy, radiotherapy, surgery, targeted therapy, stem cell transplants, and im- munotherapy [3,4]. Recently, immunotherapy has shown positive outcomes in various cancer types previously limited due to lack of known intervention strategies [5]. Im- munotherapy is a form of cancer therapy that helps human to fight cancer by activating the immune system, which is assuredly one of the greatest innovation in cancer treatment in the last decade [6]. Biomarkers are important for predicting the outcomes in response to immunotherapy, numerous candidate biomarkers have been used; however, responses are only typically present in a small number of patients [7]. Novel immunotherapy targets can
be screened by conducting pan-cancer analysis of genes in human tumors [8], including those that characterize the tumor microenvironment and targeted signaling pathways [9].
MEIS proteins, which belong to the three amino acid loop extension (TALE) class of transcription factor family, whose members include MEIS1 (NC_000002.12, Figure S1), MEIS2, and MEIS3 [10]. MEIS1 was first described in a leukemia mouse model. According to the single cell type module of Human Protein Atlas (http://www.proteinatlas.org/, accessed on 12 January 2023), MEIS1 expression is specificity to oligodendrocytes, rod photoreceptor cells, and endometrial stromal cells (Figure S2a). Meanwhile, in different blood and immune cells, the expression of MEIS1 was higher in total peripheral blood mononuclear cells (PBMC), natural killer (NK)-cells, and plasmacytoid dendritic cells (DC) (Figure S2b).
The expression of MEIS1 was influenced by cell types, age, the environment humans stay in, their pathological state, and the metabolism features of cancer cells [10,11]. MEIS1 was engaged in numerous cellular processes including chromatin remodeling, cell cycle regulation, apoptosis, and transcription regulation of self-renewal genes [12]. Research has shown that MEIS1 was strongly related to HOX genes and their cofactors to exert its regulatory effects on numerous signaling pathways [13]. Moreover, MEIS1 was a directly repressed target of MYC, and via effects on HOXB13, links MYC activity to androgen receptor activity to mediate cancer development [14]. MEIS1 can promote leukemogenesis and supports leukemic cell homing and engraftment by inducing synaptotagmin-like 1 (SYTL1) [15]. The impaired expression of MEIS1 was highly correlated with the poor prognosis of colorectal cancer patients [16]. In addition, MEIS1 was identified to reduce major histocompatibility complex class II (MHCII)expression in acute myeloid leukemia (AML) cells [17]), MEIS1 overexpression improved survival in patients with AML [18].
As a transcription factor, MEIS1 drives cell growth [19], dysregulated MEIS1 expres- sion contributes to tumorigenesis in multiple tumor types [12,20-23]. Due to its role in cancer cell proliferation, MEIS1 can become a molecular biomarker for cancer diagnosis and even a target for cancer therapy [24]. However, the expression levels of MEIS1 in various tumors are different [10,24]. It is a negative regulator in non-small-cell lung cancer (NSCLC) [25], whereas it serves as a positive regulator in esophageal squamous cell car- cinoma (ESCC) [26], malignant peripheral nerve sheath tumor (MPNST) [19] and Ewing sarcoma [27]. Even different studies found inconsistent expression of MEIS1 in prostate cancer [28,29]. The function of MEIS1 in cancer needs reassessing, we speculated that the oncogenic role of MEIS1 was affected by multiple factors, and it is possible that the complex role of MEIS1 in proliferation may largely depend on the tumor microenvironment (TME) [30]; nevertheless, the function of MEIS1 in TME remains uncertain. Various online tools were used to analyze the data from TCGA, GTEx and GEO databases, which intended to explore the potential mechanisms of MEIS1 across different cancers.
2. Materials and Methods
2.1. The Analysis of MEIS1 Expression in Different Cancers
MEIS1 gene differential expression between tumor tissues and normal tissues in 33 tumor types was explored in Gene_DE module of Tumor Immune Estimation Resource 2.0 (TIMER 2.0) [31]. Concerning many tumors without control data in TIMER 2.0, the Gene Expression Profile module of Gene Expression Profiling Interactive Analysis version 2 (GEPIA2) was used to match TCGA with GTEx data [32]. The differential expression analysis method was ANOVA and log2 (TPM + 1) for log-scale was used (we set | Log2FC | Cutoff as 1 with q-value Cutoff of 0.01) for box plots. “Stage Plots” in GEPIA2 can present MEIS1 gene expression in different stages in 26 tumor types. Log2(TPM + 1) for log-scale was used for violin plots.
The University of Alabama at Birmingham Cancer (UALCAN) data analysis Portal was used to analyze MEIS1 protein expression, phosphoprotein level and promoter methy- lation [33]. Breast cancer, clear cell renal cell carcinoma (ccRCC), colon cancer, lung ade-
nocarcinoma (LUAD), ovarian cancer, and uterine corpus endometrial carcinoma (UCEC) were available for total-protein, phosphorylation and promoter methylation level analyses.
2.2. Association of MEIS1 with Survival in Different Tumors
In GEPIA2, the “Survival Analysis” module was used to reveal OS and disease- free survival (DFS) curves using the Kaplan-Meier method for the high and low MEIS1 expression groups in different cancer types. High and low expression groups were classified according to a 50% (median) cutoff of MEIS1 expression, and the hypothesis test method was the log-rank test.
2.3. Gene Alteration and Immune Infiltration Analysis of MEIS1
cBioPortal (https://www.cbioportal.org/, accessed on 28 September 2022) was equipped with the function to analyze gene alteration frequency, alteration types (mutation, ampli- fication, multiple alterations), and mutation sites. The expression of MEIS1 in 22 tumor- infiltrating immune cells (TIICs) was evaluated by CIBERSORT on the SangerBox website (http://sangerbox.com/Tool, accessed on 28 September 2022). Meanwhile, the relationship between the expression of MEIS1 and immune checkpoint genes (ICPGs), TME biomarkers including TMB, MSI, and NEO were also explored. Furthermore, the relevance between MEIS1 expression and immune subtypes of 30 tumor types and molecular subtypes of 17 tumor types were analyzed utilizing the TISIDB database (http://cis.hku.hk/TISIDB/ index.php, accessed on 28 September 2022).
2.4. Gene Enrichment Analysis of MEIS1
Firstly, the top-50 proteins binding with MEISI1 were obtained in STRING (https: / /string-db.org/, accessed on 7 October 2022) by setting “the minimum required interac- tion score”, “the meaning of network edges”, “active interaction sources” and “the max number of interactors to show” to “Low confidence”, “Evidence”, “Experiments” and “No more than 50 interactors”, respectively. The top-50 MEIS1-binding genes network was also presented.
The “Similar Gene Detection” module of GEPIA2 was used to detect 100 similar genes to MEIS1. In addition, the first five genes correlated with MEIS1 were selected according to Pearson’s correlation coefficients using the “Correlation Analysis” module of GEPIA2.
VENNY2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html, accessed on 7 October 2022) was used to compare interacted genes with similar genes. GO and KEGG analysis was performed on 147 genes in two sets. “org.Hs.eg.db” package was applied to ID transform and the “clusterProfiler” package was applied to enrichment analysis. Data visualization was achieved through the “ggplot2” packages. This analysis was realized by R software [R-4.1.0, 64-bit, Vienna, Austria].
2.5. Ethical Approval
Our study was conducted in accordance with the principles stated in the Declaration of Helsinki, the secondary analysis of existing data of public use data sets does not require informed consent and ethical approval.
3. Results
3.1. Differential Expression of MEIS1 in Cancers
The result of MEIS1 expression in different cancers by the TIMER2.0 webserver was shown in Figure 1a, MEIS1 was down-regulated (the gene expression in the tumor group was lower than that in the control group) in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), COAD, HNSC, kidney renal papillary cell carcinoma (KIRP), LUAD, lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), thyroid carcinoma (THCA) and uterine corpus endometrial car- cinoma (UCEC) (p < 0.001), pheochromocytoma and paraganglioma (PCPG) (p < 0.01), stomach adenocarcinoma (STAD), cervical squamous cell carcinoma and endocervical ade-
nocarcinoma (CESC) (p < 0.05). MEIS1 expression in tumor tissues of cholangiocarcinoma (CHOL), Liver hepatocellular carcinoma (LIHC) (p < 0.001) and glioblastoma multiforme (GBM) (p < 0.05) was higher than normal tissues.
a
TCGA dataset
MEIS1 Expression Level (log2 TPM)
*
*
**
*
*
7.5
-
4
5.0
2.
HIN.
0.0
ACC.Tumor (n=79)
BLCA.Tumor (n=408)
BLCA.Normal (n=19)
BRCA. Tumor (n=1093)
BRCA.Normal (n=112)
BRCA-Basal. Tumor (n=190)
BRCA-Her2.Tumor (n=82)
BRCA-LumA. Tumor (n=564)
BRCA-LumB. Tumor (n=217)
CESC.Tumor (n=304)-
CESC.Normal (n=3)
CHOL. Tumor (n=36)
CHOL.Normal (n=9)
COAD.Tumor (n=457)
COAD.Normal (n=41)-
DLBC. Tumor (n=48)
ESCA.Tumor (n=184)
ESCA.Normal (n=11)
GBM.Tumor (n=153)
GBM.Normal (n=5)
HNSC.Tumor (n=520)
HNSC.Normal (n=44)
HNSC-HPV+.Tumor (n=97)
HNSC-HPV -. Tumor (n=421)
KICH. Tumor (n=66)
KICH.Normal (n=25)
KIRC.Tumor (n=533)
KIRC.Normal (n=72)
KIRP.Tumor (n=290)
KIRP.Normal (n=32)
LAML. Tumor (n=173)
LGG.Tumor (n=516)
LIHC.Tumor (n=371)
LIHC.Normal (n=50)
LUAD.Tumor (n=515)
LUAD.Normal (n=59)
LUSC.Tumor (n=501)
LUSC.Normal (n=51)
MESO.Tumor (n=87)
OV.Tumor (n=303)
PAAD.Tumor (n=178)
PAAD.Normal (n=4)
PCPG.Tumor (n=179)
PCPG.Normal (n=3)
PRAD.Tumor (n=497)
PRAD.Normal (n=52)
READ.Tumor (n=166)
READ.Normal (n=10)
SARC.Tumor (n=259)
SKCM.Tumor (n=103)
SKCM.Metastasis (n=368)
STAD.Tumor (n=415)
STAD.Normal (n=35)
TGCT.Tumor (n=150)
THCA.Tumor (n=501)
THCA.Normal (n=59)
THYM.Tumor (n=120)-
UCEC.Tumor (n=545)
UCEC.Normal (n=35)
UCS. Tumor (n=57)
UVM. Tumor (n=80)
b TCGA+GTEx dataset
00
MEIS1 Expression log2(TPM+1)
2
0
3
..:- 1.
«
E.
₪
-
2
o
Tumor (N=77)
ACC
LAML
Normal (N=128)
Tumor (N=173)
SKCM Normal
TGCT Tumor (N=137)
THYM Tumor (N=118)
UCS
Normal
Tumor (N=461) (N=558)
Normal (N=165)
Normal (N=339)
Tumor (N=57)
Normal (N=78)
(N=70)
C CPTAC dataset
Protein Expression of MEIS1 (Zvalue)
Breast cancer ***
Colon cancer ***
Clear cell RCC ***
UCEC ***
LUAD ***
Normal N=18
Tumor
Normal N=100
Tumor
Normal N=84
Tumor N=110
Normal N=31
Tumor
Normal N=111
Tumor N=111
N=125
N=97
N=100
d
TCGA dataset
MEIS1Expression Jog2(TPM+1)
ACC
F value = 3.45 Pr[F] = 0.0221
COAD
F waive = 2.9 Pr(F) = 0.0056
KIRC
F value = 4.73 Pip-F) - 0.00291
KIRP
F value = 6.06
Pm(F) = 0.000526
LIHC
F value = 4.13
PmpF) = 0.0068
Stage
II
Ill
IV
Stage
MI
III
IV
Stage
I
Il
HI
IV
Stage
1
İl
ill
IV
Stage
1
Il
İlI
IV
For those tumors without control tissues in TIMER 2.0 datasets, GEPIA2 was used to match these tumor tissues with the control tissues in the GTEx database to obtain boxplots of expression differences. As shown in Figure 1b (p < 0.05), MEIS1 was differentially expressed in ACC, acute myeloid leukemia (LAML), skin cutaneous melanoma (SKCM), testicular germ cell tumors (TGCT), thymoma (THYM) and uterine carcinosarcoma (UCS), which was down-regulated in ACC, SKCM, TGCT and UCS, and up-regulated in LAML and THYM. Furthermore, the statistically significant results were verified through GEO database. These results were presented in Figure S3, which were consistent with the results of MEIS1 differential expression analysis using the TCGA and GTEx database. MEIS1 protein expression in six common tumors was executed utilizing the CPTAC dataset. Figure 1c showed the total MEIS1 protein expression in breast cancer, colon cancer, LUAD, UCEC and ccRCC was higher than that in normal tissues (p < 0.001). Figure 1d revealed the correlation between pathological stages and MEIS1 expression levels, including ACC, COAD, KIRC, KIRP, and LIHC (p < 0.05). The correlation between pathological stages and MEIS1 expression levels in other cancer types with no statistical significance were shown in Figure S4.
3.2. Prognostic Index of MEIS1 in Different Cancers
As shown in Figure 2a, GEPIA2 survival analysis showed that low MEIS1 expression predicts poor OS of ACC, HNSC and KIRC patients (p < 0.01), while high MEIS1 expression predicts poor OS of COAD patients (p < 0.05) and LGG patients (p <0.001). As shown in Figure 2b, high MEIS1 expression predicts poor DFS of COAD and KIRP patients (p < 0.01), and LGG patients (p < 0.001), while low MEIS1 expression predicts poor DFS of HNSC patients (p <0.05).
a
Overall Survival
log10(HR)
ENSG00000143995.19
0.25
(MEIS1)
0.00
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
-025
9
Low MEIS1 Group High MEIS1 Group
:
Low MEIST Group High MEIST Group Logrank p=0.021 HR(high)=1.8
9
Low MEIS1 Group
1
Low MEIST Group Non MEIST Group Logrank p=0.02 HR (high)a) 7
2
Low MEIST Group
9
High MEIS1 Group Logrank p=9.80-07 HR(high) 2 5
Low MEIS1 Group On MEIST Group
Logrank pu0.0087
HRInich| 0 33 ((199)=0.012
Hon MEIST Group Logrank p=0.002 HR(high)=0.66
8
8
n(high)=38 n(jow)=38
POUR)-0.022
0
P(HR)=0.0021 nghigh)=258
12
Percent survival
D(HR)-0.021 n(high)=258
0
n(high)=134
p(HR)-2.30-06 n(high)=257 n(ow)-257
S
HR(hg )=0.38
PIHR TO 041 ningh)39 n[low)=39
8
ngow)=135
8
n(low)=259
0
n(low)-258
8
8
5
2
0
2
3
0
8
8
2
2
2
8
ACC
COAD
HNSC
KIRC
LGG
UVM
8
0
8
0
2
8
0
50
100
150
0
50
100
150
0
50
100
150
200
0
50
Months
100
150
0
50
100
150
200
0
20
40
60
80
Months
Months
Months
Months
Months
b Disease Free Survival
log10(HR)
ENSG00000143995.19 (MEIS1)
02
CESC CHOL
0.0
ACC
BLCA
BRCA
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
-02
9
Low MEIST Group High MEIS1 Group Logrank p=0.0038
e
Low MEIST Group COMEIST Group Logrank p=0.049 HR(high)=0.72
0
Low MEIS! Group High MEIST Group Logrank paQ 0043
1
Low MEIST Group
High MEIS1
8
HR[high)=2
8
8
HR(high)=23
Logrank pm1.Ge-05
(HR)=0.0046
p(HR)=0.049 n(high).258
p(HR)=0.0055 n(high)=141 n(low)m141
0
HR(high)=2
Percent survival
n(high)=134 n(low)= 135
P(HR)=2:26-05 n(high)-257 n(ow)-257
de
4
8
now)-259
8
8
o
8
2
2
8
0
2
2
COAD
HNSC
KIRP
LGG
:
0
6
0
0
50
100
150
0
50
100
150
200
0
50
100
150
200
0
50
100
150
Months
Months
Months
Months
3.3. Alteration of MEIS1 Gene Analysis Data
According to Figure 3a, in NSCLC, the frequency of gene change was the highest (3.51%) and mutation is the main type of gene change. Mutation is the main type of genetic change in ESCC, melanoma, endometrial carcinoma, esophagogastric adenocarcinoma, colorectal cancer, cervical squamous cell carcinoma, adrenal cortical cancer and hepato- cellular carcinoma as well. Likewise, amplification became the primary type of MEIS1 gene alteration in ovarian epithelial tumor, bladder urothelial carcinoma, mature B-cell neoplasms and prostate adenocarcinoma. Furthermore, Figure 3b contains more details, such as mutation types, sites and the number of cases with “Missense” as the main type of mutation. R102Afs*20 mutation can be observed in 2 COAD patients while R102Pfs*18 can be detected in a UCEC patient. We did not find any hotspot mutation in the MEIS1 gene.
a
Mutation
Structural Variant
Amplification
Deep Deletion
Multiple Alterations
3%-
Alteration Frequency
2%
1%-
Structural variant data
Mutation data
CNA data
Non-Small Cell Lung Cancer
Ovarian Epithelial Tumor
Esophageal Squamous Cell Carcinoma
Melanoma
Colorectal Adenocarcinoma
Bladder Urothelial Carcinoma
Endometrial Carcinoma
Esophagogastric Adenocarcinoma
Mature B-Cell Neoplasms
Cervical Squamous Cell Carcinoma
Sarcoma
Head and Neck Squamous Cell Carcinoma
Adrenocortical Carcinoma
Hepatocellular Carcinoma
Prostate Adenocarcinoma
Invasive Breast Carcinoma
Renal Clear Cell Carcinoma
Thymic Epithelial Tumor
Undifferentiated Stomach Adenocarcinoma
Renal Non-Clear Cell Carcinoma
Well-Differentiated Thyroid Cancer
Pleural Mesothelioma
Pheochromocytoma
Miscellaneous Neuroepithelial Tumor
Cervical Adenocarcinoma
Diffuse Glioma
Ocular Melanoma
Leukemia
Glioblastoma
Cholangiocarcinoma
Pancreatic Adenocarcinoma
Seminoma
Non-Seminomatous Germ Cell Tumor
b
5
# MEIS1 Mutations
91
Missense
COAD (n=2)
10 Truncating
R102Afs*20/Pfs*18
0 Inframe
UCEC (n=1)
6 Splice
1 SV/Fusion
0
Homeobo ..
0
100
200
300
390aa
3.4. Phosphorylation and Promoter Methylation Expression of MEIS1 across Different Cancers
Changes in phosphorylation pathway are closely related to cancer. Based on the CPTAC dataset, breast cancer, ovarian cancer, colon cancer, ccRCC, UCEC, LUAD and pediatric brain cancer were included to explore phosphorylation level between their tumor tissues and normal tissues. Ultimately, the box plots of four types of cancer are available in Figure 4a. In ovarian cancer, MEIS1 phosphoprotein level (S194, S196 and T202) between tumor and normal tissues has no statistical differences, while the phosphorylation level of S196 in LUAD (p <0.001), ccRCC (p <0.001) and UCEC (p <0.001) were higher in normal tissues compared to tumor tissues. More experiments evidence is needed to identify the function of MEIS1 phosphorylation at the S196 site in tumorigenesis. In addition, it is found that promoter methylation level of MEIS1 in BLCA, HNSC, KIRC, KIRP, PRAD and UCEC were lower in primary tumors compared to normal tissues (p < 0.05) (Figure 4b).
a
Ovarian cancer
3
Ovarian cancer
3-
Ovarian cancer
2
2
2.
Z value
P = 2.8 × 10-1
1
1-
P = 3.0 × 10-1
1 -
P = 2.0 × 10-1
0
0
0
-1
-1
-1
-2
-2
-2
S194
S196
T202
-3
Normal N=19
Tumor N=84
-3
Normal N=19
Tumor N=84
-3
Normal N=19
Tumor N=84
3
6
Lung adenocarcinoma
4
Clear cell RCC
UCEC
2
4
2
Z value
1
2
0
0
0
-1
P = 2.5 × 10-30
P = 2.4 × 10-31
P = 3.8 x 10-15
-2
-2
S196
S196
-2
S196
-4
Normal N=102
-4
Tumor N=111
Normal N=83
Tumor N=110
-3
Normal N=31
Tumor N=100
b
Promoter methylation level of MEIS1 in BLCA
Promoter methylation level of MEIS1 in HNSC
Promoter methylation level of MEIS1 in KIRC
0.1
0.1
0.08
Beta value
P = 9.4 × 10-4
0.09
P = 6.8 x 10-3
P = 6.0 × 10-5
0.08
0.07
0.08
0.07
0.06
0.06
0.06
0.05
0.04
0.05
0.04
0.04
0.02
0.03
Normal (n=21)
Primary tumor (n=418)
0.03
Normal (n=50)
Primary tumor (n=528)
Normal (n=160)
Primary tumor (n=324)
Promoter methylation level of MEIS1 in KIRP
Promoter methylation level of MEIS1 in PRAD
Promoter methylation level of MEIS1 in UCEC
0.08
0.175
0.09
0.15
P = 2.2 × 10-16
0.08
P = 1.2 × 10-2
Beta value
0.07
P = 7.4 × 10-4
0.07
0.125
0.06
0.06
0.1
0.05
0.05
0.075
0.04
0.04
0.05
0.03
0.03
0.025
0.02
Normal (n=45)
Primary tumor (n=275)
Normal (n=50)
Primary tumor (n=502)
Normal (n=46)
Primary tumor (n=438)
3.5. MEIS1 Expression in Immune and Molecular Subtypes across Different Cancers
As shown in Figure 5a, the expressions of MEIS1 were different in various immune subtypes including C1-C6 in ACC, BLCA, BRCA, CHOL, COAD, KIRC, LGG, LUAD, LUSC, pancreatic adenocarcinoma (PAAD), PRAD, sarcoma (SARC), STAD, TGCT (all p < 0.05). In addition, MEIS1 was diversely expressed in multiple molecular subtypes of ACC, BRCA, esophageal carcinoma (ESCA), GBM, HNSC, KIRP, LGG, LIHC, LUSC, ovarian serous cystadenocarcinoma (OV), PCPG, PRAD, STAD and UCEC (all p < 0.05) (Figure 5b).
a
ACC MEIS1_exp Pv = 3.11 x 10 -*
BLCA MEIS1_exp Pv = 5.11 * 10 -*
BRCA MEIS1_exp Pv = 3.37 * 10 -*
CHOL MEIS1_exp Pv = 3.9 * 10-
COAD MEIS1_exp Pv = 2.72 * 10-2
n=C1 1,C2 1,C3 23,C4 49,C5 3,C6 1
n=C1 173,C2 164,C3 21,C4 36,C6 3
n=C1 369,C2 390,C3 191,C4 92,C6 40
n=C1 7,C2 2,C3 17,C4 8,C6 1
n=C1 332,C2 85,C3 9,C4 12,C6 3
Expression (log2CPM)
10.0
8
8
6
7.5
:
.
7.5
Y
6
5.0
H
H
!
0
4
9
U
B
4.
H
H
H
:
!
8
4
!
2.5-
H
8
I
5.0
8
1
2
0
H
0.0
2.5
0
2
2.5
4
C1
C2
C3
C4
C5
C6
2
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
KIRC MEIS1_exp Pv = 1.29 x 10-
LGG MEIS1_exp Pv = 3.49 x 10 -*
LUAD MEIS1_exp Pv = 5.42 x 10-
LUSC MEIS1_exp Pv = 2.16 x 10-
PAAD MEIS1_exp Pv = 9.68 x 10-
n=C1 7,C2 20,C3 445,C4 27,C5 3,C6 13
n=C3 10,C4 147,C5 356,C6 1
n=C1 83,C2 147,C3 179,C4 20,C6 28
n=C1 275,C2 182,C3 B,C4 7,C6 14
Expression (log2CPM)
8
8
n=C1 57,C2 32,C3 40,C4 1,C6 21
8
5.0
7.5
U
8
H
9
6
6
6
J
E
5.0-
I
I
1
A
H
1
i
8
2.5
4
H
!
:
8
4
:
I
H
2.5
8
8
4
0.0-
2
2
0.0
2
C1
C2
C3
C4
C5
C6
C3
C4
C5
SARC MEIS1_exp Pv = 4.21 x 10
C6
C1
C2
C3
C4
C6
0
C1
C2
C3
C4
C6
C1
C2
C3
C4
PRAD MEIS1_exp
Pv = 1.34 x 10-
STAD MEIS1_exp Pv = 3.11 x 10-1
C6
TGCT MEIS1_exp Pv = 2.42 * 10-
Subtype
n=C1 35,C2 18,C3 307,C4 45
n=C1 64,C2 38,C3 42,C4 59,C6 20
12
n=C1 129,C2 210,C3 36,C4 9,C6 7
n=C1 42,C2 104,C3 2,C4 1
Expression (log2CPM)
5.0
H
:
8
·
7.5
.
8
2.5
I
İ
!
5.0
4
!
!
I
1
8
Y
U
8
4
I
0.0
2.5
0
:
0
-2.5
0.0
C1
C2
C3
C4
-4
Subtype
C1
C2
C3
C4
C6
Subtype
C1
C2
C3
C4
C6
C1
C2
C3
C4
b
Subtype
Subtype
ACC MEIS1_exp Pv = 2.21× 10-
BRCA MEIS1_exp Pv = 1.19 * 10 -**
ESCA MEIS1_exp Pv = 1.62 x 10 -*
GBM MEIS1_exp
naBasal 172, SOR LumA 508. LumB 191. Normal 137
Pv = 3 x 10-1
naCIN 74. ESCC 90, GS 1. HM-SNV 2 HM-indel 2
naClassic-like 47. high2. G-CIMP-low 5, LGm6-GBM 12. Mesenchymal-like 53
HNSC MEIS1_exp Pv = 8.56 * 10-
CIMp.Internae 27
Expression (log2CPM)
n Atypical 67, Basal 87, Classical 48. Mesenchymal 74
CIMP-low 32
10.0
8
8
7.5
6
6
6
H
:
6
:
H
H
₿
4
H
i
H
H
5.0
3
H
:
8
8
4
I
4
I
2.5
0
2.
Ş
2
2
A
CIMP-high
MP-intermediate
CIMP-low
3
0
O
Basal
Her2
LumA
LumB
Normal
CIN
ESCC
GS
HM-SNV
HM-indel
Classic-like
G-CIMP-high
G-CIMP-low
LGm6-GBM
Mesenchymal-like
Atypical
Basal
Classical
Mesenchymal
KIRP MEIS1_exp Pv = 5.8 x 10-+
LGG MEIS1_exp
Pv = 7.44 x 10-%
nClassic-like 23.
Expression (log2CPM)
G-CIMP-high 234, G-CIMP-low 12. Mesenchymal-like 45. PA-like 26
LIHC MEIS1_exp Pv = 2.95 x 10 -* n=Cluster:1 64. iCluster:2 55, iCluster:3 63
LUSC MEIS1_exp Pv = 2.6 x 10-+ n=basal 42. classical 63. primitive 26. secretory 39
OV MEIS1_exp Pv = 1 x 10
C2a 35.
C2b 22
Codel 171.
C2c-CIMP 9
n=Differentiated 66.
6
Immunoreactive 78. Mesenchymal 71. Proliferative 78
3
8
8
10.0
I
i
.
6
4
:
A
N
6
:
6
0
4
7.5
:
:
B
:
O
8
2
2
:
N
C
I
4
4
!
i
H
5.0
3
C2c-CIMP
Classic-like
Codel
G-CIMP-high
G-CIMP-low
Mesenchymal-like
PA-like
0
2
2.5
C1
C2a
C2b
O
classical
primitive
secretory
Differentiated
Immunoreactive
Mesenchymal
Proliferative
basal
PRAD MEIS1_exp Pv = 3.67 x 10-1
iCluster:1
iCluster:2
iCluster:3
Subtype
PCPG MEIS1_exp
2-ETV1 28.
Pv = 1.33 x 10 -*
3-ETV4 14,
STAD MEIS1_exp
n=Corticaladmixture 22.
4-FLI1 4,
Pv = 2 * 10*
UCEC MEIS1_exp
Kinasesignaling 68, Pseudohypoxia 61, Wnt-altered 22
5-SPOP 37.
naCIN 223, EBV 30. GS 50.
Pv = 1.36 x 10-7
6-FOXA1 9.
Expression (log2CPM)
CN LOW 144, MSI 124, POLE 79
7-IDH1 3,
HM-SNV 7. HM-indel 73
8-other 86
7
6
7.5
.
8
5
:
:
S
H
3
8
8
8
H
8
0
8
I
H
5.0
i
İ
i
9
N
!
0
N
!
4
3
2.5
Orticaladmixture
Kinasesignaling
Pseudohypoxia
Wnt-altered
3
1-ERG
2-ETV1
3-ETV4
4-FLI1
5-SPOP
6-FOXA1
7-IDH1
8-other
0.0
0
CIN
EBV
GS
HM-SNV
HM-indel
CN_HIGH
CN_LOW
MSI
POLE
Subtype
Subtype
Subtype
Subtype
3.6. MEIS1 Expression Is Related to ICPGs Expression in Different Cancers
Subsequently, the correlation between MEIS1 expression and 60 ICPGs expression was explored (Figure 6). In most cancers, such as lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), PRAD, READ, COAD, OV, KIRC and LIHC, MEIS1 expression was positively related to the expression of most ICPGs. It means that the patients with high expression of MEIS1 will show better immunotherapy effects by using immune checkpoint inhibitors (ICIs). However, in TCGT and SARC, MEIS1 expression was negatively correlated with most ICPGs expression. It showed that patients with high MEIS1 expression will have a poor prognosis when targeting these ICPGs. Therefore, it can be proved that MEIS1 is equipped with strong potential in immunotherapy.
Type
CD276
correlation coefficient
VEGFA
HAVCR2
-1.0-0.5
0.0
0.5
1.0
IL10
pValue
TGFB1
C10orf54
0.0
0.5
1.0
EDNRB
Type:
IL12A
Inhibitory
VEGFB
Stimulaotry
VTCNI
ARGI
IL13
IL4
KIR2DL1
KIR2DL3
ADORA2A
BTLA
IDO1
TIGIT
CTLA4
SLAMF7
CD274
LAG3
PDCDI
ICOSLG
TNFRSF14
ILIA
IL1B
TNFSF9
IFNA1
IFNA2
IFNG
PRF1
GZMA
CCL5
CD70
TNFRSF18
TNFRSF4
CD28
ITGB2
CD80
IL2RA
IL2
TNF
CD40LG
CD27
ICOS
CXCL10
CXCL9
CD40
ICAM1
TNFRSF9
HMGB1
SELP
ENTPD1
TNFSF4
CX3CL1
TLR4
BTN3A1
BTN3A2
3.7. The Correlation between MEIS1 Expression and Immune Cell Infiltration and TME in Different Cancers
According to results in Figure 7, MEIS1 expression was closely correlated with most immune cells in various cancers. The expression of MEIS1 was correlated with macrophages_M2 in 20 cancer types, CD8+T cells in 19 cancer types, Macrophages_M1 in 15 cancer types, macrophages_M0 in 13 cancer types and neutrophils in 10 cancer types. For evaluating anti-tumor immunity, as shown in Figure 8, the relationships between MEIS1 and TMB, MSI and NEO were explored in all cancers. MEIS1 expression was negatively associated with TMB in ACC, STAD, STES, SARC, BLCA and KIRC. As for MSI, MEIS1 expression was negatively correlated with MSI in UCS, UCEC, SARC, STAD, STES, GBM, LGG, KIPAN, PRAD and HNSC, while it was positively associated with MSI in TGCT. In addition, MEIS1 expression had a negative correlation with NEO in SARC, UCEC, KIRP, KIPAN, KIRC and LUAD.
correlation coefficient
0.5
0.0
0.5
p Value
0.0
0.5
1.0
1.5
2.0
8
*
A
»
*
*
*
8
8
*
*
$
TCGA-PRAD(N=495)
*
*
*
*
ĐỘNG
*
*
TCGA-TGCT(N=132)
-
4
4
-4
.
*
4
44
4
4
TCGA-HNSC(N-517)
#
*:
*
*:
#:
*
$
TARGET-ALL-R(N-99)
*
*
*
**
+
*
*
*
#
The
TCGA-LAML(N-214)
*
*
*
*
*
*
*
#
*
*
*
TCGA-KIPAN(N-878)
¥
*
*
*
$
*
*
TCGA-BRCA(N-1077)
*
*
%
+
+
+
*
*
*
Tận
TCGA-STAD(N-388)
-
+
-
-
-
*
+
.
-
TCGA-CESC(N-291)
*
3
*
TCGA-MESO(N-85)
-
-
₦
¥
+
.
.
-
·
*
NH
TCGA-STES(N-569)
-
-
.
5
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-
TOGA-KIRP(N-285)
*
*
*
*
*
8
*
*
*
*
TCGA-COAD(N=282)
*
*
*
*
*
*
*
*
*
TCGA-LUSC(N=491)
4
.
4
4
4
TCGA-BLCA(N=405)
*
*:
*
**
*
+
*
*
*
*
*
** TCGA-COADREAD(N-373)
*
*
*
*
*
*
*
*
*
*
*
*
TCGA-KIRC(N=528)
*
*
*
*
*
TCGA-LIHC(N-363)
%
*
*
TCGA-DLBC(N-46)
V+
1)
.
TCGA-UVM(N-79)
*
*
*
*
*
TCGA-PAAD(N=177)
*
3
¥
¥
*
¥
TCGA-SKCM(N-452)
¥
¥
.
*
*
TARGET-ALL(N-86)
*
-
4
TARGET-WT(N-80)
*
-
*
*
TCGA-OV(N=417)
*
M
*
*
*
ICGA-SARC(N 258)
*
*
*
*
*
.
TCGA-THCA(N-503)
TCGA-UCS(N-56)
*
**
*
¥
*
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*
TARGET-NB(N=153)
*
*
*
*
TCGA-ESCA(N-181)
%
**
*A
*
*
*
TCGA-UCEC(N=178)
*
*
*
*
*
*
TARGET-I.AMI.(N=142)
*
*
**
**
*
*
TCGA-SKCM-M(N=351)
**
*
*
*
TCGA-GBM(N-152)
*
*
**
TCGA-KICH(N-65)
*
*
TCGA-CHOL(N-36)
*
*
*
*
**
.
.
*
*
.
Ml
*
*
Pc
TCGA-GBMLGG(N=656)
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TCGA-SKCM-P(N-101)
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Re
TCGA-READ(N-91)
De
%
**
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*
*
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*
TCGA-THYM(N-118)
*
*
*
.
*
*
*
*
*
*
*
TCGA-LGG(N-504)
*
*
*
TCGA-ACC(N-77)
*
%
*
*
**
*
*
*
TCGA-LUAD(N-500)
TCGA-PCPG(N=177)
B_cells_naive
B_cells_memory
Plasma_cells
T_cells_CD8
T_cells_CD4_naive
T_cells_CD4_memory_resting
T_cells_CD4_memory_activated
T_cells_follicular_helper
T_cells_regulatory_(Tregs)
T_cells_gamma_delta
NK_cells_resting
NK_cells_activated
Monocytes
Macrophages_MO
Macrophages_MI
Macrophages_M2
Dendritic_cells_resting
Dendritic_cells_activated
Mast_cells_resting
Mast_cells_activated
Eosinophils
Neutrophils
3.8. MEIS1-Related Genes Are Correlated to Cell Proliferation and Differentiation
Based on STRING, 50 MEIS1-binding proteins supported by experiments were ob- tained. The protein-protein interaction networks are presented in Figure 9a. After that, 100 similar genes were acquired from correlation analysis in GEPIA2, 50 interacted genes and 100 similar genes have 3 overlap genes in Figure 9b. Then, the 147 genes were integrated to perform GO and KEGG analysis presented in Figure 9c. KEGG analysis indicates that “Transcriptional misregulation in cancer” might play a vital role in the effect of MEIS1 on cancers. Furthermore, by GO analysis, at the biological process (BP) level, most of the genes
are involved in cell differentiation and cell fate determination. At the molecular function (MF) level, most of the genes took part in regulating DNA-binding transcription activator and repressor activity, RNA polymerase II-specific, enhancer sequence-specific and acti- vating transcription factor binding. At last, the cellular component (CC) also proved that these genes were related to transcription.
a
TMB
CHOL(N=36)
SampleSize
ACC(N=77)
·
STAD(N=409)
STES(N=589)
200
SARC(N=234)
ESCA(N=180)
400
LAML(N=126)
BLCA(N=407)
600
KIRP(N=279)
GBM(N=149)
800
CESC(N-286)
SKCM(N=102)
PRAD(N=492)
KIRC(N=334)
LUAD(N=509)
pValue
DLBC(N=37)
0.0
LUSC(N=486)
PCPG(N=177)
-0.2
THYM(N=118)
UCEC(N-175)
-0.4
UVM(N=79)
HNSC(N=498)
-0.6
OV(N=303)
THCA(N=489)
-0.8
TGCT(N=143)
-1.0
KIPAN(N=679)
BRCA(N=981)
GBMLGG(N=650)
MESO(N=82)
READ(N=90)
UCS(N=57)
LGG(N=501)
PAAD(N=171)
COADREAD(N=372)
LIHC(N-357)
COAD(N=282)
KICH(N-66)
-0.2
-0.1
0.0
0.1
0.2
0.3
Correlation coefficient(pearson)
b
MSI
UCS(N=57)
SampleSize
UCEC(N-180)
.
DLBC(N=47)
SARC(N=252)
200
STAD(N=412)
STES(N-592)
400
GBMLGG(N-657)
ACC(N-77)
600
KIPAN(N=688)
PAAD(N=176)
800
PRAD(N=495)
ESCA(N=180)
= 1,000
HNSC(N=500)
SKCM(N=102)
UVM(N-79)
pValue
CESC(N=302)
0,0
PCPG(N-177)
THYM(N=118)
-0.2
KIRP(N-285)
COADREAD(N=374)
-0.4
READ(N 89)
COAD(N=285)
-0.6
BRCA(N=1039)
KIRČ(N=337)
-0.8
BLCA(N=407)
1.0
LGG(N=506)
THCA(N=493)
LIHC(N-367)
LUAD(N=511)
LUSC(N-490)
GBM(N=151)
MESO(N=83)
OV(N=303)
CHOL(N-36)
KICH(N=66)
LAML(N=129)
TGCT(N=148)
-0.4
-0.2
0.0
0.2
0.4
Correlation coefficient(pearson)
C
NEO
THYM(N=64)
SampleSize
PCPG(N-60)
·
UCS(N=50)
100
SARC(N-177)
200
UVM(N=38)
UCEC(N-166)
300
CHOL(N-30)
400
KICH(N-39)
500
KIRP(N-260)
KIPAN(N-636)
L
600
ACC(N=57)
KIRC(N-337)
LUAD(N=462)
pValue
BLCA(N-375)
0.0
CESC(N=244)
-0.2
PRAD(N=341)
BRCA(N=857)
-0.4
LUSC(N=447)
READ(N=81)
-0.6
COADREAD(N=336)
0.8
COAD(N-255)
HNSC(N=446)
-1.0
GBM(N=117)
LIHC(N=337)
GBMLGG(N=520)
PAAD(N=113)
MESO(N=65)
LGG(N=403)
THCA(N=175)
SKCM(N=83)
DLBC(N=33)
TGCT(N=94)
-0.2
-0.1
0.0
0.1
0.2
Correlation coefficient(pearson)
a
HOXD11
A
HOXC13
C
HOXBB
2
Pattern specification process
GO-BP
p.adjust
6 x 10-12
Regionalization
4 = 10
19
HOXA11
HOXA2
HIOXB13
1 * 10-12
SUMO3
VY
HOXA10
A
HOXA13
Anterior/posterior pattern
A
specification
Courts
DLX3
Embryonic organ development
14
22
HOXD13
HOXD9
30
MX1B
FAM222A
Embryonic skeletal system development
EN2
ENI
PBX4
PS
N
PBX3
0.15
0.20
0.25
0.30
CRTC1
TLX1
PBX2
GeneRatio
PBX1
MAG21L1
SRF
CREB1
MEIS1
MEIS2
PAX6
HORAS
MAB21L2
Transcription factor complex
GO-CC
p.adjust
TLX3
0.06
Y
CREBBP
HOXB4
HOXE9
Nuclear chromatin
0.04
PKNOX
0.02
ETS1
HOXD4
HOX87
HOXB
CRTC2
NUB1
Nuclear transcription.
Counts
0 3
factor complex
9
15
YAP1
HOXA7
HOXA9
HOXD12
TSHZ2
Aggresome
MTX2
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
2
GeneRatio
OVOLf
KMT2A
DNA-binding transcription activator activity, RNA polymerase II-specific
GO-MF
p.adjust
PSIPT
MEN1
0.00 800
DNA-binding transcription repressor activity.
0.00075
RNA polymerase II-specific
0.00050
0.00025
Enhancer binding
Counts
b
Enhancer sequence-specific DNA binding
»
16
MEIS1
20
interacted genes
similar genes
RNA polymerase Il distal enhancer
sequence-specific DNA binding
0.10
0.15
0.20
025
0.30
GeneRatio
47
3
97
Transcriptional misregulation in cancer
KEGG
paqjust
0.03
Human T-cell leukemia virus 1 infection
0.02
0.01
Cushing syndrome
Counts
6
Lysine degradation
10
HOXD4
0.10
0.15
0.20
0.25
0.30
HOXB4
GeneRatio
PBX2
4. Discussion
Cancer is a leading cause of death in each country, and the number of newly confirmed patients continues to increase [2]. Although current cancer therapy options exhibit some clinical success, a large number of cancer patients still have poor prognoses because of drug resistance, adverse effects, and other issues [34]. Consequently, there is a need to identify new therapeutic targets and sensitive biomarkers for cancer diagnosis and treatment. The pan-cancer analysis can deliver deep perception for the design of cancer prevention and precision treatment strategies by revealing the similarities and differences between different cancers [34]. In our study, we comprehensively performed MEIS1 expression and its correlation with prognostic and immunotherapy value in pan-cancer via different databases. The 33 cancer types were studied to identify MEIS1 expression in this study and it finally suggests that MEIS1 expression is different between tumor tissues and normal tissues in 23 cancer types. Among these 23 cancer types, MEIS1 expression is down- regulated in 18 cancer types, on the contrary, the other 5 types were up-regulated.
Another major finding is that MEIS1 expression correlates with survival in cancer patients, which indicated that the abnormal expression of MEIS1 can be responsible for the poor prognosis of tumors. For the value of MEIS1 on the prognosis of cancer patients, it could be determined by the activity of the MEIS1 protein which depends on the en- vironment in which the cell exists. A similar finding was also demonstrated by Schulte et al. and Meng et al. by performing studies on MEIS1 [30,35]. MEIS1 is a hub gene in the differentially expressed gene interaction network for lung adenocarcinoma (LUAD) and participated in the occurrence and prognosis of LUAD [23]. MEIS1 overexpression was inversely correlated with relapse and overall survival in children with acute leukemias [36].
Down-regulated expression of MEIS1 was detected in colorectal cancer and predicted poor survival of colorectal cancer patients [16]. It is believed that abnormal DNA methylation plays a critical role in cancer development and progression [37]. Tumors and benign tis- sues exhibit different methylation patterns [38,39]. Our study found that the promoter methylation levels of MEIS1 in BLCA, HNSC, KIRC, KIRP, PRAD, and UCEC were lower in primary tumors compared to normal tissues. In conclusion, MEIS1 may be able to regulate some tumors at an epigenetic level, but more studies are needed to clarify the deeper mechanisms.
Immune infiltration is highly associated with the prognosis of tumor patients [40]. Six types of immune infiltration had been explored in cancer patients, maybe promoting or inhibiting tumor cell growth [41]. Among them, C5 is positively correlated to MEIS1. A high level of C5 can impair the curative effect of immune checkpoint inhibitors, as reported by Lehrer and his colleagues [42]. Our findings showed that MEIS1 expression was strongly related to most immune cells in various types of cancer. Moreover, MEIS1 expression is related to some immune subtypes and molecular subtypes in different types of cancer. We indicated that MEIS1 can play a role in the growth and progression of cancer and have a close correlation with immune regulation. The TMB, MSI, and NEO which characterize anti-tumor immunity were negatively related to MEIS1 expression in some cancers. Therefore, the immunotherapeutic effect of cancer patients can be predicted according to MEIS1 expression. It can contribute to guide immunotherapy more accurately. The MEIS1 expression was negatively related to TMB, MSI, and NEO in SARC. Thus, there is an inference that patients suffering from SARC with low MEIS1 expression might predicted better survival after immunotherapy. In most cancers, MEIS1 expression was positively related to most ICPGs expression. There is bold speculation that using immune checkpoint inhibitors along with interfering in MEIS expression will have an effective impact on the patient [30].
In gene enrichment analysis, HOXD4, HOXB4, and PBX2 were co-expressed with MEIS1. HOX were transcription factors that serve as an essential regulator for cell fate determination, stem cell functions, and gastrointestinal development. And they need TALE family proteins such as MEIS and PBX to improve their transcriptional efficiencies [43]. MEIS and PBX were correlated with cell growth, differentiation and apoptosis [44,45]. MEIS1 accompanied by HOX protein can form a complex to recruit transcriptional core- pressors or coactivators [13]. Meanwhile, the activation of MEIS1 always couples with the activation of HOXA7 or HOXA9 [46]. Previous papers have summarized the impact of HOX genes on tumors and concluded that HOX genes take part in the development of different tumors [47]. There was research which found that MEIS1 and PBX2 overexpressed in nephroblastomas [48]. So, there may be interference with HOX genes and PBX genes during the role of MEIS1 in cancers. A further mechanism of MEIS1 for different tumors is needed, to explore in the future.
According to KEGG analysis in this study, “Transcriptional misregulation in cancer” might play a vital role in the effect of MEIS1 on cancers. As an important transcription factor, if MEIS1 is over-expressed or low-expressed, transcriptional misregulation will emerge and then play a role in cancer development [49]. Based on GO analysis, MEIS1 may take part in tumor development by regulating cell differentiation and “DNA-binding transcription activator and repressor activity”, “RNA polymerase II-specific”, “enhancer sequence-specific” and “activating transcription factor binding”. Several researches have shown that MEIS1 serves as a tumor suppressor in ccRCC [50], prostate [29], NSCLC [25], gastric [51], and colorectal cancers [52] by promoting cell differentiation and inhibiting epithelial cell proliferation.
5. Conclusions
To sum up, our findings offer a comprehensive understanding of the functions of MEIS1 in prognostic and immunotherapy in different types of cancer. MEIS1 has the potential as a cancer immunotherapy target and is worthy of more attention. Moreover, our
results may provide valuable theoretical guidance for further research on the mechanism of MEIS1 in vivo and in vitro.
6. Limitations
This research had some restrictions. Initially, we only performed a series of bioin- formatics analysis of MEIS1 in multiple databases, the mechanisms of MEIS1 in different cancer types were not verified. Secondly, the small samples for some tumor types may cause inaccurate results or bias.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/jcm12041646/s1, Figure S1: The Genomic context of MEIS1, Figure S2: The specificity expression of MEIS1 in single cell. (a) The single cell type specificity expression of MEIS1. (b) The specificity expression of MEIS1 in blood and immune cells. Figure S3: MEIS1 gene expression level in 16 tumor tissues and normal tissues in GEO database. **** p < 0.0001; ** p < 0.01; * p < 0.05. Figure S4: MEIS1 expression level in different pathological stages of THCA, BRCA, CESC, UCEC, DLBC, ESCA, HNSC, kidney chromophobe (KICH), LUAD, LUSC, UCS, PAAD, READ, SKCM, STAD, TGCT, BLCA, CHOL, and OV (p > 0.05).
Author Contributions: W.L .: conceptualization, writing-original draft preparation. H.L .: methodol- ogy, formal analysis. Y.T .: formal analysis. Z.W. and G.D .: software, validation. S.W .: data curation. L.H .: writing-reviewing and editing. S.L .: conceptualization, funding acquisition. All authors have read and agreed to the published version of the manuscript.
Funding: The study will be financed by China Postdoctoral Science Foundation (2022M721682) and the Medical Scientific Research Project of Jiangsu Provincial Health Commission (ZD2022053).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The datasets presented in this study can be found in online repositories. The names of the repository /repositories and accession number(s) can be found in the article.
Conflicts of Interest: The authors declare no conflict of interest.
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