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Pan-Cancer Analysis Identified C1ORF112 as a Potential Biomarker for Multiple Tumor Types
Jiaxuan Chent, Haoming Mait, Haitao Chen, Bin Zhou, Jinlin Hou* and De-Ke Jiang*
State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Guangdong Institute of Liver Diseases, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
C1ORF112 is an evolutionarily conserved gene across vertebrates. Over the last decade, studies have suggested that C1ORF112 may play a role in tumorigenesis. Using The Cancer Genome Atlas datasets, we explored the role of C1ORF112 across various tumor types in this study. In most tumor types, C1ORF112 expression was increased in tumor tissues compared to corresponding non-tumor tissues. In patients with certain tumor types, higher C1ORF112 expression was correlated with shorter overall survival, disease- free survival, and progression-free survival. Further analyses of C1ORF112 genetic alteration data showed that C1ORF112 amplification and mutations may have an impact on liver hepatocellular carcinoma and uterine corpus endometrial carcinoma prognosis. In cancers including lower grade glioma and adrenocortical carcinoma, C1ORF112 expression was linked to cancer-associated fibroblast infiltration. Gene Ontology analysis showed that C1ORF112 was co-expressed with genes involved in biological processes such as cell cycle and mitotic regulation. The protein interaction network demonstrated that C1ORF112 physically interacted with RAD51, DMC1, and FIGNL1, which have well characterized functions in DNA repair and cell cycle regulation. This pan-cancer study revealed the prognostic value and oncogenic role of C1ORF112 across multiple tumor types.
Keywords: C1ORF112, prognosis, pan-cancer, biomarker, cell cycle
INTRODUCTION
Cancer is the most common cause of death in the world (Sung et al., 2021). To widen the potential therapy options for malignancies, a better knowledge of carcinogenesis and tumor progression through the identification of oncogenes is crucial. Large-scale and multi-omics cancer datasets, such as The Cancer Genome Atlas (TCGA) (ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, 2020; Ye et al., 2021; Lv et al., 2021; Cheng et al., 2021; Cui et al., 2020), have made pan- cancer analysis possible in the last decade.
Chromosome 1 Open Reading Frame 112 (C1ORF112), also known as FLJ10706 (Howe et al., 2021), is evolutionarily conserved, especially in primates (Edogbanya et al., 2021). Mice lacking BC055324 (the mouse ortholog for C1ORF112) are embryonic lethal (https://www.mousephenotype.org/data/genes/ MGI:3590554), indicating that BC055324 is required for embryonic development. According to van Dam et al., genes enriched in the BRCA-Fanconi anemia-related DNA damage response pathway, such as BRCA1 and FANCI, were shown to be co-expressed with C1ORF112 (van Dam et al., 2012). And the
OPEN ACCESS
Edited by: Ismail Hosen, University of Dhaka, Bangladesh
Reviewed by:
Alessandra Bettiol, University of Florence, Italy
Yanjie Zhao,
Qingdao University, China
*Correspondence: De-Ke Jiang
Jinlin Hou
+These authors have contributed equally to this work and share first authorship
Specialty section:
This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences Received: 11 April 2021 Accepted: 26 July 2021 Published: 19 August 2021
Citation:
Chen J, Mai H, Chen H, Zhou B, Hou J and Jiang D-K (2021) Pan-Cancer Analysis Identified C1ORF112 as a Potential Biomarker for Multiple Tumor Types.
Front. Mol. Biosci. 8:693651.
doi: 10.3389/fmolb.2021.693651
dysregulation of this pathway is linked to a higher risk of cancer (Nalepa and Clapp, 2018). Furthermore, C1ORF112 knockdown inhibited cell proliferation of Hela cells, implying C1ORF112 may have a role in cancer (van Dam et al., 2012). Moreover, according to previous studies, C1ORF112 was overexpressed in tumor tissues of stomach cancer and head and neck squamous cell carcinoma (Chen et al., 2020; Huang et al., 2020; Edogbanya et al., 2021). Besides, C1ORF112 expression was found to be higher in tumor samples with mutant TP53, a well-known tumor suppressor, in a study of bladder cancer progression (Sanchez-Carbayo et al., 2007). To the best of our knowledge, no comprehensive analysis of C1ORF112’s function and clinical importance at the pan-cancer level has been done.
In this study, we systematically analyzed the expression status, prognostic value, genetic alteration, and molecular function of C1ORF112 as well as the association with cancer-associated fibroblast infiltration in multiple tumor types.
MATERIALS AND METHODS
Gene Expression Analysis of C1ORF112
We constructed a C1ORF112 mRNA expression plot using the Human Protein Atlas (HPA) database (version: 20.1) (https:// www.proteinatlas.org/).
A
Consensus dataset
RNA tissue specificity: Tissue enhanced (lymphoid tissue)
NX
25
20
15
10
5
0
Cerebral cortex
Cerebellum
Olfactory region
hippocampal formation
Amygdala
Basal ganglia
Thalamus
Hypothalamus
Midbrain
Pons and medulla
Corpus callosum -
Spinal cord
Retina
Thyroid gland
Parathyroid gland -
Adrenal gland -
Pituitary gland -
Lung
Salivary gland
Esophagus
Tongue
Stomach
Duodenum
Small intestine
Colon
Rectum
Liver
Gallbladder
Pancreas
Kidney
Urinary bladder
Testis
Epididymis
Seminal vesicle
Prostate
Ductus deferens
Vagina
Ovary
Fallopian tube
Endometrium
Cervix, uterine
Placenta
Breast
Heart muscle
Smooth muscle
Skeletal muscle
Adipose tissue
Skin
Thymus
Appendix
Spleen
Lymph node
Tonsil
Bone marrow
Granulocytes
Monocytes
-cells
B-cells
NK-cells
Dendritic cells
Total PBMC
B
Scale
169,806,000 |
169,810,000 |
20 kb|
chri:
169,800,000 |
169,815,000 |
169,820,000 |
159.825 000
GENCODE V36 (4 items fihered)
169,830,000 |
169,835,000 |
hg38
169,840,000 |
169,845,000 |
169,850,000 |
SCYLS
4.88
100 vertebrates Basewise Conservation by PhyloP
SCYLS
Cons 100 Verts
0
45
Muliz Alignments of 100 Vertebrates
Mouse Dog
Chomen Zebrafish
M
I
C
D
Cancer Normal
C1ORF112 Expression Level (log2 TPM)
Analysis Type by Cancer
**
6
Bladder Cancer
1
Brain and CNS Cancer
1
Breast Cancer
4
4
Cervical Cancer
3
·
Colorectal Cancer
7
Esophageal Cancer
1
Gastric Cancer
2
Head and Neck Cancer
1
₪
Kidney Cancer
I
Leukemia
Liver Cancer
Lung Cancer
5
0
Lymphoma
2
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)
Melanoma
Myeloma
Other Cancer
4
4
Ovarian Cancer
1
Pancreatic Cancer
Prostate Cancer
Sarcoma
7
Significant Unique Analyses Total Unique Analyses
38
4
337
FIGURE 1 | C1ORF112 expression status in different tumors and normal tissues and C1ORF112 gene conservation among vertebrates. (A) Consensus C1ORF112 tissue expression based on datasets of HPA (Human Protein Atlas), GTEx, and FANTOM5 (function annotation of the mammalian genome). (B) C1ORF112 gene conservation analysis among vertebrates was visualized using the UCSC genome browser. (C) The expression status of C1ORF112 in different tumor types was visualized by TIMER2. * p < 0.05; ** p < 0.01; *** p < 0.001. (D) Oncomine pooling analysis of C1ORF112 expression in various tumor types.
A
log10(HR)
ENSG00000000460.16
☐
ACC BLCA
☐
☐ KIRP LAML
☐ ☐ ☐
☐ OV
☐ PAAD
☐
☐
0.5
(C1orf112)
0.0
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
LGG
LIHC
LUAD
LUSC
MESO
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
-0.5
B
ACC Overall Survival
C
KICH Overall Survival
D
KIRP Overall Survival
1.0
Low C1orf112 Group
1.0
1.0
High Group
Low C1orf112 Group
High C1orf112 Group
Logrank. p=0.0032
Logrank p=0.0025
0.8
HR(high)=3.2
HR(high)=2.6
p(HR)=0.0048
0.8
0.8
p(HR)=0.0036
Percent survival
n(high)=38
Percent survival
Percent survival
n(high)=141 n(low)=141
0.6
n(low)=38
0.6
0.6
0.4
0.4
0.4
Low C1orf112
High C1orf112 Group
0.2
0.2
Logrank p=0.014
HR(high)=8.6
0.2
p(HR)=0.042
0.0
0.0
n(high)=32
n(low)=32
0.0
0
50
100
150
0
50
100
150
0
50
100
150
200
Months
Months
Months
m
LGG Overall Survival
LIHC Overall Survival
1.0
F
G
LUAD Overall Survival
Low C1orf112 Group
1.0
High
Low
Logrank p=6.2e-08
High C1orf112 Group
1.0
Low C1orf112 Group
HR(high)=2.8
Logrank p=0.032
High
HR(high)=1.5
Logrank p=0.021
0.8
p(HR)=2e-07
0.8
0.8
HR(high)=1.4
Percent survival
n(high)=257 n(low)=257
p(HR)=0.033
p(HR)=0.022
Percent survival
n(high)=182
Percent survival
n(high)=239
0.6
0.6
n(low)=182
0.6
n(low)=239
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
200
0
20
40
60
80
100
120
0
50
100
150
200
250
Months
Months
Months
H
MESO Overall Survival
PAAD Overall Survival
SARC Overall Survival
1.0
Low C1orf112 Group
1.0
High C1orf112 Group
Low C1orf112 Group
a
1.0
Low C1orf112 Group
Logrank p=0.0015
High C1orf112 Group
Logrank p=0.04
High C1orf112 Group
HR(high)=2.2
Logrank p=0.014
0.8
p(HR)=0.0017
0.8
HR(high)=1.5
0.8
HR(high)=1.7
n(high)=41 n(low)=41
p(HR)=0.041
p(HR)=0.015
Percent survival
Percent survival
n(high)=89
Percent survival
n(high)=131
0.6
0.6
n(low)=89
0.6
n(low)=131
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
20
40
60
80
0
20
40
60
80
0
50
100
150
Months
Months
Months
FIGURE 2 | Correlation between C1ORF112 expression and overall survival in patients with different TCGA tumor types. GEPIA2 was used to build a survival map (A) and conduct overall survival analyses (B-J). The survival map and Kaplan-Meier plots with significant results are displayed. The 95% confidence intervals of overall survival are indicated by red and blue dotted lines for high and low C1ORF112 groups, respectively.
The “Gene DE” module of Tumor Immune Estimation Resource version 2 (TIMER2) (http://timer.cistrome.org/) was used to investigate C1ORF112 expression differences in tumor and non-tumor tissues in various tumor types. In this module, C1ORF112 expression was also evaluated between distinct breast cancer molecular subgroups, between HPV-positive and HPV-
negative head and neck squamous cell carcinoma (HNSC), and between primary and metastatic skin cutaneous melanoma (SKCM).
We searched C1ORF112 in the Oncomine database (Research Edition) (https://www.oncomine.org) to generate a pooled analysis of C1ORF112 expression.
A
log 10(HR)
0.4
ENSG00000000460.16
02
(C1orf112)
ACC BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG LIHC
0.0
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
-0.2
-0.4
B
ACC Disease Free Survival
KIRP Disease Free Survival
1.0
C
D
LGG Disease Free Survival
Low C1orf112 Group
1.0
High C1orf112 Group
Low C1orf112 Group
1.0
Low C1orf112 Group
High C1orf112 Group
Logrank p=0.0043
High C1orf112 Group
HR(high)=2.6
Logrank p=0.0066
Logrank p=1.4e-06
0.8
p(HR)=0.0059
0.8
HR(high)=2.2
p(HR)=0.0081
0.8
HR(high)=2.2
p(HR)=2.4e-06
Percent survival
n(high)=38
n(low)=38
Percent survival
n(high)=141
n(low)=141
Percent survival
n(high)=257
0.6
0.6
0.6
n(low)=257
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
0
50
100
150
200
0
50
100
150
Months
Months
Months
E
MESO Disease Free Survival
PRAD Disease Free Survival
SARC Disease Free Survival
1.0
F
G
Low C1orf112 Group
1.0
1.0
High C1orf112 Group
Low C1orf112 Group
Logrank p=0.036
High C1orf112 Group
Low C1orf112 Group
Logrank p=0.032
High C1orf112 Group
Logrank p=0.0054
0.8
HR(high)=1.8
p(HR)=0.04
0.8
HR(high)=1.6
0.8
HR(high)=1.7
p(HR)=0.0059
Percent survival
n(high)=41
n(low)=41
Percent survival
p(HR)=0.033 n(high)=246
Percent survival
n(high)=131
0.6
0.6
n(bw)=246
0.6
n(low)=131
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
20
40
60
80
0
50
100
150
0
50
100
150
H
Months
Months
Months
ACC pathological stage plot
I
LIHC pathological stage plot
J
PAAD pathological stage plot
3.5
F value = 3.63
Pr(>F) = 0.0169
F value = 3.96
3.5
F value = 2.73
5
Pr(>F) = 0.00852
Pr(>F) = 0.0453
3.0
3.0
៛
S’
2.5
2.0
3
2.0
1.5
2
40
1.0
1.0
0.5
-
o
T
T
T
T
T
T
T
T
0.5
T
T
T
T
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
FIGURE 3 | Correlation between C1ORF112 expression and disease-free survival in patients with different TCGA tumor types. GEPIA2 was used to build a survival map (A) and conduct disease-free survival (B-G) analyses. The survival map and Kaplan-Meier plots with significant results are displayed. The 95% confidence intervals of disease-free survival are indicated by red and blue dotted lines for high and low C1ORF112 groups, respectively. (H-J) Correlation between C1ORF112 expression and pathological stages of ACC, LIHC, and PAAD from TCGA datasets. Log2 (TPM + 1) was applied for log-scale.
Survival Prognosis Analysis
Overall survival (OS) and disease-free survival (DFS) Kaplan-Meier (K-M) plots, as well as a survival significance map of C1ORF112 in all TCGA tumor types, were generated using the Gene Expression Profiling Interactive Analysis version 2 (GEPIA2) (http://gepia2.cancer-pku.cn/) “Survival Analysis” module. In addition, UCSC Xena Browser (https://xenabrowser. net/) was used to perform progression-free survival (PFS)
analysis of C1ORF112 using TCGA Pan-Cancer datasets (version: 2018-09-13). The expression threshold was set at 50% for high C1ORF112 expression and low C1ORF112 expression.
Genetic Alteration Analysis
C1ORF112 genetic alterations were analyzed using cBioPortal (version: 3.6.20) (https://www.cbioportal.org/). Based on datasets
of TCGA Pan-Cancer Atlas Studies, we calculated the frequency of C1ORF112 gene mutation and copy number alteration in the “Cancer Types Summary” module. A mutation site plot of C1ORF112 was created using the “Mutations” module.
To analyze the correlation between C1ORF112 amplification status and liver hepatocellular carcinoma (LIHC) prognosis, the molecular profile was selected as copy number alterations based on “liver hepatocellular carcinoma (TCGA Pan-Cancer)” and the survival plot was generated by dividing cases based on the presence of copy number alterations (altered and unaltered groups).
To analyze the correlation between C1ORF112 mutation status and uterine corpus endometrial carcinoma (UCEC) prognosis, the molecular profile was selected as mutations based on “uterine corpus endometrial carcinoma (TCGA Pan-Cancer)” and the survival plot was generated by dividing cases into altered and unaltered groups.
Immune Cells Infiltration Analysis
Using the “Immune” module of Tumor Immune Estimation Resource version 2 (TIMER2) (http://timer.cistrome.org/), Extended Polydimensional Immunome Characterization (EPIC) and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were used to investigate the correlation between C1ORF112 expression and cancer-associated fibroblast infiltration.
C1ORF112-Related Gene Enrichment Analysis
The STRING tool (version: 11.0b) (https://string-db.org/) was used to create a Homo Sapiens C1ORF112 co-expression network using the following main parameters: 1) Active interaction sources: co-expression; 2) meaning of network edges: evidence; 3) maximum number of interactors: 50; and 4) minimum required interaction score: low confidence (0.150).
The GEPIA2 “Similar Gene Detection” module was used to extract 100 C1ORF112-correlated genes from the TCGA datasets that had the most similar expression pattern to C1ORF112. Gene Ontology pathway enrichment analysis was performed using the gene symbols of these 100 genes as input gene symbols in the “clusterProfiler” R package (version: 3.13). In addition, pairwise gene correlation analysis was performed using the GEPIA2 “Correlation Analysis” module.
C1ORF112-Protein Interaction Analysis
The “Network” module of BioGRID (version: 4.3) (https:// thebiogrid.org/) was used to create a C1ORF112-protein interaction network, with the layout set to “Concentric Circles.”
Conservation Analysis of C1ORF112
The UCSC genome browser (version: 2021 update) (http://www. genome.ucsc.edu/cgi-bin/hgTracks) was used to visualize gene conversation of C1ORF112 among vertebrates.
RESULTS
Gene Expression Analysis of C1ORF112
Based on datasets of the HPA, GTEx, and FANTOM5 (function annotation of the mammalian genome), we found that C1ORF112
was highly expressed in lymphoid tissue, such as thymus and bone, and enriched in testis and the parathyroid gland (Figure 1A; Supplementary Figure S1). Moreover, based on single-cell RNA-seq, high expression of C1ORF112 was also observed in spermatocytes and spermatogonia (Figure 1A; Supplementary Figure S1). C1ORF112 expression has a low tissue specificity, according to these findings. We also found that C1ORF112 is relatively conserved among vertebrates (Figure 1B).
The expression pattern of C1ORF112 in tumor tissues was then investigated. Similar to its distribution in normal tissue, C1ORF112 mRNA distribution also showed low tumor specificity. C1ORF112 mRNA expression was increased in various tumor tissues when compared to corresponding normal tissue (Figure 1C). Tumor tissues of bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), HNSC, kidney renal papillary cell carcinoma (KIRP), LIHC, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), UCEC (all p < 0.001), cervical squamous cell carcinoma, and endocervical adenocarcinoma (CESC) (p < 0.01) had significantly higher C1ORF112 expression when compared to corresponding normal tissue (Figure 1C). Significantly higher C1ORF112 expression was observed in HPV-positive head and neck squamous cell tumor tissues compared with HPV-negative tissues (p < 0.001) (Figure 1C). Furthermore, when compared to primary SKCM tumor tissues, C1ORF112 expression was also significantly elevated in metastatic SKCM tissues (p < 0.001) (Figure 1C). Meanwhile, significantly decreased C1ORF112 expression was observed in kidney chromophobe (KICH) tumor tissues (p < 0.001) (Figure 1C).
We next used the Oncomine database to validate the differential C1ORF112 expression between tumor and normal tissues. As shown in Figure 1D, significantly elevated C1ORF112 expression was observed in most cancer types, including bladder cancer, brain and central nervous system cancer, breast cancer, cervical cancer, colorectal cancer, esophageal cancer, gastric cancer, head and neck cancer, lung cancer, lymphoma, ovarian cancer, and sarcoma.
Together these findings suggested that C1ORF112 may promote carcinogenesis in a variety of tumor types, and its clinical significance requires further investigation.
The Association Between C1ORF112 Expression and Prognosis of Patients With Cancer
To explore the potential prognostic value of C1ORF112 based on TCGA datasets, we investigated the correlation between C1ORF112 expression and prognosis of patients with different tumors by using GEPIA2. Higher C1ORF112 expression was associated with shorter OS in cases of adrenocortical carcinoma (ACC) (p = 3.2 × 10-3), KICH (p = 1.4 × 10-2), KIRP (p = 2.5 x 10-3), LGG (p = 6.2 × 10-8), LIHC (p = 3.2 x 10-2), LUAD (p = 2.1 × 10-2), mesothelioma (MESO) (p=1.5 x 10-3), pancreatic adenocarcinoma (PAAD) (p = 4.0 × 10-2), and sarcoma (SARC) (p = 1.4 × 10-2) (Figure 2). Furthermore, DFS
A
Mutation
Fusion
Amplification
Deep Deletion
Multiple Alterations
10%
Alteration Frequency
8%
6%
4%
2%
Mutation data
+
+
+
+
+
+
+
*
+
+
*
+
+
x
*
*
×
x
*
×
x
×
×
×
×
×
+
CNA data
+
+
+
*
*
x
X
*
x
*
x
×
×
x
+ + + + + + +
x
x
x
x
x
x
CHOL
LIHC
BLCA
UCEC
BRCA
SARC
DLBC
LUAD
LUSC
STAD
SKCM
CESC
UCS
ESCA
PAAD
PCPG
COAD
Meso
OV
THYM
HNSC
KIRP
PRAD
KIRC
THCA
LGG
GBM
B
F252Lfs*51/A253Cfs*9/R250H and 2 more
C1ORF112 Mutations
8
0
DUF4487
0
200
400
600
853aa
C
D
100%
100%
90%
Logrank Test P-Value: 0.0173
90%
Ampilification group
Logrank Test P-Value: 0.0322
80%
80%
Amplification group
Progression Free
Unaltered group
Disease Free
Unaltered group
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
0
10
20
30
40
50
60
70
80
90
100
110
120
09
Q
10
20
30
40
50
60
70
80
90
100
110
120
LIHC Progress Free Survival (Months)
LIHC Disease Free (Months)
E
F
G
100%
100%
100%
90%
90%
90%
80%
80%
80%
Progression Free
70%
70%
70%
Overall
ogoods-eseesK]
60%
60%
60%-
50%
50%
50%
40%
40%
Mutation group
40%
30%
Logrank Test P-Value: 0.0461
30%
Unaltered group
30%
Logrank Test P-Value:0.0044
Mutation group
Unaltered group
20%
Mutation group
20%
20%
Unaltered group
Logrank Test P-Value: 0.0248
10%
10%
10%
0%
0
10
20
30
40
50
60
70
80
90
100
110
120
09
0
10
20
30
40
50
60
70
80
90
100
110
120
09
0
10
20
30
40
50
60
70
80
90
100
110
120
UCEC Overall Survival (Months)
UCEC Progress Free Survival (Months)
UCEC Disease Specific Survival (Months)
FIGURE 4 | C1ORF112 genetic alteration in various tumor types of TCGA. The alteration frequency with C1ORF112 genetic alteration type (A) and C1ORF112 mutation site (B) were generated by cBioPortal. The correlations between C1ORF112 amplification status and progression-free survival and disease-free survival of LIHC (C,D) were analyzed by cBioPortal. The correlations between mutation status and overall survival, progression-free survival, and disease-specific survival of UCEC (E-G) were analyzed by cBioPortal.
analysis showed that high C1ORF112 expression was a marker for poor outcome for patients with ACC (p = 4.3 × 10-3), KIRP (p = 6.6 × 10-3), LGG (p=1.4×10-6), MESO (p=3.6x10-2), PRAD (p=3.2 × 10-2), and SARC (p = 1.4 × 10-2) (Figures 3A-G). Using UCSC Xena Browser, a significant association was also noted between C1ORF112 expression and PFS in several human cancers. In patients of ACC (p = 9.9 × 10-4), CHOL (p= 4.1 × 10-2), KICH (p=2.6 × 10-2), KIRP (p=3.0×10-5), LGG (p=7.9×10-8), LUAD (p = 7.7 × 10-3), MESO (p= 9.4 × 10-4), PRAD (p=1.5× 10-2), SARC (p=4.3×10-2), SKCM (p=1.9x10-2), and UCEC (p=5.5x 10-4), higher C1ORF112 expression is associated with a worse prognosis (Supplementary Figure S2). These results indicated that increased C1ORF112 expression was associated with poor prognosis in a variety of tumor types.
Moreover, we also investigated the correlation between C1ORF112 expression and pathological stages of tumors by GEPIA2. The high expression of C1ORF112 was significantly correlated with the advanced stage of ACC, LIHC, and PAAD (Figures 3H-J).
The Genetic Alteration Landscape of C1ORF112 in Different Tumors
The genetic alteration of C1ORF112 in various tumor types in TCGA datasets was then investigated using cBioPortal. We found that CHOL tumor samples had the highest C1ORF112 genetic alteration frequency (>10%). All of the genetic alterations occurring in CHOL tumor samples were copy number amplification (Figure 4A; Supplementary Table S1), which was the major type of genetic alteration in all TCGA tumor samples. In addition to CHOL cancer, more than 8% of LIHC, BRCA, and UCEC samples showed genetic alteration of C1ORF112 (Figure 4A; Supplementary Table S1). As shown in Figure 4B, a total of 137 C1ORF112 mutations, including 111 missense mutations, 21 truncating mutations, 4 fusion mutations, and 1 in-frame mutation, were detected in TCGA tumor samples (Supplementary Table S2). The residues 250-253 of the protein encoded by C1ORF112 had nine mutations, making it the most frequently mutated region in the C1ORF112 protein (Figure 4B).
Following that, we explored the link between C1ORF112 genetic alterations and clinical outcomes of cancer patients. C1ORF112 amplification was associated with poor prognosis in LIHC patients in terms of PFS (p = 1.7 x 10-2) and DFS (p = 3.2 × 10-2) (Figures 4C,D; Supplementary Table S3). In addition, UCEC patients with C1ORF112 mutations (6.05%; 32 cases) showed a better prognosis in terms of OS (p = 4.6 x 10-2), PFS (p = 4.4×10-3), and DFS (p = 2.5x 10-2) (Figures 4E-G; Supplementary Table S4).
Cancer-Associated Fibroblast Infiltration Analysis
Previous studies have found that cancer-associated fibroblasts in the stroma are involved in the regulation of different tumor- infiltrating immune cells (Chen and Song, 2019). We therefore employed the EPIC and TIDE algorithms to investigate the
correlation between cancer-associated fibroblast infiltration and C1ORF112 expression in different malignancies. C1ORF112 expression was positively correlated with cancer- associated fibroblast infiltration in ACC, CESC, KIRC, KIRP, LGG, MESO, and thyroid carcinoma (THCA) (Figure 5).
C1ORF112-Related Gene Enrichment Analysis
To investigate the functional mechanism of C1ORF112 in carcinogenesis, we used GEPIA2 to extract the top 100 genes with expression patterns similar to C1ORF112 from all tumor types in the TCGA datasets (Supplementary Table S5). Gene Ontology enrichment analysis indicated that these genes were closely linked to cell cycle or mitosis regulation (Figure 6A). Following that, 50 genes co-expressed with C1ORF112 were obtained by the STRING tool to validate the result of Gene Ontology enrichment analysis. As shown in Figure 6B, the correlations of these 50 genes were mutually close; also, the genes were also enriched in cell cycle and mitotic regulation (Supplementary Table S6). These findings prompted us to wonder whether C1ORF112 plays a role in these biological processes by interacting with key proteins involved in cell cycle and mitotic regulation. According to the BioGRID4.3 database, C1ORF112 physically interacts with RAD51, DMC1, and FIGNL1 (Figure 6C), which have well-characterized functions in the cell cycle, mitotic regulation, and tumorigenesis (Supplementary Figure S3) (Yuan and Chen, 2013; Laurini et al., 2020). Moreover, C1ORF112 expression is strongly correlated with expression levels of RAD51 and FIGNL1 (Figures 6D,E). Based on these results, we speculate that C1ORF112 may play a tumor-promoting role in cancers by driving the cell cycle and facilitating cell division.
DISCUSSION
TCGA project has profiled 33 prevalent tumor types with multi-omics data and provided an unprecedented opportunity to discover molecular aberrations at the pan- cancer level (Malta et al., 2018; Way et al., 2018; Schaub et al., 2018; Weinstein et al., 2013). Thanks to the development of bioinformatics algorithms and databases (Cerami et al., 2012; Carithers and Moore, 2013; Tang et al., 2019), numerous studies have been undertaken in recent years to identify pan-cancer molecular biomarkers and their functional roles (Ye et al., 2021; Lv et al., 2021; Hong et al., 2020). In this study, we analyzed the prognostic value and oncogenic role of C1ORF112 in a variety of tumor types.
C1ORF112 is located at chromosome 1q24.2 and its gene synonym is FLJ10706. C1ORF112 encodes nine transcripts, five of which are protein-coding (Howe et al., 2021). The C1ORF112 protein with the longest amino acid sequence is translated from two distinct transcripts, ENST00000286031 and ENST00000359326 (Howe et al., 2021). According to the Ensembl database, C1ORF112 orthologues have been found in 194 species. The protein-coding sequence of C1ORF112 has a relatively high level of conservation among vertebrates, and a
EPIC
TIDE
Cor
1
☒ p > 0.05
ACC (n=79)
☐
☐
0
☒ p < 0.05
BLCA (n=408)
☒
☐ ☐ ☐
-1
BRCA (n=1100)
☒
C1ORF112 Expression Level (log2 TPM)
Purity
Cancer associated fibroblast_TIDE
Purity
Cancer-associated Fibroblasts immnue infiltration
C1ORF112 Expression Level (log2 TPM)
Cancer associated fibroblast_TIDE
BRCA-Basal (n=191)
☐
Cor = 0.13
Cor = 0.337
Cor = 0.096
Cor = 0.132
·
p = 2.696-01
.
p = 3.55e-03
5
p = 1.11e-01
p = 2.836-02
BRCA-Her2 (n=82)
BRCA-LumA (n=568) ☐
☒
☒
3
4
CESC
2
0
BRCA-LumB (n=219)
☒ ☒
I
CESC (n=306)
☐
☐
3
1-
CHOL (n=36)
☒ ☒
2
COAD (n=458)
0
0.2
0.4
0.6
0.8
1.0
-0.1
0.0
0.1
0.2
0.25
0.50
0.75
1.00
-0.2
0.0
0.2
Infiltration Level
0.4
DLBC (n=48)
☒ ☒
Purity
Infiltration Level
Purity
ESCA (n=185)
☒ ☐
C1ORF112 Expression Level (log2 TPM)
Purity
Cancer associated fibroblast_TIDE
C1ORF112 Expression Level (log2 TPM)
Purity
Cancer associated fibroblast_TIDE
Cor = - 0.191
p = 3.67e-05
Cor = 0.184
GBM (n=153)
☒ ☒
4.
p = 7.18e-05
Cor = 0.033
p = 6.02c-01
Cor = 0.421
p = 1.666-12
HNSC (n=522)
☐ ☐
3
3
HNSC-HPV- (n=422)
☐
☒
KIRC
IRP
2
×
HNSC-HPV+ (n=98)
☒ ☐
A
KICH (n=66)
☒ ☐
1 .
1
.
KIRC (n=533)
☐ ☐
KIRP (n=290)
☐ ☐
0.25
0.50
0.75
1.00
-0.2
-0.1
0.0
0.1
0.2
0.3
0.25
0.50
0.75
1.00
-0.1
0.0
0.1
0.2
Purity
Infiltration Level
Purity
Infiltration Level
LGG (n=516)
☐ ☐ ☒
LIHC (n=371)
☐
☐
C1ORF112 Expression Level (log2 TPM)
Purity
Cancer associated fibroblast_TIDE
C1ORF112 Expression Level (log2 TPM)
Purity
Cancer associated fibroblast_TIDE
LUAD (n=515)
Cor - 0.011
p = 8.07e-01
Cor = 0.115
Cor = 0
Cor = 0.3
☐ ☐ ☐ ☐ ☒
p = 1.226-02
LUSC (n=501)
4 -
4
p = 9.97c-01
p = 5.250-03
MESO (n=87)
3
OV (n=303) ☒
GG
MESO
Coca
PAAD (n=179)
☒ ☐
2.
2
PCPG (n=181)
☐ ☐
1
PRAD (n=498)
☐ ☐
1
☐ ☒
0.25
0.50
0.75
1.00
-0.1
0.0
0.1
0.2
0.25
0.50
0.75
1.00-0.2
0.0
0.2
READ (n=166)
Purity
Infiltration Level
Purity
Infiltration Level
SARC (n=260)
☐ ☐
SKCM (n=471)
☐ ☐
C1ORF112 Expression Level (log2 TPM)
Purity
Cancer associated fibroblast_TIDE
C1ORF112 Expression Level (log2 TPM)
2.5
Purity
Cancer associated fibroblast_TIDE
Cor = 0.006
Cor = 0.269
SKCM-Metastasis (n=368)
☐
p = 9.35e-01
p =4.38e-04
.
Cor = - 0.044
p -3.36e=01
Cor = 0.336
p = 2.290-14
SKCM-Primary (n=103)
☐ ☒
2.0
2
STAD (n=415)
☒ ☐
PCPG
THCA
1.5
TGCT (n=150)
☐ ☐
1.0
THCA (n=509)
☐ ☐
THYM (n=120) ☐ ☐
0.5
UCEC (n=545)
☒
☐
0.25
0,50
0.75
1.000.2
-0.1
0.0
0.1
0.2
0.00
0.25
0.50
0.75
1.00
-0.2
0.0
0.2
Purity
Infiltration Level
Purity
Infiltration Level
UCS (n=57)
☐ ☒
UVM (n=80) ☐ ☒
previous study revealed that C1ORF112 might have evolved from the ancestors of eukaryotes (Edogbanya et al., 2021). Mice lacking BC055324 (the mouse ortholog of C1ORF112) are embryonic fatal, and heterozygous mice have lower bone mineral density, circulating glucose level, and cardiac output (https://www. mousephenotype.org/data/genes/MGI:3590554). In the last decade, C1ORF112 has drawn attention for its potential role in tumorigenesis. Van Dam et al. found that C1ORF112 knockdown in HeLa cancer cells significantly lowered cell growth rate (van Dam et al., 2012). A recent study revealed that C1ORF112 was co- expressed with stem cell-related genes, and these genes had elevated expression in gastric cancer tissues (Huang et al.,
2020). Another study identified C1ORF112 as one of the genes in a nine-gene risk model for predicting prognosis in patients with gastric cancer (Chen et al., 2020). However, the significance of C1ORF112 in various tumor types has not been comprehensively explored. Therefore, we systemically characterized C1ORF112 in 33 TCGA tumor types by analyzing features such as gene expression, genetic alteration, and immune infiltration.
In the present study, we found that C1ORF112 is widely expressed in a variety of tissues and C1ORF112 expression is upregulated in the majority of tumors. We further explored the relationship between C1ORF112 overexpression and clinical
A
B
C
EONRB
organelle fission.
SPAG5
CDKN3
KIF23
FBXO5
CHORFUT
SIGLECLI
nuclear division.
PORYE
OIPS
KIF.11
ASPM
VIPR2
CHRM3
DTL
PTHIR
CHRMA
mitotic nuclear division.
NCAPG
KIAA0101
RACGAP
GINS2
A
TPX2
KIF15
C1orf112
CSORF30
POCOS
Count
chromosome segregation.
PBK
AURKA
CDC6
20
W
CENPU
KIF20A
CSAR2
TSSCA
DENNO4C
25
SPATASLI
nuclear chromosome segregation.
30
DLGAPS
PRIM1
CCNB1
FIGNL1
35
CDCAB
CDK1
40
mitotic sister chromatid segregation.
NDC80
KIF2C
RFC4
TOP2A
CHEK1
HLA-B
PTGERS
p.adjust
3.5821380-36
sister chromatid segregation.
CCNB2
NUF2
ORF112
NEK2
CDC45
DMC1
1.9656404-18
DNA replication.
CENPN
MAD2L1
CENPF
CCNA
ALDHA2
RAD51
AGTRI
3931680e-18
HMMR
UBE2T
5.0975198-10
~
A
MELK
FANCI
EPHAI
TNFRSFIA
P4HAS
GLC1541
7.863350e-18
cell cycle checkpoint.
COCA5
KNTC1
BUE
TTK
microtubule cytoskeleton organization involved in mitosis.
EXO1
RADS1AP1
CEP55
TMEM263
FCGRT
RAD51 CKS1B
2
0.20
0.25
0.30
0.35
0.40
OPALIN
CCRD
GeneRatio
6
SPINT2
CD83
CLECIA
UNC9381
OPRIM1
APP
D
E
7
p < 0.001
p < 0.001
R2 = 0.64
6
R2 = 0.54
6
log2(RAD51 TPM)
5
log2(FIGNL1 TPM)
5
”
៛
(
00
2
~
-
1
0
0
0
1
2
3
4
5
6
0
1
2
3
4
5
6
log2(C1orf112 TPM)
log2(C1orf112 TPM)
parameters or prognosis. Survival analysis revealed that C1ORF112 overexpression was associated with poor OS, DFS, and PFS. High C1ORF112 expression was associated with poor prognosis in different tumor types involving ACC, CHOL, KIHC, KIRP, LGG, LUAD, MESO, PAAD, and SARC. Furthermore, upregulated C1ORF112 expression is also significantly associated with the advanced cancer stage suggesting malignant progression. Growing evidence indicates that genomic mutations involved tumor progression and chemotherapy response (Yang et al., 2011; Cha et al., 2021). For example, Yang et al. reported that BRCA1 and BRCA2 mutations are significantly associated with patient survival, which may be a result of distinct response to platinum- based treatment (Yang et al., 2011). A large-scale study identified that mutations in four genes (ESR1, CDH1, RICTOR, and TP53) tended to occur in specific metastatic sites, which could be biomarkers or therapeutic targets of metastatic breast cancer patients (Cha et al., 2021). In this study, we revealed that mutations of C1ORF112 were most common in CHOL (>10%), followed by LIHC, BRCA, and UCEC. To analyze whether C1ORF112 genetic alterations have an impact on clinical outcomes of cancer patients, we discovered that C1ORF112 amplification could be a risk factor for patients with liver cancer, while C1ORF112 mutation may be protective
in UCEC patients. Collectively, these findings indicate that C1ORF112 acts as an oncogene in the progression of a variety of cancers and is a promising predictor for practical application in cancer prognosis.
Immune cells extensively intertwine with cancer cells and exert an essential effect on cancer migration and metastasis in various tumor types (Angelova et al., 2015). Recent studies have also reported that tumor immune microenvironment was associated with the expression level of various genes (Ju et al., 2020a; Ju et al, 2020b). In this study, we found C1ORF112 expression was positively correlated with CAFs infiltration in several tumor types. CAFs are prominent components of stromal cells and have been reported to be associated with worse prognosis, chemotherapy resistance, and disease recurrence in various cancers (Calon et al., 2015; Ryner et al., 2015; Liu et al., 2016; Fiori et al., 2019). Taken together, our work elucidates the underlying effect of C1ORF112 in tumor immunity and its prognostic values for multiple cancers.
Using STRING and GEPIA2, we identified a number of genes that were co-expressed with C1ORF112 across different tumors and other tissues. Gene enrichment analysis revealed that these genes were strongly correlated with cell cycle or mitosis regulation, which was consistent with previous studies (van Dam et al., 2012;
Edogbanya et al., 2021). Moreover, our results showed that C1ORF112 physically interacts with RAD51, FIGNL1, and DMC1. And the expressions of RAD51 and FIGNL1 were strongly correlated with C1ORF112 expression. RAD51, FIGNL1, and DMC1 are well-characterized genes that encode proteins involved in DNA repair and cell cycle regulation (Yuan and Chen, 2013; Laurini et al., 2020). These findings validated the results of our gene enrichment analysis and paved the way for further exploration of C1ORF112 molecular function.
This study had several limitations as well. First, the sample sizes for some uncommon tumor types were relatively small, which may cause batch effects or inaccurate results. Second, this study only provides preliminary findings linking C1ORF112 to cancer progression in various tumors, and more experimental work is needed to determine the precise molecular function of C1ORF112 in tumorigenesis.
In conclusion, C1ORF112 is widely overexpressed in diverse cancers and its expression and genetic alteration are statistically associated with clinical outcomes in patients with certain tumors. Furthermore, immune infiltration analysis and C1ORF112- related gene enrichment analysis offer potential mechanisms by which C1ORF112 may regulate tumor immunity, cell cycle, or DNA repair in cancers. Hence, further experimental and clinical studies are warranted to investigate CIORF112’s practical application in cancer therapy and prognosis prediction.
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/ Supplementary Material.
ETHICS STATEMENT
Ethical review and approval were not required for the study on human participants in accordance with the local legislation
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and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
AUTHOR CONTRIBUTIONS
JC and HM performed the data analysis and prepared the draft of this manuscript. HC and BZ revised the draft. JH and D-KJ designed the research and supervised all the work. All authors contributed to the article and approved the submitted version.
FUNDING
This study was supported by the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (No. 2017BT01S131), the General Program from the Natural Science Foundation of Guangdong Province (No. 2019A1515011423), the General Programs from the National Natural Science Foundation of China (Nos. 81472618 and 81670535), the National Science and Technology Major Project (Nos. 2017ZX10202202 and 2018ZX10301202), the Key-Area Research and Development Program of Guangdong Province (No. 2019B020227004), the Innovative Research Team Project of Guangxi Province (No. 2017GXNSFGA198002), the Dean Fund of Nanfang Hospital, Southern Medical University (No. 2018Z005), the Grant for Recruited Talents to Start Scientific Research from Nanfang Hospital, and the Outstanding Youth Development Scheme of Nanfang Hospital, Southern Medical University (No. 2017J001).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2021.693651/ full#supplementary-material
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