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. 2022 Jan 21:8:792779.
doi: 10.3389/fmolb.2021.792779. eCollection 2021.

Integrative Genomic Analyses of 1,145 Patient Samples Reveal New Biomarkers in Esophageal Squamous Cell Carcinoma

Binbin Zou  1   2 , Dinghe Guo  1   2 , Pengzhou Kong  1   2 , Yanqiang Wang  1   2 , Xiaolong Cheng  1   2 , Yongping Cui  1   2   3
Affiliations

Integrative Genomic Analyses of 1,145 Patient Samples Reveal New Biomarkers in Esophageal Squamous Cell Carcinoma

Binbin Zou et al. Front Mol Biosci. .

Abstract

Due to the lack of effective diagnostic markers and therapeutic targets, esophageal squamous cell carcinoma (ESCC) shows a poor 5 years survival rate of less than 30%. To explore the potential therapeutic targets of ESCC, we integrated and reanalyzed the mutation data of WGS (whole genome sequencing) or WES (whole exome sequencing) from a total of 1,145 samples in 7 large ESCC cohorts, including 270 ESCC gene expression data. Two new mutation signatures and 20 driver genes were identified in our study. Among them, AP3S1, MUC16, and RPS15 were reported for the first time. We also discovered that the KMT2D was associated with the multiple clinical characteristics of ESCC, and KMT2D knockdown cells showed enhanced cell migration and cell invasion. Furthermore, a few neoantigens were shared between ESCC patients. For ESCC, compared to TMB, neoantigen might be treated as a better immunotherapy biomarker. Our research expands the understanding of ESCC mutations and helps the identification of ESCC biomarkers, especially for immunotherapy biomarkers.

Keywords: KMT2D; bioinformactics; esophageal squamous cell carcinoma; immunotherapy; mutation; neoantigen.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Analysis pipeline and mutation signature of whole genome region in ESCC. (A) Analysis pipeline, (B) SBS signature of WGS, (C) DBS signature of WGS, (D) ID signature of WGS.
FIGURE 2
FIGURE 2
Mutation signature of coding region in ESCC. (A) SBS signatures of WES, (B) DBS signatures of WES, (C) ID signatures of WES, (D) WGS-DBS-S1 survival analysis, (E) WES signature correlation with clinical characteristics, (F) WES-SBS-S6 survival analysis, (G) WES-ID-S1 survival analysis. In the survival analysis, red line represents the ESCC patients enrichment in the signature, the green line represents ESCC patients without enrichment in the signature.
FIGURE 3
FIGURE 3
TMB in ESCC. (A) the correlation between aTMB and fTMB, (B) compared the mean value between aTMB and fTMB, (C) Survival analysis of aTMB (high aTMB >10), (D) Survival analysis of fTMB (high fTMB >10), (E) Survival analysis of aTMB (high aTMB >8.0), (F) Survival analysis of fTMB (high fTMB >5.7).
FIGURE 4
FIGURE 4
Neoantigen in ESCC. (A) The correlation bewtten TNB and TNS, (B) Compared the mean value between TNB and fTMB, (C) Survival analysis of TNS (high TNS >2.2), (D) Survival analysis of TNB (high TNB>2.5), (E) Survival analysis of fTMB (high fTMB >1.6), (F) Survival analysis aTMB (high aTMB >1.8).
FIGURE 5
FIGURE 5
SMGs in ESCC. (A) SMGs of ESCC in previous studies, (B) SMGs of ESCC identified by 4 software, (C) The distribution of SMGs of ESCC identified by 4 software in previous studies, (D) Mutation profile of SMGs in ESCC, (E) MUC16 mutation in ESCC, (F) AP3S1 mutation in ESCC, (G) RPS15 mutation in ESCC, (H) The correlation between SMGs of ESCC and clinical characteristics, (I) The correlation between NOTCH1 mutation and lymph node metastasis of ESCC, (J) The correlation between NOTCH1 mutation and different age group of ESCC patients (young and old), (K) The correlation between NOTCH1 mutation and lymph node metastasis of young ESCC patients (age<60), (L) The correlation between NOTCH1 and lymph node metastasis of old ESCC patients (age >= 60).
FIGURE 6
FIGURE 6
Gene mutation with lymph node metastasis. (A) the frequency of lymph node metastasis in ESCC, (B) Survival analysis of lymph node metastasis in ESCC, (C) smoking related with lymph node metastasis in ESCC, (D) volcano plot of the correlation gene mutation with lymph node metastasis, x-axis was the log2 (the ratio of mutated rate of lymph node metastasis group and the mutated rate of without lymph node metastasis group), and y-axis was P (the correlation gene mutation with lymph node metastasis), (E) KEGG enrichment pathway of the higher mutation gene of lymph node metastasis compared with no lymph node metastasis, (F) KEGG enrichment pathway of the lower mutation gene of lymph node metastasis compared with no lymph node metastasis.
FIGURE 7
FIGURE 7
KMT2D mutation, KMT2D expression and the correlation between KMT2D mutation and clinical characteristics. (A) KMT2D mutation in ESCC, (B) KMT2D mutation in TCGA, (C) mutation and CNV of KMT2D in TCGA cancer, (D) the expression of KMT2D in tumor and normal of ESCC in the GSE53625, (E) expression of KMT2D in TCGA cancer, (F) the correlation of gene mutation with clinical characteristics.
FIGURE 8
FIGURE 8
The effect of KMT2D in KYSE150 cell line. (A) The mRNA expression of KMT2D in normal esophageal epithelial cell lines and ESCC cell lines detected by q-RTPCR, (B) The knockdown efficiency of KMT2D in KYSE150 cell line was verified by q-RTPCR, (C) KMT2D knockdown promoted the ability of colony formation in KYSE150 cell line compared to the control, (D) KMT2D knockdown promoted the ability of proliferation in KYSE150 cell line compared to the control, (E) KMT2D knockdown promoted the ability of migration in KYSE150 cell line compared to the control, (F) KMT2D knockdown promoted the ability of migration and invasion in KYSE150 cell line compared to the control. All data are presented as the mean ± standard deviation of the three in-dependent experiments.
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