Abstract
Aberrant immune gene expression has been shown to have close correlations with the occurrence and progression of esophageal cancer (EC). We aimed to generate a prognostic signature based on immune-related genes (IRGs) capable of predicting prognosis, immune checkpoint gene (ICG) expressions, and half-inhibitory concentration (IC50) for chemotherapy agents for EC patients. Transcriptome, clinical, and mutation data on tumorous and paratumorous tissues from EC patients were collected from The Cancer Genome Atlas (TCGA) database. Then, we performed differential analysis to identify IRGs differentially expressed in EC. Their biofunctions and related pathways were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. These gene expression profiling data were merged with survival information and subjected to univariate Cox regression to select prognostic genes, which were then included in a Lasso-Cox model for signature generation (risk score calculation). Patients were divided into the high- and low-risk groups using the median risk score as a cutoff. The accuracy of the signature in overall survival prediction was assessed, so were its performances in predicting ICG expressions and IC50 for chemotherapy and targeted therapy agents and immune cell landscape characterization. Fifteen prognostic IRGs were identified, seven of which were optimal for risk score calculation. As expected, high-risk patients had worse overall survival than low-risk individuals. Significant differences were found in tumor staging, immune cell infiltration degree, frequency of tumor mutations, tumor mutation burden (TMB), and immune checkpoint gene expressions between high- vs. low-risk patients. Further, high-risk patients exhibited high predicted IC50 for paclitaxel, cisplatin, doxorubicin, and erlotinib compared to low-risk patients. The seven-IRG-based signature can independently and accurately predict overall survival and tumor progression, characterize the tumor immune microenvironment (TIME) and estimate ICG expressions and IC50 for antitumor therapies. It shows the potential of guiding personalized treatment for EC patients.
Highlights
esophageal cancer (EC) is the seventh most common cancer and the sixth leading cause of death from cancer across the world [1]
RNA-seq, clinical, and tumor mutation data on 171 tissue samples (160 tumorous and 11 paratumorous tissues) from EC patients were collected from the Cancer Genome Atlas-Genomic Data Commons (TCGA-GDC; https://portal.gdc.cancer.gov/), among which incomplete clinical data were improved by the University of California Santa Cruz (UCSC) the Cancer Genome Atlas (TCGA) browser
As for mechanisms for differentially expressed IRGs (DEIs) in EC, biological process (BP) terms were mostly enriched in leukocyte chemotaxis, migration, regulation of chemotaxis, and chemokine-mediated signaling pathways
Summary
EC is the seventh most common cancer and the sixth leading cause of death from cancer across the world [1]. Current treatment strategies can vary among stages or cancer types, with endoscopic resection optimal to most early tumors, surgical resection followed by chemotherapy, radiotherapy, or targeted therapy (single or combined) beneficial to locally advanced tumors, but non-surgical treatment, systemic chemotherapy, suitable for metastatic cases [2]. The prognosis remains constant in EC patients undergoing antitumor treatment, though diverse and being improved [2]. Immunotherapy, immune checkpoint inhibitors (ICIs), has received much attention over the last few years. It has been proven effective in and is recommended for the management of malignant melanoma, non-small cell lung cancer, and clear cell renal cell carcinoma, with significantly improved patient prognosis [4]. Biomarkers for systemic prediction of prognosis and immunotherapy efficacy for EC patients are urgently needed [7]
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