Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery

Abstract

Gastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.

ACAN, MDFI, and CHST1 as Candidate Genes in Gastric Cancer: A Comprehensive Insilco Analysis

“…Comparison between normal stomach tissue and stomach tumors showed the leading five genes that were overexpressed in stomach cancer. According to their analysis, ACAN gene showed significant upregulation in all stages of cancer (Vizeacoumar et al, 2021). Similar research was performed to investigate the interaction of miRNAs and target genes in Stomach adenocarcinoma (STAD).…”

EarlyDetect: Development of tool for early detection of stomach adenocarcinoma based on blood-lipid profile as clinicopathological feature

“…Co-expression network analysis processed pDEGs for identification of gene-signature. Details of procedure can be accessed from doi.org/10.1038/s41598-021-87037-w [36]. Gene-wise similarity correlation matrix (scor) & dis-similarity correlation matrix (dcor) presented co-expression of pDEGs.…”

Multimerin-1 and cancer: a review

“…Saini et al [ 122 ] report detection of MMRN1 in the saliva of epithelial ovarian cancer patients but comment that the route by which MMRN1 enters the saliva remains unclear. Vizecoumar et al [ 177 ] identified MMRN1 as a downregulated gene whose protein product is detectable in the plasma in gastro-oesophageal cancer patients. Using mass spectrometry techniques, changes in MMRN1 protein levels were detected in the sera of multiple myeloma patients [ 178 ] and hepatocellular carcinoma patients [ 116 ] and in cell lysates from bone marrow aspirates from patients with amyloid leukaemia [ 179 ].…”