A Novel Mitochondria-Related Gene Signature in Esophageal Carcinoma: Prognostic, Immune, and Therapeutic Features

Abstract

Esophageal Carcinoma (ESCA) is a common and lethal malignant tumor worldwide. A role for mitochondria in tumorigenesis and progression has been proposed. The mitochondrial biomarkers were useful in finding significant prognostic gene modules associated with ESCA. In the present work, we obtained the transcriptome expression profiles and corresponding clinical information of ESCA from The Cancer Genome Atlas (TCGA). Differential expressed genes (DEGs) were overlapped with mitochondria related genes to obtain mitochondria related DEGs. The univariate cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and multivariate cox regression was sequentially used to define the risk scoring model for mitochondria-related DEGs, and its prognostic value was verified in the external datasets GSE53624. Based on risk score, ESCA patients were divided into high and low risk groups. GO, KEGG and Gene Set Enrichment Analysis (GSEA) were performed to further investigate the difference between low and high risk groups in the gene pathway level. CIBERSORT was used to evaluate immune cell infiltration. The mutation difference between high and low risk groups was compared by the R package “Maftools”. Cellminer was used to assess the interactions of the risk scoring model and drug sensitivity. As the most important outcome of the study, we obtained 306 mitochondria related DEGs, and constructed a 6-gene risk scoring model (APOOL, HIGD1A, MAOB, BCAP31, SLC44A2 and CHPT1). Between high and low risk group, pathways including “hippo signaling pathway” and “cell-cell junction” was enriched. According to CIBERSORT, samples with high risk demonstrated higher abundance of CD4+ T cells, NK cells, M0 and M2 Macrophages, and lower abundance of M1 Macrophages. The immune cell marker genes were correlated with risk score. In mutation analysis, the mutation rate of TP53 was significantly different between the high and low risk groups. Drugs with strong correlation with model genes and risk score were selected. In conclusion, we focused on the role of mitochondria-related genes in cancer development, and proposed a prognostic signature for individualized integrative assessment.

Construction of a five-disulfidptosis-related-lncRNA signature for predicting prognosis and immune activity in kidney renal clear cell carcinoma

“…Stepwise dimensionality reduction was performed sequentially through lasso and Cox regression analyses to reduce the number of feature parameters and construct a prognostic model. This method has been reported in several studies [52,53]. Five DRlncRNAs signi cantly associated with the prognosis of KIRC, namely, SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3 and AC108673.3, were identi ed and used to construct a prognostic model.…”