食管癌中一种新的线粒体相关基因特征：预后、免疫和治疗特征

Esophageal carcinoma (ESCA) is a common and lethal malignant tumor worldwide. The mitochondrial biomarkers were useful in finding significant prognostic gene modules associated with ESCA owing to the role of mitochondria in tumorigenesis and progression. In the present work, we obtained the transcriptome expression profiles and corresponding clinical information of ESCA from The Cancer Genome Atlas (TCGA) database. Differential expressed genes (DEGs) were overlapped with 2030 mitochondria-related genes to get mitochondria-related DEGs. The univariate cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and multivariate cox regression were 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 the risk score, ESCA patients were divided into high- and low-risk groups. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed to further investigate the difference between low- and high-risk groups at the gene pathway level.

CIBERSORT was used to evaluate immune cell infiltration. The mutation difference between high- and low-risk groups was compared by using the R package "Maftools". Cellminer was used to assess the association between the risk scoring model and drug sensitivity. As the most important outcome of the study, a 6-gene risk scoring model (APOOL, HIGD1A, MAOB, BCAP31, SLC44A2, and CHPT1) was constructed from 306 mitochondria-related DEGs.

Pathways including the "hippo signaling pathway" and "cell-cell junction" were enriched in the DEGs between high and low groups. According to CIBERSORT, samples with high-risk scores demonstrated a higher abundance of CD4 + T cells, NK cells, M0 and M2 macrophages, and a lower abundance of M1 macrophages. The immune cell marker genes were correlated with the risk score. In mutation analysis, the mutation rate of TP53 was significantly different between the high- and low-risk groups. Drugs with a strong correlation with the risk model 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.