Background The incidence and mortality of gastric cancer ranks fifth and fourth worldwide among all malignancies, respectively. Accumulating evidences have revealed the close relationship between mitochondrial dysfunction and the initiation and progression of stomach cancer. However, rare prognostic models for mitochondrial-related gene risk have been built up in stomach cancer. Methods In current study, the expression and prognostic value of mitochondrial-related genes in stomach adenocarcinoma (STAD) patients were systematically analyzed to establish a mitochondrial-related risk model based on available TCGA and GEO databases. The tumor microenvironment (TME), immune cell infiltration, tumor mutation burden, and drug sensitivity of gastric adenocarcinoma patients were also investigated using R language, GraphPad Prism 8 and online databases. Results We established a mitochondrial-related risk prognostic model including NOX4, ALDH3A2, FKBP10 and MAOA and validated its predictive power. This risk model indicated that the immune cell infiltration in high-risk group was significantly different from that in the low-risk group. Besides, the risk score was closely related to TME signature genes and immune checkpoint molecules, suggesting that the immunosuppressive tumor microenvironment might lead to poor prognosis in high-risk groups. Moreover, TIDE analysis demonstrated that combined analysis of risk score and immune score, or stromal score, or microsatellite status could more effectively predict the benefit of immunotherapy in STAD patients with different stratifications. Finally, rapamycin, PD-0325901 and dasatinib were found to be more effective for patients in the high-risk group, whereas AZD7762, CEP-701 and methotrexate were predicted to be more effective for patients in the low-risk group. Conclusions Our results suggest that the mitochondrial-related risk model could be a reliable prognostic biomarker for personalized treatment of STAD patients.

Background: Mitochondrial calcium uniporter (MCU) complex has been reported to be associated with the tumor occurrence and development in varieties of malignancies. However, the role of MCU complex in colon adenocarcinoma (COAD) remains unclear. Therefore, we constructed a risk score signature based on the MCU complex members to predict the prognosis and response to immunotherapy for patients with COAD. Methods: The MCU complex-associated risk signature (MCUrisk) was constructed based on the expressions of MCU, MCUb, MCUR1, SMDT1, MICU1, MICU2, and MICU3 in COAD. The immune score, stromal score, tumor purity and estimate score were calculated by the ESTIMATE algorithm. We systematically evaluated the relationship among the MCUrisk, mutation signature, immune cell infiltration, and immune checkpoint molecules. The response to immunotherapy was quantified by the Tumor Immune Dysfunction and Exclusion (TIDE). Results: Our results showed that high score of MCUrisk was a worse factor for overall survival (OS) in COAD, and MCUrisk score was significantly higher in advanced COAD. The mutation landscape was different between the MCUrisk-high and MCUrisk-low groups, and the mutation rate of TP53 was remarkably higher in MCUrisk-high group, which strongly suggested TP53 mutation might be associated with mitochondrial calcium dyshomeostasis in COAD. Furthermore, MCUrisk score was negatively correlated with tumor mutation burden (TMB), and combining risk score and TMB as a novel index was better than TMB alone in predicting the prognosis for COAD patients. The compositions of Tregs and M0/M2 macrophages were significantly increased in MCUrisk-high group, whereas CD4 + T cells was significantly decreased in MCUrisk-high group. Consistently, the immune score was lower in MCUrisk-high group. The expression levels of immune checkpoint molecules were negatively correlated with the MCUrisk score, including CD58 and CD226. Furthermore, a lower MCUrisk score indicated better response to immunotherapy, and combining risk score and immune score was a novel indicator to precisely predict the response to immuotherapy for COAD patients. Conclusion: Altogether, a novel MCUrisk signature was constructed based on the mitochondrial calcium uptake-associated genes, and a lower MCUrisk score may predict better OS outcome and better response to immunotherapy in COAD.

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.

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 nding signi cant prognostic gene modules associated with ESCA. In the present work, we obtained the transcriptome expression pro les 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 de ne the risk scoring model for mitochondria-related DEGs, and its prognostic value was veri ed 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 in ltration. 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 signi cantly 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.