Mitochondria-related chemoradiotherapy resistance genes-based machine learning model associated with immune cell infiltration on the prognosis of esophageal cancer and its value in pan-cancer

Ziyu Liu; Zahra Zeinalzadeh; Tao Huang; Yingying Han; Lushan Peng; Dan Wang; Zongjiang Zhou; Diabate Ousmane; Junpu Wang

doi:10.1016/j.tranon.2024.101896

Mitochondria-related chemoradiotherapy resistance genes-based machine learning model associated with immune cell infiltration on the prognosis of esophageal cancer and its value in pan-cancer

Transl Oncol. 2024 Apr:42:101896. doi: 10.1016/j.tranon.2024.101896. Epub 2024 Feb 6.

Authors

Ziyu Liu¹, Zahra Zeinalzadeh², Tao Huang², Yingying Han², Lushan Peng², Dan Wang², Zongjiang Zhou², Diabate Ousmane², Junpu Wang³

Affiliations

¹ Department of Pathology, Xiang-ya Hospital, Central South University, Changsha City, Hunan Province, China; Department of Pathology, School of Basic Medicine, Central South University, Changsha City, Hunan Province, China; Ultrapathology (Biomedical Electron Microscopy) Center, Department of Pathology, Xiang-ya Hospital, Central South University, Changsha City, Hunan Province, China; Key Laboratory of Hunan Province in Neurodegenerative Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
² Department of Pathology, Xiang-ya Hospital, Central South University, Changsha City, Hunan Province, China; Department of Pathology, School of Basic Medicine, Central South University, Changsha City, Hunan Province, China.
³ Department of Pathology, Xiang-ya Hospital, Central South University, Changsha City, Hunan Province, China; Department of Pathology, School of Basic Medicine, Central South University, Changsha City, Hunan Province, China; Ultrapathology (Biomedical Electron Microscopy) Center, Department of Pathology, Xiang-ya Hospital, Central South University, Changsha City, Hunan Province, China; Key Laboratory of Hunan Province in Neurodegenerative Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China. Electronic address: wang-jp2013@csu.edu.cn.

Abstract

Esophageal cancer, known for its high incidence and low five-year survival rate, poses significant treatment challenges. A key aspect of this challenge is the close link between mitochondria and resistance to chemoradiotherapy (CRT). Currently, there is a scarcity of biomarkers for predicting CRT response and prognosis in esophageal cancer. Our study addresses this gap by developing a prognostic model that incorporates mitochondria-related CRT resistance (MRCRTR) genes, including CTSL, TBL1X, CLN8, MMP1, PDPN, and MRPL37. Survival analysis using Kaplan-Meier curves reveals that patients with high MRCRTR scores have lower survival rates than those with low scores. Utilizing a nomogram, we successfully predict the one-, two-, and three-year overall survival rates for esophageal cancer patients. Cox regression analysis confirms the MRCRTR score as an independent prognostic factor. Furthermore, our single-cell and correlation analyses suggested that MRCRTR genes might influence CRT resistance by modulating the immune microenvironment and impacting angiogenesis. Our pan-cancer analysis also indicates the potential applicability of MRCRTR scores to head and neck squamous cell carcinoma. The validation of these findings, conducted with samples from Xiang-ya Hospital, aligns closely with our bioinformatics results. Our study not only explores the role of MRCRTR genes in predicting the prognosis of esophageal cancer but also enhances the understanding of the interplay between CRT, mitochondria, and patient outcomes.

Keywords: CTSL; Chemoradiotherapy resistance; Esophageal Cancer; Machine Learning Models; Mitochondria-related genes.