Open Access

ARTICLE

Mitochondrial autophagy gene signature predicts prognosis and response to immunity in esophageal cancer

DAIXIN ZHAO¹, QINGYU WANG², JIANBO WANG^1,*

1 Qilu Hospital, Shandong University, Jinan, 250012, China
2 Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China

* Corresponding Author: JIANBO WANG. Email:

BIOCELL 2024, 48(2), 271-281. https://doi.org/10.32604/biocell.2023.029094

Received 01 February 2023; Accepted 24 May 2023; Issue published 23 February 2024

Abstract

Background: Esophageal cancer (ESCA) is a common digestive tract tumor. As a result, optimization of the early diagnosis of ESCA and identifying the contributing prognostic genes is urgently required. Herein, the prognosis of mitochondrial autophagy-related genes was analyzed in different subtypes of ESCA, and prognostic models were constructed to identify the immune cell infiltration with significant differences between subtypes. Methods: The Cancer Genome Atlas database was searched to download 185 ESCA samples, covering gene expression level data and clinical follow-up data, and 179 samples from the Gene Expression Omnibus database for subsequent validation analysis. The consensus Cluster Plus analysis method was employed to identify the best mitochondrial autophagy subtype. Kaplan-Meier curve was used to evaluate the correlation of survival prognosis between different subtype groups and actual survival prognostic information. A chi-square test was performed to analyze the correlation between subtypes and clinical information. Differential genetic analysis between different subtypes was performed using Limma packs (threshold setting: adj. p < 0.05&|log2FC|>1). Univariate Cox regression analysis was applied to identifying genes with significant prognosis, and the LASSO algorithm screened out key genes. The risk scores were constructed by Stepwise Cox regression analysis and divided into high and low-risk groups. Independent prognostic factors were determined using the univariate and nomograms constructed by multivariate Cox analysis. The CIBERSORT method was used to calculate the composition ratio of 22 immune cells; the matrix and immune scores of tumor samples were calculated by the ESTIMATE algorithm. Wilcoxon’s test was performed to compare the expression differences of immune checkpoint genes and human leukocyte antigen family genes between high- and low-risk groups and the difference in IC50 between these risk groups of 138 chemotherapy drugs. Relationships between mitochondrial autophagy subtypes and high- and low-risk groups were assessed using the ggalluvial package in R3.6.1. Results: Seven mitochondrial autophagy genes associated with the of ESCA were identified (PTPN4, ALKBH4, IL6, FN3KRP, HSDL1, B3GNT2, CCT4). High and low risk were significantly correlated with the actual prognosis. Nomograms constructed by factors stage and Risk group showed significant relation with patient prognosis. Eleven immune cells significantly differing in the two subtypes were identified, followed by ten significantly different immune checkpoint genes. Conclusion: Seven mitochondrial autophagy genes associated with the prognosis of ESCA may serve as the key prognostic genes and novel therapeutic targets for esophageal cancer.

---

Keywords

---