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. 2024 Nov 18;14(1):28491.
doi: 10.1038/s41598-024-78729-0.

Construction of molecular subtype and prognostic model for gastric cancer based on nucleus-encoded mitochondrial genes

Xu Wang #  1 , Sainan Li #  2 , Yuhuan Shen #  2 , Li Cao  1 , Yajuan Lu  2 , Jinghao Cao  2 , Yingchao Liu  2 , Aoli Deng  2 , Jiyun Yang  3 , Tongtong Wang  4
Affiliations

Construction of molecular subtype and prognostic model for gastric cancer based on nucleus-encoded mitochondrial genes

Xu Wang et al. Sci Rep. .

Abstract

Gastric cancer (GC) is a common digestive system cancer, characterized by a significant mortality rate. Mitochondria is an indispensable organelle in eukaryotic cells. It was previously revealed that a series of nucleus-encoded mitochondrial genes (NMG) mutations and dysfunctions potentially contribute to the initiation and progression of GC. However, the correlation between NMG mutations and survival outcomes for GC patients is still unclear. In this study, NMG expression profile and clinical information in GC samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Through consistent clustering and functional enrichment analysis, we have identified three NMG clusters and three gene clusters that are associated with patterns of immune cell infiltration. Prognostic genes were identified through Univariate Cox regression analysis. The principal component analysis was conducted to set up a scoring system. Subsequently, the Single‑cell RNA sequencing (scRNA-seq) data of GC patients and cancer cell drug sensitivity data were retrieved from the GEO database. Patients with high NMG scores exhibited increased microsatellite instability status and a heightened tumor mutation rate compared to those with low NMG scores. Survival analysis revealed that GC samples with high NMG scores could achieve a better prognosis. Additionally, These patients were observed to be more responsive to immunotherapy. Moreover, we delved into prognostic genes at the level of single cells, revealing that MRPL4 and MRPL37 exhibit high expression in epithelial cells, while TPM1 demonstrates high expression in tissue stem cells. Utilizing cancer cell drug sensitivity data from the Drug Sensitivity in Cancer (GDSC) database, we noted a heightened sensitivity to chemotherapy in the high NMG group. Furthermore, we discovered a significant enrichment of cuproptosis-related genes in clusters with high NMG scores. Consequently, employing the scoring system could facilitate the prediction of GC patients' sensitivity to cuproptosis-induced therapy. Our study confirmed the potency of this scoring system as a therapeutic response biomarker for gastric cancer, potentially informing clinical treatment strategies.

Keywords: Gastric cancer; Immunotherapy; Nucleus-encoded mitochondrial genes; Tumor microenvironment; Tumor mutational burden.

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Conflict of interest statement

Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Genetic changes of NMGs in gastric cancer from online databases. (A) The heatmap shows 113 differentially expressed NMGs between gastric cancer tissues and paired non-tumor tissues from gastric cancer patients in the TCGA cohort. (B) The mutation frequency of 19 prognostic NMGs in the TCGA cohort. (C) The CNV alteration frequency of NMGs. The ordinate represents the mutation frequency of NMGs. Red signifies an increase in copy number, while green denotes a decrease. (D) The location of CNV alteration of NMGs on 23 chromosomes using TCGA cohort. (E) The comparison of expression levels for the 19 prognostic NMGs in tumor versus normal tissues within the TCGA cohort. (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, p-value < 0.05 was regarded as statistically significant). TCGA, The Cancer Genome Atlas; CNV, copy number variation.
Fig. 2
Fig. 2
NMG clusters and the clinicopathological and biological characteristics analysis. (A) Interaction networks diagram shows NMGs regulate the prognosis. The pink or blue lines indicate positive and negative correlations, respectively. (B) Consensus clustering matrix with k = 3 and their corresponding area. (C) The Cumulative distribution function (CDF) curve illustrates the consistency score across varying values of k (k = 2–9). (D) Kaplan–Meier curves of patients in the TCGA cohort based on NMG clusters. (E) Prognostic NMG expression levels and clinicopathologic characteristics among the three distinct NMG clusters. CDF, cumulative distribution function.
Fig. 3
Fig. 3
GSVA for KEGG pathways analysis between clusters. (A) NMG cluster A vs. cluster B. (B) NMG clusters A vs. clusters C. (C) NMG clusters B vs. clusters C. GSVA, gene set variation analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Fig. 4
Fig. 4
Immune infiltration levels and functional enrichment analysis in three NMG clusters. (A) Three clusters were compared for differences in the abundance of immune infiltrating cells. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns, non-significant, and p-values less than 0.05 were regarded as statistically significant. (B) Principal component analysis scatter plot of the transcriptome. (C) Venn diagram depicts shared and unique DEGs in the indicated clusters. (D, E) Enrichment analyses of overlapping DEGs among three clusters were conducted for both GO and KEGG pathways. The length of the barplots represents the number of enriched genes, and the color depth of the barplots represents the significance of the enrichment. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Fig. 5
Fig. 5
Phenotypic characteristics of the NMG gene subtypes. (A) The correlation of the three gene clusters with clinicopathologic characteristics was visualized by heatmap. (B) Kaplan–Meier overall survival curves for patients in three gene clusters showed significant survival differences. (log-rank tests, p-value < 0.001). (C). Expression levels of prognostic DEGs across distinct gene clusters. (D) The Sankey diagram illustrates the associations between patients grouped by NMG clusters, gene clusters, NMG scores, and different prognosis statuses. (E) Kaplan-Meier survival curves from gastric cancer patients with low and high NMG scores (log-rank tests, p-value < 0.001). (F) Correlation between immune cell infiltration level and NMG score. Blue denotes a negative correlation, while red indicates a positive correlation. * indicates statistical significance. The difference in NMG score among different NMG clusters (G), and different gene clusters (H).
Fig. 6
Fig. 6
The association between the NMG score and TMB. (A) Correlations between NMG score and TMB. (B) TMB in high- and low-score groups. (C) Subgroup survival analysis of NMG score combined with TMB. (log-rank tests, p-value < 0.001). (D–E) Waterfall plots display somatic mutation characteristics in cohorts stratified by high and low NMG scores. The upper or right bar plot represents the variants and proportion of different mutation types, respectively. TMB, tumor mutational burden.
Fig. 7
Fig. 7
The capacity of NMG score in predicting immunotherapy response. (A) The MSI status evaluation in the subgroups with high and low NMG scores. (B) The difference in NMG scores among three different MSI groups. (C,D) The expression levels of PD-L1 and PD-1 in the high-NMG-score and low-NMG-score subgroups. p-value < 0.05 was considered statistically significant.
Fig. 8
Fig. 8
Prognostic ability of the NMG score. (A,B) Relationship between NMG score and survival status of patients. (C,D) Kaplan-Meier survival curves were generated for gastric cancer patients, grouped by NMG score and stratified by T-stage.
Fig. 9
Fig. 9
Investigating the NMG risk model at the single-cell level. (A) A t-SNE plot showing cell clusters. (B) The expression levels of prognostic genes among different clusters.
Fig. 10
Fig. 10
Box plots represent the estimated drug sensitivity of the top 10 most significant compounds: (A) 5-Fluorouracil, (B) Afatinib, (C) AGI-6780, (D) Crizotinib, (E) Cytarabine, (F) Dabrafenib, (G) Dactinomycin, (H) Dihydrorotenone, (I) Docetaxel, and (J) Gefitinib.
Fig. 11
Fig. 11
The correlation of the cuproptosis with NMG. Variations in the expression of cuproptosis-related genes across the three NMG clusters (A), and gene cluster (B). (The * indicates p-value < 0.05, ** indicates p-value < 0.01, *** indicates p-value < 0.001, with p-value < 0.05 considered statistically significant).
Fig. 12
Fig. 12
Immunohistochemical staining for MRPL4 in gastric cancer tissues and paired adjacent normal tissues.
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