The role of mitochondrial energy metabolism in drug resistance and prognosis of lung adenocarcinoma: a multi-omics and machine learning strategy for predictive and personalized therapy
The MDrisk signature derived from mitochondrial energy metabolism and drug resistance may be useful for distinguishing prognosis, immune contexture, and computationally inferred drug susceptibility in LUAD.
Abstract
Background As the leading histological form of lung cancer, lung adenocarcinoma (LUAD) displays considerable intratumoral heterogeneity, frequent therapeutic resistance, and an unfavorable clinical outcome. Although rewiring of mitochondrial energy metabolism is known to drive tumor progression and treatment failure, a comprehensive understanding of its dual role in LUAD drug resistance and prognosis has yet to be established. Here, we built a robust predictive signature that integrates mitochondrial metabolism with drug resistance through multi-omics integration and machine learning frameworks. Methods We explored single-cell RNA sequencing profiles together with TCGA-LUAD transcriptomic data. Weighted gene co-expression network analysis (WGCNA) was applied to extract gene modules linked to mitochondrial-related genes (MRGs) and drug resistance-related genes (DRGs). From these, a five-gene (KLF4, KLF10, CAT, ALDOA, HLA-DRA) prognostic classifier, designated MDrisk, was formulated using LASSO-Cox regression and 101 combinations of 10 machine learning algorithms. Results The MDrisk model demonstrated reliable and precise prognostic capacity across training, internal test, and external GEO cohorts, serving as an independent risk factor. Elevated MDrisk scores correlated with an immunosuppressive microenvironment, higher tumor mutational burden, distinct copy-number alteration profiles, and decreased drug sensitivity in computational predictions. In vitro experiments further validated that silencing ALDOA—a central component of the signature—suppressed the proliferation, migration, and invasive capacity of LUAD cells. Conclusion The MDrisk signature derived from mitochondrial energy metabolism and drug resistance may be useful for distinguishing prognosis, immune contexture, and computationally inferred drug susceptibility in LUAD. It may offer a tool for further exploration of individualized therapy and sheds light on the interplay between metabolic dysregulation and antitumor immunity.
Background Lung adenocarcinoma (LUAD), which is the leading subtype of non-small cell lung cancer (NSCLC), poses considerable difficulties in accurate prognostic assessment and targeted therapeutic options. Cell proliferation-related genes (CPGs) and mitochondrial biogenesis-related genes (MBGs) play critical roles in tumor metabolic reprogramming; however, their prognostic value and molecular mechanisms in LUAD are poorly understood. This study aims to construct a CPG/MBG-based prognostic risk model for LUAD, evaluate its clinical utility in predicting prognosis and immunotherapy response, and experimentally validate the functional role of key model genes in LUAD progression. Methods By utilizing The Cancer Genome Atlas (TCGA)-LUAD and GSE72094 datasets, this investigation formulated a risk scoring model through differential expression screening combined with least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. The molecular characteristics and clinical implications of the risk model were investigated via immune microenvironment evaluation, genomic alteration analysis, and drug sensitivity prediction. The functional contributions of key genes were further substantiated using quantitative reverse transcription polymerase chain reaction (qRT-PCR), commercial assay kits, the JC-1 fluorescent probe, the Cell Counting Kit-8 (CCK-8), Transwell invasion assays, and wound healing assays. Results A risk model based on seven CPGs and MBGs (PLK1, HMMR, CYP27A1, LDHA, NPAS2, KRT17, CIDEC) showed reliable predictive performance in both GSE72094 and the TCGA-LUAD cohorts. Enhanced tumor heterogeneity and an immunosuppressive microenvironment were observed in the high-risk group. Drug sensitivity analysis indicated that the risk model could guide personalized treatment strategies; for instance, high-risk patients showed increased susceptibility to agents such as docetaxel and 5-fluorouracil. In vitro experiments demonstrated that the key gene CIDEC exhibited upregulated expression in LUAD tissues and cells. Knockdown of CIDEC led to enhanced cellular energy metabolism and increased mitochondrial membrane potential, while also effectively suppressing cell invasion, proliferation, and migration. Conclusions The established MBGs/CPGs prognostic model provides a novel tool for stratified treatment planning in LUAD, underscoring the crucial roles of cellular proliferation and mitochondrial biogenesis in tumor progression. Functional validation of CIDEC offers experimental support for the development of potential therapeutic strategies.
This signature stratified patients by prognosis in two geographically distinct external cohorts and generated testable metabolic and immune hypotheses.
Q. Ma, Jian-Qing Liang, Jin-tian Li et al.· Genes· 0 citations
Background Lung adenocarcinoma (LUAD) remains a leading cause of cancer mortality, with metabolic reprogramming, particularly dysregulated mitochondrial transport and glycolysis (MG), emerging as a hallmark of its progression. Despite the recognized importance of these pathways, an integrated, clinically actionable model that combines them for patient stratification and therapeutic targeting is lacking. Objective This study aims to decode integrated MG-associated molecular patterns in the progression of LUAD, providing novel clinical actionable target for LUAD patients. Methods GSVA and WGCNA were applied to an integrated GEO cohort to identify MG-associated genes. LASSO-Cox and multivariable Cox analyses were used to construct and validate a prognostic model and prioritize CCNA2 as a prognostic candidate. Single-cell, spatial, cell-based, drug-sensitivity, and xenograft analyses were used for validation. Results The MG-associated signature stratified overall survival, and CCNA2 was enriched in malignant cells. CCNA2 knockdown reduced A549 proliferation, wound closure, and invasion. GSCA analysis identified BHG712, IPA-3, and KIN001-260 as candidate compounds, and IPA-3 produced the largest reduction in xenograft tumor burden. Conclusion The integrated analyses identify an MG-associated prognostic framework and prioritize CCNA2 for further validation in LUAD.
Yi-Qing Gu, Min-Hua Shi· Frontiers in Genetics· 0 citations
Pancreatic ductal adenocarcinoma (PDAC) is characterized by marked molecular, cellular, and clinical heterogeneity. Chaperone-mediated autophagy (CMA) supports adaptation to metabolic and environmental stress, but its cell type-specific distribution and prognostic relevance in PDAC remain unclear.
Single-cell RNA sequencing data from GSE212966 were analyzed to characterize CMA-related transcriptional states in PDAC and adjacent non-tumor tissues. Bulk transcriptomic data from TCGA-PAAD were used for differential expression analysis, weighted gene co-expression network analysis, and molecular model development, while ICGC PACA-CA and PACA-AU served as independent validation cohorts. Multiple survival machine-learning approaches were compared to establish a CMA-related prognostic model. Hallmark pathway activity, immune infiltration, and predicted drug sensitivity were evaluated between risk groups. KRT19, the highest-weighted model gene, was selected for
in vitro
validation. In parallel, an independent single-center cohort of 468 patients was analyzed using eight survival machine-learning methods to identify clinical prognostic factors and construct a nomogram.
CMA-related transcriptional activity varied among cell types, with macrophages showing prominent scores and PDAC-derived macrophages exhibiting higher CMA scores than those from adjacent tissues. Integration of TCGA differential expression analysis and WGCNA identified 105 candidate genes. The StepCox [forward] plus random survival forest model showed favorable overall performance, with C-index values of 0.903, 0.678, and 0.733 in the TCGA, PACA-CA, and PACA-AU cohorts, respectively. High molecular risk was associated with enhanced glycolytic, proliferative, and cell cycle-related signaling, increased M0 macrophages, reduced CD8
+
T cells, and differential predicted drug sensitivity. KRT19 overexpression promoted PDAC cell proliferation, colony formation, migration, and invasion. In the single-center cohort, N stage, CA125, vascular tumor thrombus, and total bilirubin ranked highest in weighted prognostic importance. The clinical nomogram achieved AUC values of 0.661 and 0.750 for 1- and 3-year overall survival, respectively.
This study identified CMA-related cellular heterogeneity, established a molecular prognostic model that retained prognostic discrimination in two independent validation cohorts, demonstrated the functional relevance of KRT19, and developed an independent clinical prediction tool. These molecular and clinical models provide complementary perspectives on PDAC prognosis and warrant further evaluation in matched prospective cohorts.
Qing-Yan Kou, Sheng-Qian Qiao, Zhen-Yuan Liu et al.· Frontiers in Cell and Develo...· 0 citations
The clinical relevance of mitochondrial RNA modification (MRM) in colorectal cancer (CRC), particularly its value for prognostic stratification, has not been fully defined. We integrated bulk transcriptomic profiles, machine learning-based model screening, single-cell analysis, and experimental validation to identify MRM score-associated prognostic genes and construct a CRC risk model. Seven CRC-related prognostic genes were selected: SPARCL1, MGP, PRELP, PALMD, TNS1, ARHGEF25, and PTGIS. These genes were incorporated into a risk signature with favorable prognostic performance, as supported by nomogram-based assessment. Gene Set Enrichment Analysis indicated that cytokine-related processes may participate in CRC progression. TNS1 showed the strongest positive association with natural killer cells (cor = 0.784, P < 0.05) and the strongest inverse association with type 17 T helper cells (cor = -0.279, P < 0.05). Database-based screening predicted 113 candidate compounds targeting CRC. Single-cell analysis further highlighted smooth muscle cells, epithelial cells, endothelial cells, T cells, and B cells as major cellular populations of interest. SPARCL1, MGP, PRELP, and PALMD increased during both early and late B cell maturation, whereas TNS1 and ARHGEF25 were highly expressed across broader cellular contexts. In conclusion, SPARCL1, MGP, PRELP, PALMD, TNS1, ARHGEF25, and PTGIS may represent MRM score-associated prognostic markers in CRC and require further study.
Qingfang Yue, Hongxia Wen, Zeyu Zhang et al.· Computational biology and ch...· 0 citations
Aberrant expression of mitochondrial quality regulation genes (MQRGs) is intricately linked to mitochondrial dysfunction and the progression of hepatocellular carcinoma (HCC), highlighting the urgent need for reliable prognostic biomarkers. In this study, we aimed to identify differentially expressed MQRGs from 20 candidate genes by analyzing transcriptomic profiles and clinical records from the TCGA (n = 371) and GEO (n = 167) datasets. The identified prognostic MQRGs, along with associated subtype differentially expressed genes (DEGs, n = 156), underwent LASSO and multivariate Cox regression analyses to construct a risk model, which was subsequently validated through time‐dependent ROC analysis, Kaplan‐Meier curves, and in vitro RT‐qPCR. Our findings established a robust 4‐MQRG signature comprising ANXA10, BAMBI, AKR1B15, and SPINK1, which revealed that patients classified as high risk had significantly shorter overall survival compared to their low‐risk counterparts (p < 0.001). The predictive accuracy of this signature was noteworthy, yielding 1‐, 3‐, and 5‐year AUCs of 0.725, 0.696, and 0.747 in the training cohort (n = 243) and 0.676, 0.627, and 0.592 in the testing cohort (n = 242), respectively. Furthermore, high‐risk scores were associated with distinct immunosuppressive tumor microenvironments and varying sensitivity to systemic therapies. The dysregulated expression of the four genes was corroborated by RT‐qPCR and analysis of the HPA database. In conclusion, this validated MQRG‐based prognostic signature serves as an accurate tool for survival prediction and risk stratification, thus offering valuable biomarker support for personalized therapeutic approaches in HCC.
Bin-Bin Li, Li-Jun Zeng, Zhi-Long He et al.· Drug development research (P...· 0 citations
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