It is suggested that glycolytic activity is closely associated with both drug resistance and clinical prognosis in LUAD, and the proposed integrative model holds promise for enhancing precision treatment decision-making through optimized risk assessment and rational selection of chemotherapeutic regimens.
Abstract
Background Drug resistance and poor clinical outcomes in lung adenocarcinoma (LUAD) necessitate robust biomarkers for personalized therapy. Glycolysis reprogramming is a hallmark of cancer, but its clinical utility remains incompletely defined. Methods We integrated TCGA and GEO transcriptomic data with Weighted gene co−expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression to construct a glycolysis−related prognostic signature. A nomogram combining the risk score with clinicopathological factors was developed. Drug sensitivity was predicted using the pRRophetic algorithm. qRT−PCR and xenograft models using A549 and cisplatin−resistant A549/DDP cells validated the expression of candidate genes. Results Patients stratified by glycolysis-related risk scores exhibited significantly distinct survival outcomes, and the glycolysis-based signature functioned as an independent prognostic factor for overall survival in LUAD. The nomogram demonstrated robust predictive performance and effectively estimated patient sensitivity to three commonly used conventional chemotherapeutic agents. In vitro and in vivo studies using A549 cells and their cisplatin-resistant derivative A549/DDP revealed aberrant expression of VIPR1, ADRB2, RXFP1, PDGFB, WNT3A, and SPRY1 in the resistant cell line and in corresponding xenograft tumor tissues. These findings suggest that glycolytic activity is closely associated with both drug resistance and clinical prognosis in LUAD. Conclusions This study identifies a glycolysis-related gene signature with demonstrable utility for prognostic stratification and therapeutic response prediction in LUAD. The proposed integrative model holds promise for enhancing precision treatment decision-making through optimized risk assessment and rational selection of chemotherapeutic regimens.
Pancreatic cancer (PC) is a fatal malignancy, with glycolysis and T cells playing crucial roles in its pathogenesis. This study explored prognosis-related genes in glycolysis and T cells in PC using bioinformatics methods.
We first quantified distinct T-cell subsets via immune infiltration analysis of the TCGA-PAAD cohort and extracted T cell-related genes (T-RGs). Differentially expressed genes (DEGs) were obtained from GSE28735 and TCGA-PAAD. Intersecting these DEGs with T-RGs and glycolysis-related genes (G-RGs) yielded candidate genes. We screened prognostic genes using univariate Cox and LASSO regression, built a prognostic model in TCGA-PAAD and validated it in GSE57495. A nomogram was constructed for survival prediction, its reliability verified by calibration and ROC curves. We compared immune microenvironment, pathway enrichment, mutation landscape and drug sensitivity between high- and low-risk subgroups, and built lncRNA-miRNA-mRNA regulatory networks.
In vitro
and
in vivo
mouse tumorigenesis assays explored GPR87’s potential involvement in glycolytic activity and CD8
+
T cell infiltration. Seahorse analysis, glucose uptake detection and immunohistochemistry further explored roles in glucose metabolism and anti-tumor immunity.
MET
,
KDELR3
,
AK4
, and
GPR87
were determined as prognostic genes. In the training set, this exploratory prognostic model showed moderate predictive performance, with area under the curve values of 0.72, 0.71, and 0.71 at 1, 2, and 3 years, respectively. The risk score and N-stage were identified as independent prognosis predictors, and the developed nomogram demonstrated moderate predictive performance. Functional pathways revealed enrichment in 44 pathways. There were 17 differential immune cells. In addition, risk scores were correlated with 28 immune checkpoints. The regulatory network comprised 26 miRNAs and 50 lncRNAs. Furthermore, computationally predicted correlations between prognostic genes and 60 drugs were identified in different risk groups.
In vitro
and
in vivo
experiments demonstrated that
GPR87
knockdown inhibits the proliferation, migration, and clonogenic ability of PC cells while promoting their apoptosis. Furthermore,
GPR87
expression was negatively correlated with CD8
+
T cell, and
GPR87
knockdown inhibited glycolysis in pancreatic cancer cells.
MET
,
KDELR3
,
AK4
, and
GPR87
were identified as candidate prognostic genes in PC, providing preliminary hypothesis-generating insights that require validation in independent institutional or prospective clinical cohorts before any clinical application.
Unknown authors· Frontiers in Immunology· 0 citations
A six-gene-fibrosis-based prognostic model based on six genes stratifies survival risk and correlates with immune features and drug sensitivity, but provides a preliminary framework requiring prospective clinical validation.
Yanyan Qiu, Cui Lv, Shubo Ding· Clinical and Translational O...· 0 citations
This study establishes a novel CSC–associated gene signature for diagnosis and prognosis in HCC and nominates belinostat as a repurposing candidate for targeting stemness‐related pathways, offering a promising strategy for personalized therapy.
Yang Zi, Ying Zhang, Jun Wu et al.· Stem Cells International· 0 citations
Keratins (KRTs) are intermediate filament proteins expressed in epithelial cells and serve as diagnostic cancer biomarkers. They serve pivotal roles in tumor progression and metastasis, but their prognostic value in lung adenocarcinoma (LUAD) and relationship with the tumor immune microenvironment remain unclear. Gene expression of KRTs in The Cancer Genome Atlas (TCGA)-LUAD was analyzed; candidate genes were selected using LASSO and random forest. An XGBoost classifier with SHAP interpretation distinguished tumor from normal tissues. Prognostic models were developed using 101 algorithms with 10-fold cross-validation and validated in two independent GEO cohorts. Immune cell composition and pathway activity were assessed by CIBERSORT, MCP-counter, ssGSEA and GSEA; the TIDE score estimated potential immunotherapy response. Functional validation of KRT81 was performed via shRNA knockdown in LUAD cell lines, followed by proliferation, apoptosis and migration assays. A KRT-based diagnostic and prognostic signature was established. The XGBoost model achieved high accuracy (TCGA AUC=0.996; GSE31210 AUC=0.860); SHAP identified KRT81 as a key contributor. A four-gene prognostic model (KRT27, KRT80, KRT16, KRT81) stratified patients into high- and low-risk groups. The risk score was associated with advanced tumor stage and independently predicted overall survival (multivariate HR=2.16, 95% CI 1.43–3.28, P=2.0×10-4). High-risk tumors enriched proliferation/stroma pathways (cell cycle, DNA repair, ECM-receptor interaction, focal adhesion, p53 signaling); low-risk tumors enriched immune pathways. Immune profiling revealed reduced T/B cell infiltration, increased endothelial cells and higher T-cell exclusion scores in high-risk patients, indicating an immunosuppressive microenvironment. Higher risk scores also associated with chemotherapy resistance. Functional validation confirmed that shRNA-mediated KRT81 knockdown reduced LUAD cell proliferation, migration and invasion, while promoting apoptosis. These findings link KRT expression to clinical outcomes, the tumor microenvironment and therapeutic response in LUAD, suggesting roles for KRTs in cancer progression, chemotherapy resistance and predictive potential for immunotherapy response. This study provides new insights for prognostic evaluation and therapeutic decision-making in LUAD.
Aohui Chen, Ting Gao, Fengqi Liu et al.· Oncology Letters· 0 citations
This study aims to construct a prognostic model based on neutrophil heterogeneity by integrating single-cell and bulk transcriptomic data. Additionally, it seeks to explore the features of the immune microenvironment across different risk subgroups to facilitate precise prognostic assessment and individualized immunotherapy for colorectal cancer.
Colorectal cancer transcriptomic datasets from TCGA and GEO were utilized. Single-cell data underwent quality control, clustering, and cell annotation. Cell communication analysis characterized neutrophil signaling interactions. Candidate genes were identified through differential expression analysis and WGCNA, leading to the establishment and validation of a CCL15/CCDC154 dual-gene prognostic model. Differences in immune infiltration, immune checkpoint expression, and immune escape between high- and low-risk groups were analyzed, followed by molecular subtype clustering.
Significant remodeling of the tumor microenvironment was observed, with neutrophils extensively participating in multicellular signaling networks. The dual-gene model demonstrated strong predictive performance and functioned as an independent prognostic indicator. The high-risk group exhibited a pronounced immunosuppressive phenotype, characterized by significant immune escape and upregulated inhibitory immune checkpoints. Notably, distinct immune and prognostic differences were identified among molecular subtypes.
The dual-gene model based on neutrophil heterogeneity facilitates accurate prognostic stratification. The neutrophil-mediated immune microenvironment is closely linked to clinical outcomes, providing a theoretical basis for personalized treatment strategies in colorectal cancer.
A 13-gene prognostic cluster associated with poor survival in EGFR-mutant LUAD is identified and the EGFR-regulated USP15/KRT17 axis is position as a novel framework for patient risk stratification, with potential implications for guiding targeted therapeutic strategies.
Jiamin Huang, Yi Huang, Qiurong Yuan et al.· Respiratory Research· 0 citations