The lung cancer-originated TME subtyping system cannot be directly extrapolated to Chinese CRC patients, and LAG3 serves as a promising independent transcriptomic candidate marker for distinguishing CRC TME subtypes.
Abstract
Abstract Objective Commercially available next-generation sequencing (NGS) platforms in China routinely adopt a lung cancer-derived tumor microenvironment (TME) subtyping signature from a European cohort to classify colorectal cancer (CRC), yet its diagnostic performance in Chinese CRC patients remains unvalidated. This study aimed to evaluate the subtyping efficiency of the lung cancer TME signature in a Chinese CRC cohort, screen CRC-specific immune mRNA biomarkers for TME subtyping, and explore the clinical utility of IRF1, CD8A and LAG3 for distinguishing immune-enriched (IE) and immune-desert plus fibrotic (D+F) subtypes. Methods A total of 87 FFPE CRC specimens with complete NGS and clinicopathological data were retrospectively enrolled, including 15 IE subtype and 72 D+F subtype patients. Thirty-one mRNA transcripts covering 13 immune-metabolic homeostasis genes and 18 immune checkpoint/infiltration-related genes were divided into two functional modules. Spearman correlation analysis was performed to assess co-expression patterns among candidate genes. Receiver operating characteristic (ROC) curves combined with five-fold cross-validation were used to compare the discriminatory efficacy of single-gene markers and the three-gene combined panel. Results Strong positive co-expression was observed between IRF1, CD8A and LAG3 (IRF1-CD8A: r=0.93; IRF1-LAG3: r=0.84; CD8A-LAG3: r=0.73). Nominal P-values indicated elevated expression of IRF1, CD8A and LAG3 in IE subtype, though no intergroup significance remained after Benjamini-Hochberg FDR correction, largely attributed to the limited sample size of IE cases. Single-gene ROC analysis showed AUC values of 0.763 (IRF1), 0.752 (CD8A) and 0.771 (LAG3), with LAG3 exhibiting the best individual discriminatory capacity. The three-gene combined panel yielded a cross-validated AUC of 0.717, inferior to single LAG3, due to severe collinearity that generated redundant predictive information. Conclusions The lung cancer-originated TME subtyping system cannot be directly extrapolated to Chinese CRC patients. LAG3 serves as a promising independent transcriptomic candidate marker for distinguishing CRC TME subtypes. The robust collinearity among IRF1, CD8A and LAG3 eliminates additional predictive benefits of the combined signature. Large independent multi-center Chinese CRC cohorts are required to construct population-specific immune transcriptomic biomarkers for standardized clinical NGS TME stratification.
The 36-gene signature developed in this study demonstrates favorable predictive performance and stability across both the training and independent Chinese cohorts, and underscores the critical roles of energy metabolism and ECM remodeling in NMIBC recurrence.
Liyuan Dong, Yun Peng, Yuxuan Song et al.· Beijing da xue xue bao. Yi x...· 0 citations
Telomere maintenance-related genes (TMRGs) are implicated in Colorectal cancer (CRC) development, but their prognostic value and clinical relevance remain insufficiently explored. This study aims to develop a TMRG-based prognostic model and elucidate its clinical utility in CRC management.
The Cancer Genome Atlas database was utilized to download RNA-seq data from 638 CRC and 51 control samples. Differential expressed genes were screened and intersected with 2086 TMRGs, resulting in the identification of 976 TMRGs. Through univariate and multivariate Cox regression analysis, a prognostic model comprising three telomere maintenance-related biomarkers (
PDE1B, TFAP2B, and HSPA1A
) was developed and validated using an external dataset. By integrating the model risk score with clinical features, a nomogram was constructed to predict the survival outcomes of CRC patients. Additionally, an in-depth investigation of the immuno-infiltration, functional variation and drug sensitivity analysis were performed in two risk subgroups defined by the prognostic model. Finally, the functional significance of
PDE1B
in CRC cell lines was investigated through MTT assays, cell colony formation assays, transwell assays and flow cytometry.
A total of 976 DE-TMRGs were enriched in telomere/DNA replication pathways. A three-gene signature (PDE1B, TFAP2B, and HSPA1A) stratified patients into high- and low-risk groups with divergent survival (AUC >0.60, validated externally). High-risk patients had advanced N/M stages, elevated M0/M2 macrophages, reduced CD4
+
memory T cells, and upregulated immune checkpoints. Nomogram integrating risk score, age, and N/M stage accurately predicted 1-/3-/5-year survival. Low-risk patients showed greater 5-fluorouracil sensitivity.
PDE1B
expression was significantly reduced in CRC tissues and correlated with advanced stages. Functional assays confirmed PDE1B overexpression suppressed proliferation, migration, invasion, and induced apoptosis in CRC cells.
This study identifies a moderately predictive telomere maintenance-related gene signature as an independent prognostic predictor in CRC. The risk stratification model effectively discriminates patients with distinct survival patterns, tumor microenvironments, and therapeutic responses, while the integrated nomogram offers additional reference information for survival analysis, albeit with only moderate predictive accuracy. These findings indicate telomere maintenance-related gene signature could serve as a preliminary auxiliary risk stratification tool for postoperative CRC patients,
PDE1B
may also serve as a potential epithelial tumor-suppressor target for future preclinical studies.
Unknown authors· Frontiers in Molecular Biosc...· 0 citations
Neoadjuvant chemoimmunotherapy (NACI) improves outcomes in resectable lung squamous cell carcinoma (LUSC), yet response varies widely and current biomarkers lack precision. Novel correlates of immunotherapy sensitivity tailored to the LUSC tumor microenvironment (TME) are urgently needed.
Using TCGA-LUSC transcriptomic data, we constructed a 25-gene prognostic model and applied three machine learning algorithms in combination with the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to identify core genes linked to prognosis and immunotherapy response. Immune infiltration and enrichment analyses were performed to characterize the TME. An independent pre-NACI biopsy cohort (n=36) was used for histopathological validation to explore correlations with pathological response, while single-cell RNA-seq (GSE207422) and CellChat were used to infer tumor-stromal crosstalk and explore underlying mechanisms.
The risk score independently stratified prognosis. Among three core genes, high MBNL2 expression was associated with higher TIDE scores, lower TIDE-predicted response rates, and elevated cancer-associated fibroblast (CAF) scores. scRNA-seq revealed systematically enhanced communication between MBNL2-high tumor cells and FAP
+
CAFs, with unique ligand-receptor pairs enriched in WNT and EGF pathways; FAP
+
CAFs interacted with regulatory T cells via ECM remodeling and the MDK-NCL axis. Histopathological validation confirmed that low tumor-cell MBNL2 expression correlated with higher pathological response and pCR rates, reduced FAP, and decreased FOXP3 expression.
This study establishes a 25-gene prognostic model for LUSC and identifies MBNL2 as a novel correlate of poor pathological response to NACI. Elevated MBNL2 expression in tumor cells is associated with enhanced tumor-CAF crosstalk, CAF activation, and an immunosuppressive TME, laying a foundation for future mechanistic investigation.
Hao Wu, Yang Cheng, Honglin Yan et al.· Frontiers in Oncology· 0 citations
Patients with head and neck squamous cell carcinoma (HNSC) face a high risk of developing non-small cell lung cancer (NSCLC). However, the shared molecular drivers linking these two malignancies remain poorly defined. We integrated TCGA and GEO datasets to identify shared differentially expressed genes (DEGs) between HNSC and NSCLC, subsequently evaluating their prognostic value, immune infiltration patterns (ssGSEA), and enriched pathways. Clinical validation of SCG5 protein expression was comprehensively performed using tissue microarrays (TMA) via immunohistochemistry. in vitro assays, including siRNA-mediated knockdown, CCK-8, colony formation, and Western blotting, were conducted in both HNSC (SCC-25) and NSCLC (A549) cell lines to ascertain its oncogenic functions and underlying molecular mechanisms. SCG5 was identified as a consistently upregulated, independent predictor of poor survival in both cancers. IHC confirmed SCG5 protein overexpression, correlating with lymph node metastasis and advanced clinical stages. GSEA revealed that SCG5-associated genes were significantly enriched in focal adhesion and the PI3K/AKT signaling pathway in both HNSC and NSCLC. SCG5 expression correlated positively with tumor-infiltrating macrophages (P < 0.001) and with PDCD1LG2, HAVCR2 and SIGLEC15 (P < 0.05). SCG5 knockdown significantly suppressed cell proliferation and colony formation in both SCC-25 and A549 cells, concurrently attenuating PI3K and AKT phosphorylation. SCG5 is a novel shared oncogenic driver and prognostic biomarker for HNSC and NSCLC. Downregulation of SCG5 suppresses the growth of HNSC and NSCLC cells, potentially through modulating the PI3K/AKT signaling pathway, thereby presenting a potential therapeutic target for both malignancies.
Feng Zhao, Haining Wang, Tao Qin et al.· Discover Oncology· 0 citations
BACKGROUND AND STUDY AIMS
Immunotherapy offers promising prospects for esophageal squamous cell carcinoma (ESCC), a highly fatal malignant tumor. Given the association between manganese metabolism and tumor immunity, this study explored the prognostic value and role of manganese-metabolism-related genes (MMRGs) in the ESCC immune microenvironment to uncover their clinical potential.
PATIENTS AND METHODS
Transcriptomic and clinical data of ESCC were retrieved from TCGA and the GEO as training and validation cohorts, respectively. Patients were clustered and subtyped based on MMRGs, with survival compared. A prognostic risk model was constructed using differential analysis, PPI network, and Cox regression; its relationship with immune characteristics, immunotherapy response, and drug sensitivity was evaluated. SLC40A1 was knocked down in vitro, and its effects on cellular function and drug sensitivity were assessed via qRT-PCR, Western blot, colony formation, Transwell, and CCK-8 assays.
RESULTS
ESCC patients were stratified into two MMRG-defined subtypes. Cluster 2 showed significantly worse overall survival (OS) than Cluster 1. An 8-gene prognostic model was established and patients were assigned to RiskScorehigh and RiskScorelow groups. The high-risk group, characterized by worse OS, displayed an immunosuppressive microenvironment with abundant M2 macrophages and high immune-checkpoint expression (PDCD1, CTLA4, TIGIT), along with a lower TIDE score, suggesting potentially greater benefit from immune-checkpoint blockade. The RiskScorehigh group was more sensitive to Gemcitabine and Oxaliplatin, whereas the RiskScorelow group responded better to BI-2536 and NU7441. Cellular functional assays confirmed high expression of SLC40A1 in ESCC cells. SLC40A1 knockdown significantly inhibited cell proliferation, migration, and invasion. Additionally, cells exhibited greater sensitivity to Gemcitabine than to BI-2536.
CONCLUSION
MMRG-based subtyping and the reliable prognostic risk score model provide novel insights for predicting prognosis and developing personalized therapy in ESCC.
Long Li, Ying Zhang, Qi Li et al.· Arab Journal of Gastroentero...· 0 citations
BACKGROUND
Pancreatic cancer is among the most lethal cancers because of late diagnosis and inadequate therapeutic preferences. The tumor microenvironment and immune response have critical roles in disease progression and patient outcomes. LncRNAs are regulators of immune function and tumorigenesis, yet their prognostic value in pancreatic cancer remains partly clarified.
METHODS
RNA-sequencing data and corresponding clinical features for 178 pancreatic cancer patients were obtained from TCGA. Immune-related mRNAs were retrieved from the MSigDB, and immune-related lncRNAs were identified based on a significant Pearson correlation with at least two immune-related mRNAs. Regression analyses were sequentially applied to construct a prognostic immune-related lncRNA set. The predictive performance of the set was assessed using time-dependent ROC curves and Kaplan-Meier survival analysis in both the TCGA training cohort and an independent external validation cohort (GSE224564).
RESULTS
From 4059 lncRNAs, 1172 immune-related lncRNAs demonstrated strong associations with immune-related mRNAs. Sequential screening identified 34 survival-associated lncRNAs by univariate Cox regression (P < 0.01), 12 lncRNAs following LASSO regression, and ultimately two lncRNAs-CASC8 (HR = 1.7, P < 0.05) and LINC01004 (HR = 0.54, P < 0.05)-by multivariate Cox regression. A risk score signature was developed using these two lncRNAs. Time-dependent ROC analysis demonstrated moderate-to-good predictive precision in the training cohort, with area under the curve (AUC) values of 0.702, 0.774, and 0.84 for 1-, 3-, and 5-year overall survival, respectively. Patients in the high-risk group exhibited significantly worse survival compared to the low-risk group (P < 0.05). External validation in the GSE224564 cohort confirmed consistent risk stratification (P = 0.019), though with lower discriminative accuracy (AUC ∼ 0.60). Multivariate analysis demonstrated that the risk score acted as an independent prognostic factor in both cohorts.
CONCLUSION
The CASC8 and LINC01004 immune-related lncRNA profile serves as a potential prognostic marker for pancreatic cancer. This set demonstrates increasing predictive trends over time in the training dataset. The presented findings offer novel insights into the correlation of immune-related lncRNAs with pancreatic cancer progression and provide a foundation for future risk stratification modeling.
