Aug 2026· Frontiers in Oncology· Vol 16· 0 citations· 57 references
Medicine
TL;DR
A 25-gene prognostic model for LUSC is established and MBNL2 is identified as a novel correlate of poor pathological response to NACI, laying a foundation for future mechanistic investigation.
Abstract
Background 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. Methods 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. Results 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. Conclusion 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.
The esophageal cancer multimodal immuno-chemotherapy classifier (EMIC), which effectively stratifies patients into two groups with distinct therapeutic responses: EMIC1 (predicted to benefit from combined immuno-chemotherapy) and EMIC2 (predicted to have equivalent outcomes with chemotherapy alone), providing a rationa...
Yan Zheng, Wen-Qun Xing, Ji Wang et al.· Diseases of the esophagus· 0 citations
Targeting OLR1 may represent a promising strategy to enhance immunotherapy efficacy and improve clinical outcomes in patients with resectable lung cancer, but the study is limited by a relatively small validation cohort and a strong reliance on bioinformatics analyses.
Xin-Xian Chen, Siang Zhang, Xue-Feng Ai et al.· Journal of Inflammation Rese...· 0 citations
An attention-based multi-omics framework that integrates pretreatment genomics, transcriptomics, proteomics, epigenomics, and clinical variables to predict pCR in early-stage breast cancer improves prediction of NAC response while producing interpretable outputs.
J. Fakoya, Catherine Falayi, M. Ajinaja· Cureus Journal of Computer S...· 0 citations
Esophageal Cancer: Adjuvant and Neo-Adjuvant Therapies
The integration of anti-PD-1 antibody therapy with chemotherapy has shown promising results in the neoadjuvant treatment of locally advanced esophageal squamous cell carcinoma. Conventional imaging may not fully capture immunotherapy effects due to unc...
Jian-Feng Zhou, Yu-Shang Yang, Yong Yuan et al.· Diseases of the esophagus· 0 citations
Background Triple-negative breast cancer (TNBC) shows marked intratumoral heterogeneity and variable responses to neoadjuvant chemotherapy (NAC), but the cellular determinants of treatment response remain incompletely defined. Methods We analyzed the scRNA-seq dataset GSE161529 to characterize TNBC cellular states and...
Menglong Gao, Zhen Liu, Lili Xi et al.· Frontiers in Oncology· 0 citations
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