bims-meluca 2026-03-15 papers

J Gastrointest Oncol. 2026 Feb 28. 17(1): 13

Colon cancer cachexia remodels gut microbiota and metabolite profiles in a murine model.

Ting Zou, Jingwei Zheng, Zhangyi Xie, Yan Hu, Xinxin Yang, Xiangyang Xue, Liting Lu, Xiaodong Chen, Sunzhong Mao, Mengyuan Niu.

Background: Cancer cachexia is a multifactorial syndrome involving involuntary weight loss, muscle atrophy, and systemic inflammation, contributing significantly to mortality in advanced cancers. Although gut microbiota dysbiosis has been implicated in metabolic and inflammatory disturbances relevant to cachexia, the functional metabolic consequences remain poorly understood. Using a murine model of colon carcinoma 26 (C26)-induced cachexia, we integrated metagenomic sequencing and non-targeted metabolomics to delineate cachexia-specific microbial and metabolic alterations compared to non-cachexia tumor-bearing and healthy controls.
Methods: To investigate colon cancer cachexia-induced remodeling of the gut ecosystem, we established mouse models using cachexia-inducing and non-cachexia-inducing colon carcinoma 26 cells. Food intake, body weight, muscle and fat weight were monitored. Cecal content was collected for metagenomic sequencing and non-targeted metabolome analysis.
Results: Colon cancer cachexia models were successfully established as evidenced by reduced food intake, decreased body weight, and loss of muscle and fat mass. Metagenomic sequencing revealed decreased microbial diversity and distinct structural separation in colon cancer cachexia mice, with enriched genera including Bacteroides, Phocaeicola, Escherichia, Enterobacter, Helicobacter, and Proteus, and depletion of butyrate- and bile acid-producing taxa including Alistipes, Eubacterium, Roseburia, Clostridium, and Hungatella. Functional analysis indicated significant alterations in metabolic pathways. Metabolomic profiling identified reduced levels of ursodeoxycholic acid (UDCA), hyodeoxycholic acid (HDCA), branched-chain amino acids, and bacterial amino acid metabolites (bAAms), alongside enrichment in nucleotide and steroid hormone metabolism. Correlation analyses demonstrated significant associations between specific microbial genera and altered metabolites.
Conclusions: Colon cancer cachexia remodeled the gut microbiota and metabolite landscape in a murine model. These findings suggested specific bacterial taxa and metabolites as potential biomarkers and therapeutic targets, offering new directions for the prevention and treatment of cancer cachexia. This study reveals distinct taxonomic and functional shifts in the gut microbiota alongside associated metabolic disruptions, offering new insights into cachexia pathophysiology and potential therapeutic targets.

Keywords: Colon cancer cachexia; gut microbiota; host-microbial interactions; metabolomics; metagenomic sequencing

DOI: https://doi.org/10.21037/jgo-2025-720

Transl Lung Cancer Res. 2026 Feb 28. 15(2): 25

PKC-iota drives EGFR-TKI resistance in EGFR-mutated NSCLC by phosphorylating FASN to reprogram lipid metabolism.

Lin Ma, Yingwen Han, Lunran Xu, Qingyuan Sun, Siyue Song, Huizhen Yang, Xinran Chen, Wenhui Xie, Yumei Chen, Liu Liu.

Background: Non-small cell lung cancer (NSCLC) remains a significant challenge to global public health issues. However, epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) resistance inevitably occurs in treating EGFR-mutant NSCLC, and the underlying metabolic mechanisms remain unclear. The objective of our study is to explore the regulatory mechanisms of the atypical protein kinase C-iota (PKC-iota) in reprogramming lipid metabolism and whether this regulation contributes to the EGFR-TKI resistance in EGFR-mutant NSCLC.
Methods: We use mass spectrometry (MS), co-immunoprecipitation, proximity ligation assays and molecular docking to investigate the interaction of PKC-iota and fatty acid synthase (FASN). Subsequently, Western blot assay, MS, lipid staining, membrane fluidity assay, and membrane proteins assay were performed to investigate how PKC-iota regulated the lipid metabolism by FASN. We established four types of transiently transfected H1975 and PC9 cell lines. These models were then employed in a series of assays-including Cell Counting Kit-8 (CCK-8), flow cytometry, cell counting, and colony formation-to evaluate changes in cell proliferation and EGFR-TKIs sensitivity. Four types of stably transfected H1975 cell lines were inoculated into female BALB/c nude mice, then the tumorigenicity and osimertinib sensitivity of the cell groups were analyzed. Finally, we collected 45 tumor samples of EGFR-mutated NSCLC patients to examine the clinical significance of the PKC-iota/FASN axis.
Results: We observed that PKC-iota physically interacted with FASN and stabilized the FASN protein by phosphorylating it and inhibiting its ubiquitin-proteasome degradation at the post-transcriptional level. PKC-iota enhanced short/medium-chain and unsaturated fatty acid synthesis via FASN, and the PKC-iota/FASN axis increased membrane fluidity to inhibit lipid raft-mediated EGFR endocytosis and degradation while upregulating EGFR membrane localization and promoting EGFR overactivation in NSCLC. The increased tumor growth and EGFR-TKI resistance induced by the PKC-iota/FASN axis were observed both in vivo and in vitro. Clinically, we showed that high co-expression of PKC-iota and FASN correlated with poor EGFR-TKI response in EGFR-mutated NSCLC.
Conclusions: Our study elucidates a mechanism of EGFR-TKI resistance mediated by the PKC-iota/FASN axis through reprogramming lipid metabolism in EGFR-mutated NSCLC, which provides a novel therapeutic target for overcoming EGFR-TKI resistance and improving the prognosis of EGFR-mutated NSCLC patients.

Keywords: Protein kinase C-iota (PKC-iota); epidermal growth factor receptor tyrosine kinase inhibitor resistance (EGFR-TKI resistance); fatty acid synthase (FASN); fatty acid synthesis; non-small cell lung cancer (NSCLC)

DOI: https://doi.org/10.21037/tlcr-2025-aw-1260

Thorac Cancer. 2026 Mar;17(6): e70257

Blood Based Biomarkers for Predicting Treatment Response to Immune Checkpoint Inhibitors After EGFR-TKI Resistance in Non-Small Cell Lung Cancer.

Min Seok Park, Jun Hyeok Lim, Nuri Park, Eunji Park, Ayoung Lim, Suji Lee, Yejin Cho, Sehan Kwak, Minseo Lee, Donghyun Seo, Lucia Kim, Woo Kyung Ryu, Jeong-Seon Ryu, Eun Young Kim, Soon-Sun Hong, Kyung Hee Jung.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have limited benefit in epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC). However, they are often tried after tumors develop resistance to EGFR tyrosine kinase inhibitors (TKIs). Because EGFR-TKI treatment alters the tumor microenvironment, biomarkers predictive of ICI response are ideally identified post-EGFR-TKI resistance, but obtaining repeat biopsies at this time can be challenging. The purpose of this study was to explore predictive biomarkers for ICI response using plasma samples collected after EGFR-TKI therapy.
METHODS: This retrospective analysis included 28 patients with EGFR-mutant NSCLC treated with an ICI after developing resistance to EGFR-TKI. Plasma samples collected at TKI progression were profiled using an Olink Target 96 immune protein panel to identify differential protein expression. Candidate protein biomarkers were validated by immunohistochemistry in tumor tissue. Durable clinical response (DCB) was defined as patients achieving progression-free survival (PFS) ≥ 6 months during ICI therapy.
RESULTS: Of the 28 patients, 6 (21.4%) achieved durable clinical benefit, with PFS ≥ 6 months. Proteomic analysis identified four plasma proteins that differed significantly between DCB and NCB. Gal-9 and GZMH levels were elevated in NCB, whereas IL-4 and IL-6 were elevated in DCB. Notably, PFS was significantly longer in patients with lower Gal-9 and higher IL-4 levels.
CONCLUSIONS: Plasma-based immune markers measured at the time of TKI resistance may help predict which patients with EGFR-mutant NSCLC will respond to subsequent ICI therapy. Such biomarkers could guide immunotherapy decision-making in this clinically challenging population.

Keywords: epidermal growth factor receptor; immune checkpoint inhibitor; liquid biopsy; non‐small cell lung cancer; predictive biomarker; treatment response; tyrosine kinase inhibitor

DOI: https://doi.org/10.1111/1759-7714.70257

J Transl Med. 2026 Mar 10.

Unraveling lung cancer dynamics: a new metabolic signature improving the prediction of recurrence in resected lung adenocarcinoma.

BACKGROUND: Lung cancer is characterized by wide genetic, molecular, and phenotypic alterations that may challenge diagnosis and clinical decision-making. This heterogeneity often leads to variable responses to therapies, resulting in suboptimal outcomes for many patients. Recent advancements in omics technologies have enabled a deeper exploration of mechanisms driving tumor behavior and identification of specific molecular signatures. Tumor metabolic reprogramming, one of the hallmarks of cancer development, progression, and recurrence, represents a promising field of research.
METHODS: In this study, we developed a comprehensive metabolic signature using RNA-sequencing data from independent cohorts of patients diagnosed with stage I-III resectable lung adenocarcinoma (LUAD) to enhance patient stratification and prognostic accuracy.
RESULTS: We identified a novel prognostic signature "LMetSig" consisting of 10 metabolic genes that significantly stratified LUAD patients into high- and low-risk subgroups for disease-free survival (DFS). Cox regression analysis demonstrated that LMetSig is an independent prognostic biomarker for DFS. Among the LMetSig, TK1 gene emerged as a promising LUAD-specific biomarker. It was undetectable in normal tissue, showed variable expression in tumor samples and correlated with shorter DFS when expressed at high levels.
CONCLUSION: Our findings suggest that LMetSig can significantly improve LUAD patients' stratification alongside conventional pathological and clinical parameters. By distinguishing high-risk patients from those with more favorable prognosis, this approach has the potential for informing personalized treatment strategies and improving clinical decision-making.

Keywords: Lung adenocarcinoma (LUAD); Metabolism; Non-Small Cell Lung Cancer (NSCLC); Prognostic signature

DOI: https://doi.org/10.1186/s12967-026-07917-5