J Transl Med. 2026 Mar 20. doi: 10.1186/s12967-026-08022-3. Online ahead of print.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is a predominant contributor to cancer‑related mortality globally. Lung‑associated fibroblasts (LAFs) are intricately linked to tumorigenesis and the tumor microenvironment (TME), but their heterogeneity and prognostic relevance in LUAD remain incompletely understood. This study aimed to systematically characterize LAF subsets across the spectrum of pulmonary disease, identify LAF subpopulations associated with LUAD prognosis, and construct a robust LAF‑based prognostic signature.

METHODS: We employed a multi-omics approach, leveraging bulk RNA data of 2719 patients from 19 LUAD cohorts, single-cell RNA (scRNA) sequencing data of 368,904 cells from 93 samples, and spatial transcriptomics data of 15,673 spots from 6 samples to characterize the landscape of LAFs across various stages of pulmonary disease. We employed multiple advanced machine learning algorithms to construct and validate a robust nuclear division LAFs (nLAFs) risk score (nLRS) prediction model.

RESULTS: We observed a dynamic and gradual increase in the proportion of LAFs during the progression of LUAD. Throughout this process, we identified nine LAFs subtypes and found nLAFs are significantly associated with the prognosis of LUAD. Utilizing 100 machine learning algorithm combinations and integrating nLAFs marker genes, we developed a five gene based nLRS model, which demonstrated superior performance than other 49 published models in predicting clinical outcomes for LUAD. Additionally, we observed distinct biological functions and immune cell infiltration in the TME between high and low nLRS groups. Exploratory analysis of pan-cancer immunotherapy cohorts suggested that patients with high nLRS scores may exhibit resistance to immunotherapy in some cancer types, but prospective validation in LUAD-specific cohorts is required. Conversely, high nLRS patients displayed increased sensitivity to chemotherapeutic and targeted therapies in preclinical models.

CONCLUSION: Our study introduces a candidate five-gene signature derived from nLAFs that may serve as a robust prognostic biomarker pending prospective validation, offering insights into personalized therapeutic strategies for LUAD patients.

PMID:41862916 | DOI:10.1186/s12967-026-08022-3

Cancers (Basel). 2026 Feb 25;18(5):735. doi: 10.3390/cancers18050735.

ABSTRACT

BACKGROUND: Lactate accumulation is increasingly recognized as a feature of tumor metabolic reprogramming that can coincide with immune dysregulation and aggressive phenotypes. The prognostic and immunologic relevance of lactate-associated heterogeneity in lung cancer remains to be clarified.

METHODS: We curated lactate-related genes and identified prognostic candidates in lung cancer cohorts. Consensus clustering was applied to define lactate-associated molecular subtypes, followed by characterization of survival and tumor microenvironment features. A LASSO-based gene signature was developed to generate an individual-level risk score and an integrated nomogram. Multi-omics analyses were used to evaluate concordance between transcriptomic and proteomic alterations. Single-cell transcriptomic data were analyzed to explore cellular heterogeneity in lactate-related programs. In vitro assays evaluated the response of candidate genes to lactate exposure and assessed cell migration and invasion under proliferation-inhibited conditions after genetic perturbation.

RESULTS: Two lactate-associated molecular subtypes were identified with distinct overall survival and divergent immune microenvironment features. Subtype 1 was associated with better outcomes and a more immune-inflamed profile, whereas Subtype 2 was associated with poorer outcomes and a myeloid-enriched, immunosuppressive contexture. Pathway analyses indicated subtype-associated differences in extracellular matrix-related processes and apoptosis-associated signaling. We developed an 11-gene prognostic signature and nomogram that stratified patients by risk across TCGA and GEO cohorts. Multi-omics integration highlighted ANLN, FGA, and DKK1 as consistently dysregulated at both transcript and protein levels. Among these candidates, DKK1 showed lactate-responsive induction in vitro. DKK1 perturbation altered lactate-enhanced migratory and invasive phenotypes and was accompanied by changes in intracellular lactate levels and global protein lactylation, supporting a potential feedforward relationship between lactate exposure, DKK1 expression, and lactylation.

CONCLUSIONS: This study characterizes lactate-associated molecular heterogeneity in lung cancer and provides a lactate-related subtype framework and prognostic risk model for patient stratification. The findings nominate DKK1 as a lactate-responsive candidate linked to migration/invasion phenotypes and lactate/lactylation changes in vitro.

PMID:41827671 | PMC:PMC12985219 | DOI:10.3390/cancers18050735