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TMEM171 is a potential biomarker associated with lung injury and lung adenocarcinoma.
BACKGROUND: Lung cancer therapy frequently induces lung injury, leading to treatment interruption and poor prognosis; however, specific biomarkers linking cancer therapy and therapy-related lung injury remain poorly defined. This study aimed to identify a biomarker relevant to both lung cancer progression and lung injury, with emphasis on prognostic significance and immune infiltration. METHODS: RNA-sequencing data from Gene Expression Omnibus (GEO) (GSE166340, GSE27128) were used to identify lung injury-related genes (LIRGs), and The Cancer Genome Atlas (TCGA) data were analyzed to evaluate their expression in lung adenocarcinoma (LUAD). Single-cell RNA-sequencing data (PRJNA846488) and in vivo lung injury models were used to validate TMEM171 expression. Prognostic value was assessed using Kaplan-Meier analysis, univariate and multivariate Cox regression, and a least absolute shrinkage and selection operator (LASSO) prognostic model. Immune infiltration, immune checkpoint correlations, and pathway enrichment were analyzed using single-sample gene set enrichment analysis (ssGSEA). Functional effects of TMEM171 were examined by in vitro knockdown experiments. RESULTS: A total of 10 LIRGs were identified. TMEM171 was significantly upregulated in LUAD tissues and immunotherapy-induced lung injury models. High TMEM171 expression was an independent predictor of poor prognosis and was associated with TP53 mutations, apoptosis, and inflammatory response pathways. TMEM171 expression positively correlated with neutrophil infiltration and immune checkpoint expression. TMEM171 knockdown suppressed malignant behavior of LUAD cells and induced G1/S cell-cycle arrest in vitro. CONCLUSIONS: TMEM171 represents a potential biomarker linking LUAD and therapy-related lung injury and may serve as a candidate for future mechanistic investigation.
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Identification of a chemoresistance-related prognostic gene signature by comprehensive analysis and experimental validation in pancreatic cancer.
BACKGROUND: Chemoresistance is a major hurdle to improving the prognosis of pancreatic cancer (PC). This study aimed to identify key genes regulating chemoresistance and develop a chemoresistance-related gene signature for prognosis prediction. METHODS: A total of 30 PC cell lines were subtyped according to gemcitabine sensitivity data from the Cancer Therapeutics Response Portal (CTRP v2). Differentially expressed genes (DEGs) between gemcitabine-resistant and gemcitabine-sensitive cells were subsequently identified. These upregulated DEGs associated with prognostic values were incorporated to build a LASSO Cox risk model for The Cancer Genome Atlas (TCGA) cohort. Four datasets (GSE28735, GSE62452, GSE85916, and GSE102238) from the Gene Expression Omnibus (GEO) were used as an external validation cohort. Then, a nomogram was developed based on independent prognostic factors. The responses to multiple anti-PC chemotherapeutics were estimated by the "oncoPredict" method. Tumor mutation burden (TMB) was calculated using the "TCGAbiolinks" package. Analysis of the tumor microenvironment (TME) was performed using the "IOBR" package, while the TIDE and "easier" algorithms were employed to estimate immunotherapy efficacy. Finally, RT-qPCR, Western blot and CCK-8 assays were conducted to validate the expression and functions of ALDH3B1 and NCEH1. RESULTS: A five-gene signature and a predictive nomogram were developed from six prognostic DEGs, including EGFR, MSLN, ERAP2, ALDH3B1, and NCEH1. Bulk and single-cell RNA sequencing analyses indicated that all five genes were highly expressed in tumor samples. This gene signature was not only an independent prognostic factor but also a biomarker forecasting chemoresistance, TMB, and immune cells. In vitro experiments suggested that ALDH3B1 and NCEH1 were involved in PC progression and gemcitabine chemoresistance. CONCLUSION: This chemoresistance-related gene signature links prognosis with chemoresistance, TMB, and immune features. ALDH3B1 and NCEH1 are two promising targets for treating PC.
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Pan-cancer Transcriptomic Predictors of Perineural Invasion Improve Occult Histopathologic Detection.
PURPOSE: Perineural invasion (PNI) is associated with aggressive tumor behavior, recurrence, and metastasis, and can influence the administration of adjuvant treatment. However, standard histopathologic examination has limited sensitivity in detecting PNI and does not provide insights into its mechanistic underpinnings. EXPERIMENTAL DESIGN: A multivariate Cox regression was performed to validate associations between PNI and survival in 2,029 patients across 12 cancer types. Differential expression and gene set enrichment analysis were used to learn PNI-associated programs. Machine learning models were applied to build a PNI gene expression classifier. A blinded re-review of hematoxylin and eosin (H&E) slides by a board-certified pathologist helped determine whether the classifier could improve occult histopathologic detection of PNI. RESULTS: PNI associated with both poor overall survival [HR, 1.73; 95% confidence interval (CI), 1.27-2.36; P < 0.001] and disease-free survival (HR, 1.79; 95% CI, 1.38-2.32; P < 0.001). Neural-like, prosurvival, and invasive programs were enriched in PNI-positive tumors (P adj < 0.001). Although PNI-associated features likely reflect in part the increased presence of nerves, many differentially expressed genes mapped specifically to malignant cells from single-cell atlases. A PNI gene expression classifier was derived using random forest and evaluated as a tool for occult histopathologic detection. On a blinded H&E re-review of sections initially described as PNI negative, more specimens were reannotated as PNI positive in the high classifier score cohort compared with the low-scoring cohort (P = 0.03, Fisher exact test). CONCLUSIONS: This study provides salient biological insights regarding PNI and demonstrates a role for gene expression classifiers to augment detection of histopathologic features.
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Tissue and cellular spatiotemporal dynamics in colon aging.
Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.
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Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry.
Single-nucleus analysis allows robust cell-type classification and helps to establish relationships between chromatin accessibility and cell-type-specific gene expression. Here, using samples from 92 women of several genetic ancestries, we developed a comprehensive chromatin accessibility and gene expression atlas of the breast tissue. Integrated analysis revealed ten distinct cell types, including three major epithelial subtypes (luminal hormone sensing, luminal adaptive secretory precursor (LASP) and basal-myoepithelial), two endothelial and adipocyte subtypes, fibroblasts, T cells, and macrophages. In addition to the known cell identity genes FOXA1 (luminal hormone sensing), EHF and ELF5 (LASP), TP63 and KRT14 (basal-myoepithelial), epithelial subtypes displayed several uncharacterized markers and inferred gene regulatory networks. By integrating breast epithelial cell gene expression signatures with spatial transcriptomics, we identified gene expression and signaling differences between lobular and ductal epithelial cells and age-associated changes in signaling networks. LASP cells and fibroblasts showed genetic ancestry-dependent variability. An estrogen receptor-positive subpopulation of LASP cells with alveolar progenitor cell state was enriched in women of Indigenous American ancestry. Fibroblasts from breast tissues of women of African and European ancestry clustered differently, with accompanying gene expression differences. Collectively, these data provide a vital resource for further exploring genetic ancestry-dependent variability in healthy breast biology.
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A TMED3-governed disulfidptosis-related diagnostic signature reveals tumor microenvironment remodeling in intrahepatic cholangiocarcinoma.
BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is an aggressive malignancy with poor prognosis and limited treatment options. Disulfidptosis, a novel cell death pathway driven by disulfide bond accumulation, has emerged as a potential mechanism in cancer biology; however, its role in ICC remains unclear. METHODS: We integrated single-cell RNA sequencing (GSE138709) with bulk transcriptomic datasets (TCGA-CHOL, GSE107943, GSE32225) to systematically characterize the ICC cellular landscape. Analyses included CNV inference, stemness scoring, disulfidptosis activity assessment, and cell-cell communication profiling. A diagnostic model was constructed using LASSO-logistic regression with 10-fold cross-validation and validated in independent cohorts. TME characterization, survival analysis, and drug-target screening were also performed. Experimental validation included HPA immunohistochemistry, qRT-PCR, and functional assays following TMED3 knockdown. RESULTS: Seven major cell types were identified, with malignant cholangiocytes exhibiting high aneuploidy (74%), elevated stemness, upregulated disulfidptosis activity, and extensive communication via SPP1-CD44 and IGFBP3-TMEM219 networks. A five-gene signature (TMED3, TMEM184B, MAPK13, MFSD10, GRB7) demonstrated robust diagnostic performance. Survival analysis showed borderline prognostic value for TMED3 (adjusted HR = 2.37, P = 0.073), while TMEM184B emerged as an independent prognostic factor (adjusted HR = 4.79, P = 0.028). PPI and co-expression analyses established links between signature genes and disulfidptosis regulators. Functional experiments confirmed that TMED3 knockdown suppressed ICC cell proliferation, migration, and enhanced sensitivity to glucose deprivation-induced disulfidptosis. Network-based drug screening identified eight high-priority candidates for therapeutic repurposing. CONCLUSION: This study provides a comprehensive single-cell atlas of ICC, identifies TMED3 as a key regulator of a disulfidptosis-related diagnostic signature, and demonstrates its functional role in promoting ICC malignancy. The five-gene signature shows diagnostic and prognostic promise, and the drug screening offers preliminary leads for therapeutic repurposing, providing a foundation for precision diagnosis and targeted therapy in ICC.
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Expression, oncological and immunological characterizations of BZW1/2 in pancreatic adenocarcinoma.
Background: Despite the progress in early diagnosis and treatment, prognosis of pancreatic adenocarcinoma (PAAD) is still poor. Basic leucine zipper and W2 domain-containing protein 1 (BZW1) and protein 2 (BZW2) are attached to the basic leucine zipper (bZIP) superfamily. Recently, BZW1 was identified as an important role in glycolysis of PAAD. However, the comprehensive reports about BZW1/2 in PAAD are not sufficient. Methods: RNA-seq data in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were retrospectively analyzed. We explored the expression of BZW1/2 in PAAD tissues and the associations between BZW1/2 and prognosis. In addition, the potential roles of BZW1/2 in tumor microenvironment (TME) of PAAD were analyzed. Finally, clinicopathological data of 49 patients with PAAD in our institution were collected. Immunohistochemistry was used to determine the expression of BZW1/2 in PAAD samples. Results: BZW1 and BZW2 were upregulated in PAAD tissues compared to normal tissues (p < 0.05). The expression of BZW1/2 were not significantly correlated with gender, grade and stage of PAAD (p > 0.05). High expression of BZW2 was an independent predictor for poor prognosis of PAAD (HR 1.834, 95%CI 1.303-2.581, p = 0.001). And a nomogram to predict overall survival (OS) of PAAD was established with a C-index of 0.685. BZW1 and BZW2 expression were positively associated with T cell mediated immune response to tumor cell and Th2 cells in xCell database. Tumor Immune Single-Cell Hub (TISCH) analyses indicated that BZW1 and BZW2 were mainly expressed in B cells and malignant cells. External cohort furtherly validated that high expression of BZW1 and BZW2 were predictors for poor prognosis of PAAD. Conclusion: We found that BZW1 and BZW2 are highly expressed in malignant cells and B cells in the TME of PAAD. BZW2 is an independent predictor for OS of PAAD. BZW1 and BZW2 expression are positively associated with T cell mediated immune response to tumor cell and Th2 cells in PAAD.
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An Immune Gene Signature Stratifies Breast Cancer Prognosis Through iCAF-Driven Immunosuppressive Microenvironment.
Background/Objectives: Breast cancer is the leading cause of cancer-related mortality in women, highlighting the urgent need for robust prognostic tools to enable individualized risk stratification. Methods: Transcriptomic data from 1075 breast cancer and 113 adjacent normal tissues in The Cancer Genome Atlas (TCGA) were integrated with clinical information. Differential expression analysis identified 531 immune-related genes, which were further selected by univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct a 13-gene prognostic signature. The model was validated in an independent cohort (n = 327). Tumor immune microenvironment and single-cell RNA sequencing data were analyzed to explore underlying biological differences. Results: The 13-gene signature effectively stratified patients into low- and high-risk groups with significantly different overall survival in both the TCGA cohort (log-rank p < 0.0001; C-index = 0.678; 5-year AUC = 0.72) and the validation cohort (log-rank p < 0.0001; C-index = 0.703; 3-year AUC = 0.81). Low-risk tumors exhibited an antitumor immune microenvironment enriched in CD8+ T cells, T follicular helper (Tfh) cells, and M1 macrophages, whereas high-risk tumors were dominated by immunosuppressive regulatory T cells and M2 macrophages (all p < 0.0001). Single-cell analysis revealed expansion of malignant epithelial cells and inflammatory cancer-associated fibroblasts (iCAFs) in high-risk tumors, with higher iCAF scores significantly associated with poorer survival (log-rank p = 0.00036). Conclusions: Collectively, this study delivers a rigorously validated 13-gene immune signature whose prognostic utility is rooted in distinct immune microenvironmental features, while unveiling iCAF-targeted therapeutic strategies as a promising intervention avenue.
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Causal intervention validation of gene regulatory signals in scGPT.
OBJECTIVE: Single-cell foundation models such as scGPT have been promoted as representations of gene regulation, but their advertised regulatory signal has been judged largely from attention weights, which are correlational. We ask a deliberately bounded question: whether direct interventions on scGPT gene tokens-causal with respect to the model's own computation-recover model-internal transcription-factor (TF)-target dependencies that align with curated references, whether that signal is robust, and whether it transfers to real perturbation responses. We separate these into two explicit validation axes and report both, including where the model-internal signal does not correspond to biological causality. METHODS: On Tabula Sapiens kidney, lung, and immune subsets, plus an external Krasnow lung atlas and three CRISPR perturbation datasets (Adamson, Dixit, Shifrut), we ablate or swap TF token values inside scGPT and quantify changes in target-token readouts. Axis 1 (reference alignment): intervention scores are evaluated against curated TRRUST and DoRothEA references and against attention and coexpression baselines, with new robustness studies over cell count (120-500), positive/negative pair count, four negative-sampling designs, and four readout strategies (mean, L2, cosine, max). We trace component-level circuits via activation patching, and benchmark against twelve GRN inference methods including a neural-network baseline on a matched substrate, additionally giving the classical methods larger cell budgets to test the equal-information question. Axis 2 (perturbation transfer): scores are evaluated against CRISPR perturbation-derived edges under balanced labels and AUROC. RESULTS: On Axis 1, lung shows reproducible enrichment that is stable across cell count and survives all four negative-sampling designs (permutation p improving from 0.07 to 0.03 as cells grow from 120 to 500); kidney enrichment is significant at small samples but does not survive scaling (permutation p rising to 0.16 at 500 cells); immune is at baseline. Richer readouts (L2, cosine) recover signal the scalar mean compresses away, particularly in kidney. On the matched benchmark scGPT leads classical and neural baselines at the shared 120-cell budget, but the lead narrows as the classical methods are given more cells, confirming this is a signal-efficiency result, not general superiority. On Axis 2, balanced evaluation of all three CRISPR datasets, including the TF-screen Dixit data, yields AUROC ≈ 0.50: the model-internal signal does not predict real perturbation responses. CONCLUSION: scGPT encodes tissue-conditional, intervention-sensitive regulatory structure that is aligned with literature-curated TF-target edges (robustly in lung) but is representational rather than biologically causal: it does not transfer to perturbation outcomes. The pipeline is a practical mechanistic-audit toolkit for biological foundation models, and the gap between reference alignment and perturbation transfer is a concrete cautionary result for using such models in regulatory inference.
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Multi-omics reveals key cell types and gene families regulating eggshell strength in chicken uteri.
Eggs represent an accessible and nutrient-dense source of high-quality animal protein, and decades of selective breeding have markedly elevated reproductive output in commercial laying hens. However, sustaining elevated productivity while improving eggshell integrity presents a critical challenge, as the molecular mechanisms of eggshell strength remain unclear. In this study, phenotypic assessment of eggshell strength was combined with single-cell transcriptomic profiling of the uterus from high- and low-strength groups, transcriptomic analysis of multiple tissues, and quantitative proteomic analysis of uterine fluid. Serum calcium and phosphorus levels did not differ significantly between groups. A single-cell atlas of the Rhode Island Red uterus was successfully generated for the first time, identifying nine distinct cell populations encompassing smooth muscle, epithelial, endothelial, and immune subsets. Integration of transcriptomic and proteomic datasets revealed that genes encoding collagens ( COL4A1/ 2, COL1A1/ 2, COL5A1, and COL6A1/ 2/ 3), solute carriers ( SLC4A4/ 7, SLC6A4, SLC9A2/ 9, and SLC38A2), ATPases ( ATP1A1, ATP1B1, ATP2B1/ 2, ATP2A2/ 3, and ATP2C1), calcium voltage-gated channels ( CACNB2, CACNA1C, and CACNA2D1), annexins ( ANXA5 and ANXA6), and integrins ( ITGB1 and ITGA9) were key molecular determinants associated with variation in eggshell strength. These genes were primarily enriched in signaling cascades involved in focal adhesion, actin cytoskeleton regulation, extracellular matrix (ECM)-receptor interactions, and calcium signaling. Notably, collagen family genes were predominantly localized to smooth muscle cells, consistent with the tissue remodeling and uterine inversion that occur during shell calcification, which may enhance spatial proximity between calcium ions and matrix proteins. These findings establish a multi-omics framework for understanding the uterine regulatory mechanisms underlying eggshell formation and offer a molecular foundation for breeding strategies aimed at prolonging laying cycles while preserving shell quality.