SEARCH · PubMed Health

Results for “Signature”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4Linked to original sources

PubMed2026

SERPINE1-centric inflammatory signature associates with treatment resistance and survival in laryngeal squamous cell carcinoma.

BACKGROUND: Laryngeal squamous cell carcinoma (LSCC) prognosis remains poor despite treatment advances. More accurate prognostic assessment models can help guide individualized treatment and improve prognosis. Chronic inflammation contributes to tumorigenesis, yet inflammatory response-related genes (IRGs) in LSCC prognosis are underexplored. This study aimed to construct an IRG prognostic signature for LSCC and further dissect core IRG-mediated mechanisms of immune escape and chemoresistance. METHODS: Transcriptional profiles and clinical data from LSCC patients were retrieved from The Cancer Genome Atlas (TCGA). IRGs were sourced from Gene Set Enrichment Analysis (GSEA) hallmark gene set. We identified differentially expressed IRGs linked to survival outcomes in LSCC. Key IRGs were subsequently selected using least absolute shrinkage and selection operator (LASSO) Cox regression analysis to establish an inflammatory risk score model. This model underwent internal validation within the TCGA cohort and external validation using independent Gene Expression Omnibus (GEO) datasets. We further assessed the model's association with the tumor immune microenvironment and the impact of IRGs on chemotherapy response. Finally, the functional roles of interested signature IRG were experimentally validated in LSCC cell lines. RESULTS: Four significant IRGs (AQP9, ITGA5, LCK, SERPINE1) were identified to build the risk score model. The model stratified LSCC patients into distinct prognostic groups: TCGA cohort: 5-year area under the curve (AUC) =0.836, P<0.001; GSE25727 cohort: 5-year AUC =0.706, P=0.02; GSE27020 cohort: 5-year AUC =0.798, P<0.01. Multivariate analysis confirmed the risk score as an independent prognostic factor (P<0.05). High-risk patients showed reduced immune cell infiltration (CD8+ T cells, dendritic cells) and suppressed immune pathways. Multi-algorithm immune analysis further revealed defective antigen presentation and reduced anti-tumor immune infiltration in high-risk LSCC, promoting tumor immune escape. GSEA/Gene Ontology (GO) enrichment combined with drug sensitivity prediction further revealed that high-risk tumors activate invasive signaling and acquire broad chemoresistance alongside impaired anti-tumor immunity. SERPINE1 might be associated with chemotherapy resistance and exhibited the highest alteration frequency (predominantly amplification) and overexpression in LSCC tissues. Its knockdown significantly suppressed proliferation, migration, invasion and chemoresistance in LSCC cells. Immunohistochemistry (IHC) confirmed tumor SERPINE1 overexpression (P=0.002 vs. normal tissues), correlating with poor survival (P<0.001). CONCLUSIONS: The 4-IRG risk signature is a reliable prognostic indicator reflecting immune dysfunction in LSCC. SERPINE1 is validated as a therapeutic target and biomarker, enriching our understanding of gene regulation dynamics in LSCC.

Laryngeal cancer↗

PubMed2026

Microplastics and nanoplastics-related genes signature predicts prognosis in pancreatic ductal adenocarcinoma and functional validation of interleukin 1 alpha.

BACKGROUND: Microplastics and nanoplastics (MNPs), as emerging environmental pollutants, have garnered significant attention from the global scientific community due to their potential threats to human health, particularly their association with the occurrence and development of cancer. The goal of our study is to create a predictive marker for pancreatic ductal adenocarcinoma (PAAD) based on MNPs-related genes, with the purposes of predicting survival outcomes and assessing the tumor immune microenvironment. METHODS: Using multi-cohort data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC), we assessed the association between MNPs and PAAD prognosis through the Xiantao Academic (https://www.xiantao.love/). The development of a prognostic signature was followed by an assessment of its significance through the Kaplan-Meier method, time-dependent receiver operating characteristic (ROC), and decision curve analysis (DCA). The validity of the risk model was confirmed through the ICGC and GSE71729 cohorts. The model was then assessed for levels of tumor immune infiltration. To explore MNPs-related genes expression characteristics within immune cells in PAAD, we performed single-cell RNA sequencing and spatial transcriptomics analysis through the Sparkle Platform (https://grswsci.top/). Finally, in vitro experiments were conducted to investigate the biological function of interleukin 1 alpha (IL1A). RESULTS: A four-gene signature comprising XDH, IL1A, KIF20A, and ASPM, based on MNPs, was developed to stratify PAAD patients into two distinct risk groups. The high-risk group showed a significantly poorer prognosis. A similar trend was verified in the external cohorts ICGC and GSE71729. The signature risk score affected immune cell infiltration in the PAAD microenvironment. The infiltration of B cells, CD8+ T cells, cytotoxic cells, immature dendritic cells (iDCs), mast cells, plasmacytoid dendritic cell (pDC), T cells, Tem cells, T follicular helper (TFH) cells, and T helper 17 (Th17) cells had a positive correlation with the low-risk group. In contrast, high-risk patients tended to have increased number of T helper (Th2) cells and higher expression of SIGLEC15, CD274, IGSF8. Knockdown of IL1A in PAAD cells inhibited their tumor proliferation ability in vitro. CONCLUSIONS: Using MNPs-related genes, we built a prognostic model for PAAD, revealing that patients with high-risk scores are likely to have a worse prognosis. This model is designed to develop personalized treatment strategies tailored to the specific needs of each patient, thereby improving clinical outcomes for PAAD patients. Furthermore, IL1A could be a promising therapeutic candidate for PAAD.

Microplastics↗

PubMed2025

Identification of Prognostic Gene Signatures for Survival of Patients With Phaeochromocytoma, Paraganglioma, and Other Tumor Types.

BACKGROUND/AIM: Tumor treatments remain unsatisfactory, as many patients continue to die despite therapy. There is an urgent need for novel drug targets, particularly for rare tumors. In this study, we sought to identify genes with prognostic significance for survival in patients with phaeochromocytoma or paraganglioma. We also examined whether these genes are relevant in other tumor entities. PATIENTS AND METHODS: We mined the TCGA-based KM Plotter and studied 186 risk genes for phaeochromocytoma and paraganglioma. RESULTS: Using Kaplan-Meier statistics, we performed 3,163 calculations based on 7,489 tumor biopsies and identified a 2-gene signature for phaeochromocytoma/paraganglioma (AQP4, FAM84H). Since the 186 risk genes are not exclusively related to the development of phaeochromocytoma/paraganglioma alone, we also investigated their prognostic relevance in 17 other tumor types. A clustered 12-gene signature has been found common in four other tumor entities (liver hepatocellular carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, lung adenocarcinoma). This signature consisted of BUB1, BUB1B, CDK1, CENPA, CKAP2L, IQGAP3, MKI67, NDC80, PBK, RRM2, TOP2A, and TTK. CONCLUSION: Our analysis provides a basis for the development of a novel prognostic test to predict the survival time of patients.

Kaplan-Meier analysis↗

PubMed2026

ZBTB16-associated NK cell alterations reveal shared immunometabolic signatures linking primary Sjögren's syndrome and type 1 diabetes mellitus.

BACKGROUND: Primary Sjögren's syndrome (pSS) and type 1 diabetes mellitus (T1DM) share immune-inflammatory features, yet conserved pathogenic signatures linking these autoimmune disorders remain incompletely understood. The present research sought to uncover common molecular markers and dissect the underlying immune-metabolic cross-talk underlying pSS and T1DM. METHODS: Gene expression profiles of patients with pSS and T1DM were retrieved from the Gene Expression Omnibus database, normalized, and corrected for batch effects prior to downstream analyses. Overlapping potential biomarkers were screened by integrating differential expression analysis, weighted gene co-expression network analysis and least absolute shrinkage and selection operator regression. Functional enrichment based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases was implemented to interpret gene biological properties, and a protein-protein interaction network was further established afterwards. Diagnostic performance was evaluated using receiver operating characteristic analysis. Experimental validation was conducted in non-obese diabetic (NOD) mice using quantitative PCR, immunohistochemistry, and flow cytometry. The CIBERSORT algorithm was adopted to quantify immune cell infiltration levels. RESULTS: ZBTB16 was identified as a shared hub biomarker in both pSS and T1DM and exhibited favorable diagnostic performance. Experimental validation confirmed significantly reduced ZBTB16 expression in peripheral blood mononuclear cells, salivary gland tissues, and pancreatic tissues of NOD mice. Gene Set Enrichment Analysis indicated that ZBTB16-associated signatures were enriched in mitochondrial-related processes, neuroactive ligand-receptor interactions, and ribosome-related pathways. Immune infiltration analysis revealed that resting natural killer (NK) cells were positively correlated with ZBTB16 expression in both diseases. Flow cytometric analysis further confirmed a reduced proportion of resting NK cells in peripheral blood of NOD mice, consistent with the CIBERSORT-based prediction. CONCLUSION: This study identifies ZBTB16 as a shared biomarker linking pSS and T1DM. Reduced resting NK-cell abundance was consistently observed in both computational and experimental analyses, and bioinformatic correlation analysis suggested a positive association with ZBTB16 expression. These findings provide evidence for shared molecular and immunological signatures underlying the two autoimmune disorders and support further investigation of the biological role and diagnostic value of ZBTB16 in pSS and T1DM.

Sjogren's Syndrome↗

PubMed2026

Genome-Resolved Functional Profiling of Osteoporosis-Associated Gut Bacteria Highlights Putative Metabolic and Immunogenic Signatures of the Gut-Bone Axis.

The gut microbiota has emerged as a potential regulator of bone metabolism, but the genome-encoded functional repertoire of osteoporosis-associated gut bacteria remains insufficiently characterized. This study performed in silico functional profiling of gut bacterial taxa associated with osteoporosis, low bone mineral density, or comparator bone-related phenotypes. Twenty candidate taxa were selected from evidence in the human microbiome and represented by 26 curated bacterial reference genomes. Genome-wide annotations were used to map predicted gut-bone axis signatures, carbohydrate-active enzyme (CAZyme) repertoires, selected Kyoto Encyclopedia of Genes and Genomes pathways, and gutSMASH-predicted metabolic gene clusters. Functional burdens were normalized as hits per 1000 annotated proteins and integrated into metabolic, immunogenic, CAZyme, KEGG, and metabolic gene cluster profiles. Twelve predicted gut-bone axis signatures were identified, comprising 3337 primary candidate protein hits and a strict high-confidence subset of 2497 hits. Dominant signatures included vitamin B12/cobalamin metabolism, folate/one-carbon metabolism, peptidoglycan/cell-wall biosynthesis, and short-chain fatty acid-related functions. Dialister invisus, Dialister succinatiphilus, Megamonas funiformis, and Megamonas hypermegale showed the strongest normalized predicted gut-bone axis signal. These hypothesis-generating findings prioritize microbial metabolic and immunogenic features for future metagenomic, metabolomic, and experimental validation studies.

Osteoporosis↗

PubMed2026

Multi-omic signatures of genetic mechanisms inform on type 2 diabetes biology and patient heterogeneity.

Type 2 diabetes (T2D) is a heterogeneous disease shaped by genetic pathways related to insulin resistance and β-cell dysfunction, but how this heterogeneity is reflected molecularly remains unclear. We integrated partitioned polygenic scores (pPS) with proteomic and metabolomic profiling to define molecular signatures of T2D and their clinical relevance. We analyzed UK Biobank participants with genomic, proteomic, and metabolomic data. In a disease-free training subset, we used LASSO regression to identify multi-omic signatures associated with each pPS by jointly modeling proteins and metabolites. In an independent testing set, we constructed multi-omic scores and examined their associations with clinical traits and diabetes-related outcomes. Mediation analyses were used to investigate putative causal pathways. Key findings were evaluated in the Multi-Ethnic Study of Atherosclerosis (MESA). We identified distinct multi-omic signatures that capture the molecular architecture of T2D genetic risk across physiological subtypes. Compared with genetic scores alone, multi-omic pPS showed larger effect sizes and better disease discrimination. These scores recapitulated subtype-specific physiology and were associated with T2D risk. The Beta-Cell 2 multi-omic score showed marked stratification for insulin use, which was replicated in MESA, where it also predicted future insulin use. Mediation analyses implicated lipoprotein remodeling and fatty acid metabolism in the Lipodystrophy 1 cluster, accounting for 30-45% of the total effect of pPS on T2D risk. Integrating process-specific genetic risk with circulating multi-omic profiles reveals biologically distinct endotypes of T2D and supports a framework for improved patient stratification and risk assessment.

Journal Article↗

PubMed1979

Mutually exclusive genetic signatures of human breast tumor cell lines with a common chromosomal marker.

Seventeen recently established human breast carcinoma cell lines of metastatic origin (MDA series), HeLa cells, and MCF-7 (a well-established breast carcinoma cell line) were studied by starch gel electrophoresis for allozymic differences at 17 enzyme loci. Ten loci proved to be informative in establishing unique genetic signatures for all of the lines with the exception of MDA-134 and -309, which had the same genetic signatures. The probability of these latter two lines being of independent origin and finding their similar genetic signatures by chance is 0.07. These studies enable us to conclude that the chromosomal marker shown to be common to these breast carcinoma cell lines of metastatic origin is not present because of cross-contamination of the lines with other long-term lines or each other.

Breast Neoplasms↗

PubMed2025

Defining APOBEC-induced mutation signatures and modifying activities in yeast.

APOBEC cytidine deaminases guard cells in a variety of organisms from invading viruses and foreign nucleic acids. Recently, several human APOBECs have been implicated in mutating evolving cancer genomes. Expression of APOBEC3A and APOBEC3B in yeast allowed experimental derivation of the substitution patterns they cause in dividing cells, which provided critical links to these enzymes in the etiology of the COSMIC single base substitution (SBS) signatures 2 and 13 in human tumors. Additionally, the ability to scale yeast experiments to high-throughput screens allows use of this system to also investigate cellular pathways impacting the frequency of APOBEC-induced mutation. Here, we present validated methods utilizing yeast to determine APOBEC mutation signatures, genetic interactors, and chromosomal substrate preferences. These methods can be employed to assess the potential of other human APOBECs and APOBEC orthologs in different species to contribute to cancer genome evolution as well as define the pathways that protect the nuclear genome from inadvertent APOBEC activity during viral restriction.

Humans↗

PubMed2026

Genetic structure and selection signatures of Beijing-You chicken populations provide insight into breed conservation.

Preserving genetic diversity and maintaining population viability are critical yet challenging goals that demand rigorous evaluation of conservation strategies. Beijing-You chicken, as the sole indigenous chicken breed originating from Beijing, China, is currently maintained as four independent populations under distinct conservation programs. How different conservation regimes have shaped its genomic architecture remains largely unknown, limiting evidence-based evaluation. Here, we generated whole-genome resequencing data from 240 individuals representing four Beijing-You chicken populations to assess population structure, genetic diversity, and signatures of selection over decades of conservation. All four populations formed distinct clusters, reflecting measurable differentiation after decades of separate conservation. The differences in genetic diversity were broadly consistent with the variation in effective population size estimates. Runs of homozygosity and linkage disequilibrium decay patterns further characterized each population, with extended values indicating reduced effective population size and increased inbreeding under long-term conservation. We applied the fixation index (FST) and pairwise diversity ratio (θπ) methods to identify selection signatures. A total of 171 genes were identified as candidates. These genes were enriched in pathways related to reproduction, growth regulation, and environmental adaptation. These findings highlight patterns of reduced diversity and skewed relatedness, which could arise from management-related factors such as breeding preferences or mating strategies. Still, they are also compatible with neutral processes, including drift and founder effects. Regardless of the underlying cause, integrating scientifically informed conservation strategies with routine genomic monitoring across generations is essential for sustaining genetic diversity in Beijing-You chicken and other indigenous breeds.

Beijing-You chicken↗

PubMed2026

A machine learning-derived and functionally validated circadian rhythm signature predicts clinical outcomes and in silico drug sensitivity in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) displays considerable heterogeneity in clinical outcomes, highlighting the need for reliable prognostic biomarkers. While the aberrant expression of circadian rhythm-related genes has been implicated in cancer pathogenesis, its comprehensive role in CRC progression and predicted therapeutic vulnerabilities remains inadequately characterized. METHODS: Bulk and single-cell RNA-sequencing data were integrated from multiple CRC cohorts. A circadian rhythm signature (CRS) was developed through machine learning algorithms and validated for prognostic value. Comprehensive analyses of tumor microenvironment, genomic alterations, and drug sensitivity were performed. Furthermore, the biological function of the core gene, BHLHE40, was validated in CRC cell lines through CCK-8, EdU, and wound healing assays. RESULTS: Single-cell analysis demonstrated an elevated expression signature of circadian rhythm-related genes in dendritic cells. The optimized CRS, comprising 14 circadian rhythm-related genes, successfully categorized patients into high- and low-risk groups. Patients with a high CRS showed markedly poorer overall survival and computationally inferred immunosuppressive features, including reduced CD8+ T cell infiltration and increased M2 macrophage polarization. Genomic analysis revealed enhanced mutation burden in TP53 and alterations in RTK-RAS/WNT pathways. Notably, in vitro assays confirmed that BHLHE40 is significantly overexpressed in CRC cells. Knockdown of BHLHE40 markedly inhibited tumor cell proliferation and migration. Drug sensitivity profiling identified bexarotene and SMER-3 as potential therapeutic options for high-CRS patients. A nomogram integrating CRS with clinical parameters demonstrated superior predictive accuracy for 1-, 3-, and 5-year survival. CONCLUSIONS: The CRS represents a promising prognostic biomarker that reflects tumor immune status and genomic features, providing valuable insights for personalized treatment strategies in CRC.

Circadian rhythm↗

PubMed2026

Mutational signature stratification of recurrent gliomas reveals distinct patterns of genomic traits.

BACKGROUND: Although temozolomide (TMZ) is widely used for glioma treatment, its therapeutic benefit is limited by acquired resistance and recurrence, facilitated by intratumor heterogeneity. Mutational signatures (MSs) inform tumor evolution and reveal alterations associated with treatment response. METHODS: We performed molecular analyses of 96 glioma recurrences with sufficient private single-nucleotide variants relative to their matched primary tumors, stratified by their dominant MS. RESULTS: Four groups were identified: MS11/TMZ-related (n = 38), MS1/5/aging-related (n = 32), MS6/15/21/26/microsatellite instability (MSI)-related (n = 13), and other MS-related recurrences (n = 13). MS11/TMZ-related recurrences showed higher acquired mutational counts than the other groups (1338 vs 59 (MS1/5/aging) vs 57 (MS6/15/21/26/MSI) vs 57 (other MSs); P < .01). Mutations in SYNE2, SZT2, and FBN3 were restricted to recurrences with dominant or second-dominant MS11/TMZ-related signature (n = 41), and 85% (35/41) harbored mutations in these genes. In MS11/TMZ-related recurrences with RNA sequencing data (n = 17), mRNA co-expression analyses identified SYNE2-ATAD5 and SZT2-MAPKBP1 associations. Among MS11/TMZ-related recurrences, MS23 was frequent (44%, 18/41) and associated with higher acquired mutational counts (2089 vs 1188; P = .018) and more IDH-wildtype tumors (67% vs 30%; P = .037). MAPKBP1 mutations were enriched in MS23-positive recurrences (56% (10/18) vs 0% (0/23); P < .001). MS1/5/aging-related recurrences showed more frequent acquired chromosome 16q losses (22% vs 8% (TMZ) vs 0% (MSI) vs 0% (other); P < .05), which were associated with an increased fraction of genome altered relative to 16q-diploid cases (15% vs 7%; P = .01). CONCLUSIONS: These findings show that MS-based stratification of recurrences refines molecular characterization after therapy and nominates candidate biomarkers and pathways for functional studies of treatment-associated glioma evolution.

mutational signatures↗

PubMed2026

Tertiary lymphoid structure transcriptomic signatures show limited and cohort-dependent value for predicting axillary nodal involvement in oestrogen receptor-positive luminal breast cancer.

Tertiary lymphoid structures (TLS) are associated with prognosis in solid tumours. Their value for predicting axillary nodal involvement in oestrogen receptor-positive luminal breast cancer remains uncertain. Three published TLS signatures were scored by single-sample gene set enrichment analysis in oestrogen receptor-positive luminal tumours. The Cancer Genome Atlas Breast Invasive Carcinoma cohort (TCGA-BRCA) included 632 cases, of which 379 met strict consensus. METABRIC included 1086 cases, of which 663 met strict consensus. Logistic models adjusted for age and pathological tumour stage. Strict consensus, majority vote, and continuous scores were compared. Performance assessment included bootstrapped changes in area under the receiver-operating-characteristic curve, Brier scores, calibration, and decision-curve analysis. Survival was evaluated in METABRIC and explored in TCGA-BRCA. Strict-consensus TLS status was not associated with nodal positivity in TCGA-BRCA (adjusted odds ratio: 0.95, 95% confidence interval: 0.62-1.45, P = 0.822). METABRIC was similar (odds ratio: 0.76, 95% confidence interval: 0.55-1.06, P = 0.105). Full-cohort METABRIC analyses detected small majority-vote and continuous-score associations, absent in TCGA-BRCA. Across specifications, bootstrapped changes in area under the receiver-operating-characteristic curve ranged from 0.0002 to 0.0089, with minimal Brier-score improvement and no stable decision-curve benefit. In METABRIC, the univariable overall survival association attenuated after age adjustment (hazard ratio: 1.33-1.10). TCGA-BRCA survival analyses were nonsignificant. TLS transcriptomic signals showed small, cohort-dependent associations with nodal status but no reproducible or clinically meaningful incremental predictive value. These data do not support replacing sentinel lymph node biopsy with a TLS signature in oestrogen receptor-positive luminal breast cancer.

breast cancer↗

PubMed2026

scRNA-seq and bulk RNA-seq reveal the characteristics of macrophage copper metabolism and establish a risk signature in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with an urgent need for improved prognostic stratification and treatment-response prediction. This study aimed to explore a macrophage copper metabolism-associated prognostic model and to investigate the relationship between this risk model and the tumor immune microenvironment. METHODS: The FindClusters function was used to analyze cell clusters, and CellChat and CellPhoneDB/LIANA were employed for cell-cell communication analysis. Copper metabolism-related genes were sourced from the MSigDB database. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis, and a nomogram was constructed by integrating the prognostic model with clinicopathological factors. Additional analyses were performed to map the seven model genes in single-cell data, assess model uncertainty and robustness, evaluate macrophage/copper/cuproptosis-related transcriptional programs, and examine the correlations between risk score, immune infiltration and predicted drug sensitivity. RESULTS: Using single-cell RNA sequencing (scRNA-seq) data, we identified four macrophage subpopulations. Macrophages with high SPP1 expression showed close interaction with T cell populations and were associated with copper ion metabolism. By incorporating 141 copper metabolism-related genes and using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a seven-gene risk prediction model. Additional single-cell mapping showed that the model genes were detectable in the HCC single-cell dataset and showed a macrophage-associated expression pattern. The model showed moderate prognostic discrimination in TCGA-LIHC, whereas its external performance was heterogeneous and remained evaluable across external cohorts, with performance varying among datasets. Immune and mechanism-related analyses suggested that the risk signature was associated with macrophage-related infiltration, copper metabolism and cuproptosis-related transcriptional programs. Drug sensitivity analysis nominated Daporinad as a computationally predicted candidate compound, supporting Daporinad as a pharmacogenomic candidate for follow-up investigation. CONCLUSIONS: By integrating scRNA-seq and bulk RNA sequencing (RNA-seq) data, we constructed a macrophage copper metabolism-associated prognostic signature for HCC. The risk score was associated with survival, immune microenvironment features and predicted drug response, providing a transcriptomic framework for risk stratification and therapeutic hypothesis generation.

Hepatocellular carcinoma (HCC)↗

PubMed2026

Mitochondria related gene signature serves as prognosis prediction and risk stratification of cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CHOL) is a highly aggressive biliary malignancy with poor clinical outcomes and limited effective prognostic biomarkers. Mitochondrial dysfunction participates in multiple oncological processes of CHOL, yet the prognostic roles of mitochondria‑related genes (MRGs) remain poorly understood. This study aimed to characterize MRGs expression in CHOL and develop a molecular prognostic model for predicting patient survival and guiding clinical management. METHODS: RNA sequencing (RNA-seq) and clinical data of CHOL were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) (GSE89748) databases. Differentially expressed MRGs were identified, and 10 machine learning algorithms were used to construct prognostic models. The optimal model (highest average C-index) was selected to establish a mitochondria-related risk score (MRRS), which was validated internally and externally. A nomogram integrating clinical factors and MRRS was developed, and biological mechanisms were explored via functional and immune analyses. RESULTS: A 3-MRG (MAP3K1, MRPL18, PYGB) prognostic signature was constructed, stratifying patients into high- and low-risk groups with significantly different overall survival. The model showed high predictive accuracy, with an area under the curve (AUC) up to 0.845, and MRRS was an independent prognostic factor. The signature was associated with mitochondrial pathways, and the high-risk group had distinct immune infiltration and mutation profiles. CONCLUSIONS: A validated MRG prognostic model effectively stratifies CHOL patients and has potential clinical value for prognosis prediction. Further validation in larger cohorts is needed to confirm its applicability.

Cholangiocarcinoma (CHOL)↗

PubMed2026

A machine learning-derived intratumoral heterogeneity-related signature predicts the prognosis for and therapeutic response in patients with skin cutaneous melanoma.

BACKGROUND: Reliable biomarkers for predicting prognosis and therapeutic response in skin cutaneous melanoma (SKCM) remain limited. This study aimed to develop an intratumoral heterogeneity (ITH)-related prognostic signature for SKCM using integrative machine learning. METHODS: RNA sequencing (RNA-seq) data from 472 SKCM patients in The Cancer Genome Atlas (TCGA) and 214 patients in the GSE65904 cohort were analyzed. ITH scores were calculated using the DEPTH2 algorithm. Differentially expressed genes (DEGs) were identified between high- and low-ITH groups [|log2fold change (FC)| ≥1, false discovery rate (FDR) <0.05]. Based on 38 prognostic DEGs identified by univariate Cox regression, we employed an integrative framework of 101 machine learning algorithm combinations to construct prognostic models in the TCGA training cohort. The model with the highest average concordance index (C-index) was validated in the GSE65904 cohort and selected as the prognostic ITH-related signature (PIRS). Associations of the PIRS risk score with tumor mutational burden (TMB), immune cell infiltration, immune checkpoint gene expression, and drug sensitivity were systematically evaluated. Model performance was assessed using receiver operating characteristic (ROC) curves and Cox regression analyses. RESULTS: A 38-gene PIRS was constructed using the plsRcox algorithm. Patients with high PIRS risk scores exhibited significantly poorer overall survival (OS) in both the TCGA and Gene Expression Omnibus (GEO) cohorts. The PIRS was identified as an independent prognostic factor, with area under the curve (AUC) values of 0.779, 0.734, and 0.756 for 1-, 3-, and 5-year survival, respectively. High-risk samples displayed significantly lower TMB (P<0.05), reduced immune and stromal cell infiltration (P<0.001), downregulated immune function, and decreased expression of immune checkpoint genes. Additionally, high- and low-PIRS risk score groups exhibited distinct sensitivity patterns to different classes of targeted agents. CONCLUSIONS: The machine learning-derived PIRS robustly predicts prognosis in SKCM patients. Its clinical application is promising for optimizing patient risk stratification and treatment decisions, though further prospective validation is warranted.

Skin cutaneous melanoma (SKCM)↗

PubMed2026

Dietary Polyphenol Acteoside-Related Molecular Signatures in Clear Cell Renal Cell Carcinoma: Multi-Omics Profiling and Functional Validation of IMPDH1.

Clear cell renal cell carcinoma (ccRCC) is characterized by substantial metabolic and molecular heterogeneity, but the disease-relevant programs associated with acteoside, a dietary polyphenol, remain poorly understood. We integrated predicted acteoside targets with bulk, single-cell, and spatial transcriptomic data from ccRCC and combined molecular subtyping with cross-cohort machine-learning analysis. Acteoside-related signatures were preferentially enriched in malignant compartments and increased with tumor grade and stage. Consensus clustering identified two molecular subtypes with distinct biological and clinical features. C1 was associated with immune activation, metabolic activity, and more favorable survival, whereas C2 showed greater genomic instability, reduced renal epithelial differentiation, and poorer outcomes. We further benchmarked multiple machine-learning strategies and established a 10-gene prognostic model that retained predictive performance across independent cohorts, with IMPDH1 emerging as the strongest risk-associated feature. Functional experiments confirmed the biological relevance of IMPDH1: its knockdown suppressed ccRCC cell proliferation, DNA synthesis, colony formation, and migration, whereas overexpression produced the opposite effects. Together, these findings indicate that acteoside-related molecular signatures capture clinically relevant heterogeneity in ccRCC and provide a framework for linking dietary-polyphenol-related molecular space with tumor biology. The identification and functional validation of IMPDH1 further highlight its potential importance in ccRCC progression.

IMPDH1↗

PubMed1992

Amino acid binding by the class I aminoacyl-tRNA synthetases: role for a conserved proline in the signature sequence.

Although partial or complete three-dimensional structures are known for three Class I aminoacyl-tRNA synthetases, the amino acid-binding sites in these proteins remain poorly characterized. To explore the methionine binding site of Escherichia coli methionyl-tRNA synthetase, we chose to study a specific, randomly generated methionine auxotroph that contains a mutant methionyl-tRNA synthetase whose defect is manifested in an elevated Km for methionine (Barker, D.G., Ebel, J.-P., Jakes, R.C., & Bruton, C.J., 1982, Eur. J. Biochem. 127, 449-457), and employed the polymerase chain reaction to sequence this mutant synthetase directly. We identified a Pro 14 to Ser replacement (P14S), which accounts for a greater than 300-fold elevation in Km for methionine and has little effect on either the Km for ATP or the kcat of the amino acid activation reaction. This mutation destabilizes the protein in vivo, which may partly account for the observed auxotrophy. The altered proline is found in the "signature sequence" of the Class I synthetases and is conserved. This sequence motif is 1 of 2 found in the 10 Class I aminoacyl-tRNA synthetases and, in the known structures, it is in the nucleotide-binding fold as part of a loop between the end of a beta-strand and the start of an alpha-helix. The phenotype of the mutant and the stability and affinity for methionine of the wild-type and mutant enzymes are influenced by the amino acid that is 25 residues beyond the C-terminus of the signature sequence.(ABSTRACT TRUNCATED AT 250 WORDS)

Amino Acid Sequence↗

PubMed2024

Cross-platform proteomics signatures of extreme old age.

In previous work, we used a SomaLogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood generated in an independent set. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals. The comparison with blood transcriptomics also highlights a possible role for neutrophil degranulation in aging.

Humans↗