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PubMed2026

Proteomic Signatures Related to Physical Activity Are Associated with Risks of Future Disease.

PURPOSE: Physical activity (PA) can lower the risk of developing chronic diseases. However, few studies have examined the proteomic signatures linked to PA, and the role of these signatures in the connection between PA levels and future disease risk remains unclear. This study aimed to investigate whether proteomic signatures indicative of PA are associated with the risk of developing common chronic diseases and to explore their role as statistical links in the relationship between PA levels and disease development. METHODS: We used data from a subcohort of UK Biobank participants. PA intensity data were collected from accelerometers worn by each participant. Plasma proteomics results were obtained through Olink analysis. The risks of developing each primary chronic disease were evaluated for types of PA and their associated proteomic signatures, adjusting for age, sex, ethnicity, socioeconomic status, lifestyle factors, and key measurement time-lag covariates. RESULTS: Based on the UK Biobank, we identified significant differences among the proteomic signatures of accelerometer-measured light PA, moderate-to-vigorous PA, and total PA. The main enriched pathways of these proteomic signatures included cell adhesion, cell migration, and immune response. Higher levels of accelerometer-measured PA and their associated proteomic signatures correlated with a lower risk of developing cardiometabolic disorders, cancers, psychological or neurological disorders, and respiratory diseases. CONCLUSIONS: Our findings show that PA and PA-related proteomic signatures are statistically associated with lower risks of chronic diseases. Further analyses identified proteins that were correlated with both PA and disease risk. These results need to be confirmed through longitudinal studies involving diverse populations.

Humans↗

PubMed1980

A molecular approach to the identification and individualization of human and animal cells in culture: isozyme and allozyme genetic signatures.

The electrophoretic resolution of a group of genetically monomorphic gene-enzyme systems that are developmentally and biologically ubiquitous has been used to provide a species-specific and type-specific biochemical characterization of various cultured cells. The relative mobilities of gene-enzyme systems representing nine distinct gene products from cell cultures of 25 species from Drosophila to man are presented. These isoenzymes effectively discriminate interspecies cell-to-cell contamination and almost invariably serve to identify the contaminating species. The resolution of eight polymorphic gene-enzyme systems in human cell cultures provides a virtually unique allozyme genetic signature as a monitor of intraspecies cellular contamination. The genetic signatures of 47 commonly used human cells are presented. Included in the test were seven putative HeLa (human cervical carcinoma) contaminants each of which expressed a signature identical with that of HeLa. The probability that an unrelated human cell line will have a signature identical to a typed cell is computed for each line from the genotypic frequencies at each locus in a population of cultured human cells. The gene frequencies of this cell population are comparable to the same frequencies in natural human populations. The most common human signature has a frequency (and therefore a probability) of 0.02. The majority of the 17,010 possible signatures are far less probable. A calculation of the theoretical incidence of chance matching of signatures within test groups of two or more individuals is presented. The probability of a chance match between any two randomly selected individuals is 0.004 and among five randomly selected individuals is 0.034. The allozyme genetic signature represents a definitive monitor of cell identity and is presented as a standard of cell and tissue identification for a variety of biological studies.

Animals↗

PubMed2026

Seqwin: ultrafast identification of signature sequences in microbial genomes.

MOTIVATION: Polymerase chain reaction (PCR) enables rapid, cost-effective diagnostics but requires prior identification of genomic regions that allow sensitive and specific detection of target microbial groups, herein referred to as microbial signature sequences. We introduce Seqwin, an open-source framework designed to automate microbial genome signature discovery. Tens of thousands of microbial genomes are now available for a single species, limiting the application of existing manual and automated approaches for identifying signatures. Modern approaches that are capable of leveraging all available microbial genomes will ensure sensitive and accurate DNA signature identification and enable robust pathogen detection for clinical, environmental, and public health applications. RESULTS: Seqwin builds weighted pan-genome minimizer graphs and uses a traversal algorithm to identify signature sequences that occur frequently in target genomes but remain rare in non-targets. Unlike earlier tools that depend on strict presence or absence of sequences, Seqwin accommodates natural sequence variation and scales to very large genome collections. When applied to genomes from C. difficile, M. tuberculosis, and S. enterica, Seqwin recovered more high-quality signatures than alternative methods with lower computational burden. Seqwin's analysis of nearly 15 000 S. enterica genomes yielded over 200 candidate signatures in three minutes. Seqwin provides an open-source solution for the long-standing need for scalable microbial signature discovery and diagnostic assay design. AVAILABILITY AND IMPLEMENTATION: Seqwin is available on GitHub (https://github.com/treangenlab/Seqwin) and can be installed via Bioconda (https://bioconda.github.io/recipes/seqwin/README.html). Benchmarking datasets, outputs, and scripts are available on Zenodo (https://doi.org/10.5281/zenodo.19874011).

Software↗

PubMed2026

Prognostic value and immune landscape implications of using a novel homologous recombination repair pathway signature in prostate cancer: A retrospective cohort study.

ObjectiveAlthough the homologous recombination repair (HRR) pathway plays a critical role in the treatment of prostate cancer, its prognostic value remains incompletely understood. This study aimed to identify HRR pathway-related biomarkers with clinical utility for prognosis prediction and treatment guidance.MethodsWe analyzed genomic data from The Cancer Genome Atlas and Chinese patients with prostate cancer in a retrospective cohort study using a comprehensive multiomics approach to characterize a novel HRR-related prognostic signature and its immune implications.ResultsIn the Chinese cohort, 25.6% of the patients exhibited homologous recombination deficiency scores >42, whereas 27.3% carried ≥1 HRR gene mutation. We established a prognostic HRR signature (homologous recombination deficiency score >32, HRR gene mutations, and Signature 3) associated with poor outcomes. Compared with The Cancer Genome Atlas data, the Chinese cohort demonstrated a higher prevalence of HRR signature. Patients with HRR signatures demonstrated significantly increased genomic instability markers, including segment number, alteration burden, aneuploidy score, and intratumor heterogeneity. The HRR signature was associated with higher neoantigen load but reduced T cell receptor (TCR) evenness. Immunologically, HRR-positive tumors were associated with computationally inferred immune profiles suggestive of reduced immune activity, characterized by depletion of T-helper 17 cell; downregulation of TLR4/PDCD1LG2 expression; and upregulation of ARG1, IFNG, KIR2DL3, and CXCL9. However, these findings are descriptive and require experimental validation.ConclusionOur findings identify a clinically relevant HRR signature that warrants investigation as a potential predictive biomarker for prostate cancer prognosis and treatment response. This biomarker provides new insights for personalized therapy and may help optimize patient outcomes.

Humans↗

PubMed2025

An anti-androgen resistance-related gene signature acts as a prognostic marker and increases enzalutamide efficacy via PLK1 inhibition in prostate cancer.

BACKGROUND: Anti-androgen resistance remains a major clinical challenge in the treatment of prostate cancer (PCa), leading to disease progression and treatment failure. Despite extensive research on resistance mechanisms, a reliable prognostic model for predicting patient outcomes and guiding therapeutic strategies is still lacking. This study aimed to develop a novel gene signature related to anti-androgen resistance and evaluate its prognostic and therapeutic implications. METHODS: Anti-androgen resistance-related differentially expressed genes (ARRDEGs) were identified through transcriptomic analysis of enzalutamide- and dual enzalutamide abiraterone-resistant PCa cell lines from the GEO database. Functional enrichment analysis was performed to determine the biological roles of these genes. A prognostic gene signature was developed using univariate Cox regression, LASSO, and multivariate Cox regression models. The model was validated in independent PCa cohorts from The Cancer Genome Atlas (TCGA). Additionally, we assessed the correlation between the signature, immune infiltration, immune checkpoint expression, and drug sensitivity. The efficacy of PLK1 inhibition combined with enzalutamide was further explored using in vitro and in vivo experiments. RESULTS: We identified 304 ARRDEGs, from which three key genes (LMNB1, SSPO, and PLK1) were selected to construct a prognostic signature. This gene signature effectively stratified PCa patients into high- and low-risk groups, with the high-risk group exhibiting shorter recurrence-free survival and distinct immune characteristics. High-risk patients demonstrated elevated immune checkpoint expression (B7H3, CTLA-4, B7-1, and TIGIT), increased M2 macrophage infiltration, and enhanced sensitivity to chemotherapy and targeted therapy. Mechanistically, PLK1 inhibition potentiated the antitumor effect of enzalutamide by downregulating SLC7A11 and inducing ferroptosis, providing a potential therapeutic strategy to overcome anti-androgen resistance. CONCLUSION: We established a novel ARRDEGs-based prognostic signature that predicts PCa progression and response to chemotherapy and targeted therapy. The integration of this signature with immune profiling and drug sensitivity analysis provides a valuable tool for precision oncology in PCa. Our findings highlight the potential of PLK1 inhibition as a therapeutic strategy to enhance enzalutamide efficacy and overcome resistance.

Humans↗

PubMed1995

Pattern recognition of amino acid signatures in retinal neurons.

Pattern recognition of amino acid signals partitions the cells of the goldfish retina into nine statistically unique biochemical theme classes and permits a first-order chemical mapping of virtually all cellular space. Photoreceptors, bipolar cells, and ganglion cells display a set of unique, nominally glutamatergic type E1, E1+E2, and E4 signatures, respectively. All horizontal cells are assignable to a GABAergic gamma 2 class or a non-GABAergic class with a glutamate-rich E3 signature. The amacrine cell layer is largely a mixture of (1) a taurine-dominated T1 Müller's cell signature and (2) GABAergic gamma 1, glycinergic G1, and dual glycinergic/GABAergic G gamma 1 amacrine cell signatures. Several major conclusions emerge from this work. (1) Glutamatergic, GABAergic, and glycinergic neural signatures and glial signatures account for over 99% of the cellular space in the retina. (2) All known neurons in the goldfish retina are associated with a set of conventional nonpeptide neurotransmitters. (3) Multiple forms of metabolic profiles are associated with a single nominal neurotransmitter category. (4) Glutamate and aspartate contents exhibit overlapping distributions and are not adequate univariate probes for identifying cell classes. (5) Signatures can serve as quantitative measures of cell state.

Amino Acids↗

PubMed2025

Glioma mutational signatures associated with haloalkane exposure are enriched in firefighters.

BACKGROUND: Glioma is the most common malignant primary brain tumor and is associated with significant morbidity and mortality. Modifiable risk factors remain unidentified. New advances in exposure assessment, genomic analyses, and statistical techniques permit more accurate evaluation of glioma risk associated with exogenous occupational or environmental exposures. METHODS: By using whole-exome sequencing data from matched germline and glioma tumor samples, the authors compared tumor mutational signatures for 17 persons with glioma and a documented occupational history of firefighting with those of 18 persons with glioma without an occupational history of firefighting. All 35 individuals were participants in the University of California, San Francisco Adult Glioma Study. RESULTS: There was a positive correlation among firefighters between the median number of sample variants attributable to single-base substitution signature 42, a single-base substitution mutational signature associated with haloalkane exposure (from the Catalogue of Somatic Mutational Signatures in Cancer) and firefighting years (p = .04; R2 = 0.29). Among nonfirefighters, the individuals with the highest number of median variants attributable to single-base substitution signature 42 also had occupations that possibly exposed them to haloalkanes, such as painting and being a mechanic. CONCLUSIONS: In summary, the authors identified gliomas that had mutational signatures associated with haloalkane exposure that were enriched in firefighters and other occupations.

Humans↗

PubMed2024

Construction of an immunogenic cell death-related LncRNA signature to predict the prognosis of patients with lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most common malignant diseases worldwide. This study aimed to construct an immunogenic cell death (ICD)-related long non-coding RNA (lncRNA) signature to effectively predict the prognosis of LUAD. METHODS: The RNA-sequencing and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA). Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox proportional hazard regression analysis were utilized to construct lncRNA signature. Then, the reliability of the signature was evaluated in the training, validation and whole cohorts. The differences in the immune landscape and drug sensitivity between the low- and high-risk groups were analyzed. Finally, the expression level of the selected ICD-related lncRNAs in LUAD cell lines via reverse transcription quantitative PCR (RT-qPCR). CCK-8 and transwell assays were performed to study biological function of AC245014.3. RESULTS: A signature consisting of 5 ICD-related lncRNAs was constructed. Kaplan Meier (K-M) survival analysis showed shorter overall survival (OS) in high-risk group. The receiver operating characteristic (ROC) curves and Multivariate Cox regression analysis showed the signature was good predictive and independent prognostic factor in LUAD. Moreover, the high-risk group had a lower level of antitumor immunity and was less sensitive to some chemotherapeutics and targeted drugs. Finally, the expression level of selected ICD-related lncRNAs was validated in LUAD cell lines by RT-qPCR. Knockdown of AC245014.3 significantly suppressed LUAD proliferation, migration and invasion. CONCLUSIONS: In this study, an ICD-related lncRNA signature was constructed, which could accurately predict the prognosis of LUAD patients and guide clinical treatment.

Humans↗

PubMed2025

TSC angiofibroma and ungual fibroma have different mutation signatures, with recurrent mutations in KMT2C.

PURPOSE: Tuberous sclerosis complex (TSC) is an autosomal dominant tumor suppressor syndrome characterized by tumors affecting multiple tissues, including skin, due to inactivating TSC1/TSC2 variants. Genome-wide profiling of somatic mutations in a unique collection of angiofibroma (FAF) and ungual fibroma (UF) TSC skin tumors was performed. METHODS: Genome sequencing was performed on 9 samples, comprising 4 FAF and 5 UF, along with 6 matched normal samples from 6 individuals with TSC. RESULTS: TSC-FAF and TSC-UF skin tumors have different mutation signatures, with a predominance of UV-related single-nucleotide variant (SNV; SBS7a and SBS7b) and dinucleotide variant (DNV; DBS1) signatures in FAF, and aging-related SNV (SBS1 and SBS5) signatures in UF. We also identified a novel DNV signature for TSC-UF, with frequent TG>CA and TT>GG substitutions. Furthermore, 3 inactivating somatic mutations in KMT2C were observed in 2 of 4 TSC-FAF and 5 mutations in other cancer genes. CONCLUSION: The distinct SNV mutation signatures seen in TSC-FAF and UF indicate that they develop through distinct pathogenic mechanisms, UV-induced mutagenesis in FAF, and aging-related mutagenesis in UF. The mechanism of the novel DNV signature in UFs merits further investigation. Our observation on the occurrence of KMT2C mutations suggests that KMT2C inactivation contributes to the pathogenesis of TSC-FAF.

Humans↗

PubMed2025

Identification and Validation of a Previously Missed Mutational Signature in Colorectal Cancer.

Mutational signature analysis has greatly enhanced our understanding of the mutagenic processes found in cancer and normal tissues. As part of a recent study, we analyzed 802 treatment-naïve, microsatellite-stable colorectal cancers (CRC) and identified a de novo signature, SBS_D, which was conservatively decomposed into SBS18, a signature associated with reactive oxygen species. Here, we re-evaluate this decomposition and provide evidence that SBS_D represents a distinct mutational process from that of SBS18. Through an independent analysis of 2,616 whole-genome sequenced microsatellite-stable CRCs across three distinct cohorts, we demonstrate that SBS_D is consistently present at a similar prevalence, suggesting that this signature may have been previously overlooked. Using a naïve decomposition approach, we demonstrate that the pattern of SBS_D better aligns with signatures previously associated with deficiencies in DNA polymerase delta (POLD1) proofreading and mismatch repair. However, multiple lines of evidence, including the absence of pathogenic mutations in the exonuclease domain of POLD1 or in mismatch repair-associated genes, indicate that SBS_D is not driven by canonical defects in these DNA repair pathways. Overall, this study identifies a previously unrecognized mutational signature in microsatellite-stable CRC and proposes that its etiology may be linked to DNA repair infidelity emerging late in tumor development in samples without canonical defects in DNA repair pathways.

Journal Article↗

PubMed2026

Bayesian Integration of Tumor Mutational Signatures and Somatic Features Refines Pathogenicity Assessment of Germline Mismatch Repair Variants.

Variants of uncertain significance (VUS) in mismatch repair (MMR) genes represent a persistent bottleneck in germline interpretation for Lynch syndrome, creating a critical opportunity to leverage tumor biology to refine pathogenicity assessment. Although tumor features such as microsatellite instability (MSI) and immunohistochemistry (IHC) are routinely evaluated, they are typically interpreted separately from germline classification, and their quantitative contribution within ACMG/AMP frameworks remains poorly defined. We therefore analyzed paired germline and tumor sequencing data from 1110 tumors across 1073 patients with colorectal or endometrial cancer to determine whether mismatch repair-deficient (MMR-d) mutational signatures can be quantitatively integrated into Bayesian germline variant interpretation. Using COSMIC single-base substitution signatures, tumors were classified as MMR-d or MMR proficient, and an empirically derived likelihood ratio (LR) quantified the association between MMR-d signatures and pathogenic germline MMR variants. The presence of an MMR-d signature increased the likelihood of an underlying pathogenic germline MMR variant approximately eightfold (LR ≈ 8; log10 LR ≈ 0.90), whereas its absence provided moderate-to-strong benign evidence (LR ≈ 0.156; log10 LR ≈ -0.81). Applying this integrative framework to 45 germline MMR VUS, joint modeling of tumor mutational signatures with additional somatic and variant-level evidence resulted in clinically significant reclassification of 38 (84.4%) variants, including three reclassified as pathogenic or likely pathogenic and 35 as likely benign. A total of 16 downgraded variants were independently downgraded by Invitae. These findings demonstrate that tumor mutational signatures can be formally incorporated into Bayesian germline interpretation, transforming tumor data into quantitative pathogenicity evidence and offering a principled strategy to reduce VUS burden in hereditary cancer genetics.

Humans↗

PubMed1984

Signature size in the psychiatric diagnosis: a significant clinical sign?

To test the hypothesis that patients' signatures may have a useful potential in making psychiatric diagnoses, the authors conducted a correlation study between signature sizes and psychiatric diagnoses. 252 medical records at St. Louis State Hospital in Missouri, USA, were randomized for the measurement of the signature sizes and assessment of DSM-III diagnoses. Analysis of variance and pair-wise comparison show that the signature size in the manic group is significantly larger than those of any other categories of psychiatric diagnoses (p less than 0.05), and that the signature size of organic mental disorder is significantly larger than those of the normal group (p less than 0.05). The authors suggest that further studies are needed to develop the clinical significance, if any, for interpreting patients' signatures.

Bipolar Disorder↗

PubMed1992

The pre-stenotic Doppler shift signature.

In two series of measurements the Doppler shift signatures of 10 healthy volunteers were studied at varying distances proximal to a total reflection site, in order to describe parameters which are predictive for downstream lesions. Characteristic changes both in amplitude and time parameters were found; the most marked changes being the abolishment of a DC-component for monophasic signatures, the development or the augmentation of early diastolic reverse flow amplitudes together with a highly significant reduction in the signatures' systolic deceleration time. Maximum changes however tended to be localized at 2 to 4 centimeters upstream from the reflection site. Further upstream propagation was limited. Hence the time course of a Doppler signature and particularly its systolic deceleration should be taken into account in addition to the known resistance indices, if downstream lesions shall be predicted from upstream Doppler signatures. The limited upstream propagation of pre-stenotic Doppler signature changes restricts its diagnostic value to vascular segments where the region adjacent to a lesion is routinely scanned; thus diagnostic benefit can be expected for extracranial carotid artery disease but hardly for peripheral (lower limb) lesions.

Adult↗

PubMed2026

An oxidative stress - and immunotherapy-related six-gene signature defines immune subtypes and predicts prognosis and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Oxidative stress and the tumor immune microenvironment jointly shape hepatocellular carcinoma (HCC) progression and response to immunotherapy, yet integrated biomarkers linking these processes are lacking. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to identify oxidative stress- and immunotherapyrelated differentially expressed genes (OSIRDEGs). Functional enrichment, weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression were used to construct a prognostic signature. Consensus clustering, TIDE, CIBERSORT and ssGSEA characterized immune phenotypes. Somatic mutation, copy-number and drug-response data were integrated to assess genomic alterations and drug sensitivity. Expression of model genes was validated by qRT-PCR and western blotting in HCC cell lines. RESULTS: We identified 24 OSIRDEGs enriched in cell-cycle and mitotic pathways. WGCNA intersection yielded 18 module genes, from which a six-gene signature (BUB1B, CDKN2A, CENPE, HMMR, PTTG1, SPP1) was derived. The signature robustly stratified patients into high- and low-risk groups with significantly different progression-free and disease-free survival in both TCGA-LIHC and GSE14520. Based on signature expression, two molecular subtypes were defined, exhibiting distinct survival, immune landscapes and predicted immunotherapy responsiveness. Model genes harbored recurrent alterations and showed significant correlations with anticancer agents. All six genes were upregulated at mRNA and protein levels in metastatic HCC cell lines versus normal hepatocytes. CONCLUSIONS: We systematically explored the landscape of OSIRDEGs in HCC, and proposed a validated six-gene signature that refines prognostic stratification, delineates immunerelevant HCC subtypes and highlights candidate biomarkers for therapeutic selection and mechanistic investigation.

Humans↗

PubMed2026

Construction of molecular signatures based on the co-expression network of NECSO-related gene TRPM4 and its prognostic value in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) demonstrates significant prognostic variability that is not entirely accounted for by traditional staging systems. Necrosis by sodium overload (NECSO) is an emerging programmed cell death pathway, but its clinical relevance in HCC remains undefined. Therefore, this study aimed to identify TRPM4-associated core genes, develop and validate a prognostic signature, and investigate its relationship with the tumor immune microenvironment, tumor mutational burden, and single-cell expression patterns in HCC. METHODS: We integrated transcriptomic, clinical, and mutational datasets from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) (n=421) and Gene Expression Omnibus (GEO) cohorts (n=115) to identify genes co-expressed with TRPM4-a key NECSO mediator-and those differentially expressed in HCC. A prognostic signature was developed using least absolute shrinkage and selection operator (LASSO)-Cox regression and validated through survival analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox regression analysis. The immune landscape was characterized using CIBERSORT, somatic mutation data were used to calculate tumor mutational burden (TMB) and assess its correlation with the risk score, and single-cell RNA sequencing (scRNA-seq) resolved cell-type-specific expression patterns. RESULTS: From 294 TRPM4-associated core genes, we identified an 11-gene signature (BRSK1, MMP1, GRIN2D, GP6, MYOM2, N4BP3, CCDC112, TSEN54, MAP3K9, SPP1, B3GNT4) that independently predicted overall survival (OS) (hazard ratio =5.419, P<0.001) with areas under the curve (AUCs) of 0.779, 0.693, and 0.701 at 1, 3, and 5 years. These values were superior or comparable to conventional clinicopathologic variables after direct comparison. High-risk patients exhibited an immunosuppressive microenvironment, characterized by enrichment of M0 macrophage, a higher M2/M1 ratio (P<0.001) and distinct immune checkpoint profiles. When integrated with TMB, the prognostic stratification was further refined: high-TMB/high-risk patients had poorest outcomes (median OS, 15.3 months), while low-TMB/low-risk patients had the most favorable survival (median OS, 68.7 months). Single-cell analysis revealed that MMP1 was induced in cancer-associated fibroblasts (CAFs) and SPP1 was downregulated in macrophages, single-cell risk scores confirmed TAFs and macrophages as the main contributors to the prognostic model. CONCLUSIONS: The TRPM4-centered 11-gene signature provides robust and independent prognostic stratification in HCC by integrating immune, mutational, and single-cell features. This signature serves as a potential tool for prognostic evaluation and may help inform immunotherapeutic strategies for HCC.

Hepatocellular carcinoma (HCC)↗

PubMed2026

Thyroid hormone deprivation creates an immunological signature in the mouse liver, involving Kupffer cell presentation as the mouse ages.

PURPOSE: Aging is associated with an increased prevalence of chronic liver diseases suggesting impaired immune and metabolic function. In addition, thyroid hormone (TH) impacts liver physiology and TH deprivation or excess negatively affect organ maintenance. However, whether age-dependent consequences of TH alterations are reflected in a liver-specific adaptation is unknown so far. The present study aimed to characterize the impact of TH deprivation or excess on the liver transcriptome during aging. METHODS: Five- and 21-month-old male C57BL/6 mice were exposed either to chronic TH deprivation or to chronic TH excess and compared to control treatment by microarray-based liver transcriptome analysis. RESULTS: Significant roles of both TH state and age became obvious: Bioinformatic analysis of the liver transcriptome data revealed an age-dependent immune signature by chronic TH deprivation, an age-dependent immune and metabolic signature independent of exogenous TH modulation, as well as an age-dependent metabolic signature by chronic TH excess. Published data of single cell transcriptomic atlas characterizing aging tissues in the mouse were compared with our data and revealed Kupffer cell presentation in the immunological signature by TH deprivation during aging. Literature data for four prominent differentially expressed genes, namely C1qb, C3ar1, Ctss, and Msr1, revealed that the complement system, extracellular matrix remodelling, as well as the proinflammatory phenotype of Kupffer cells are altered by TH deprivation during aging. CONCLUSION: In conclusion, our study illuminates the interplay between TH deprivation, aging, and liver transcriptome signatures, highlighting potential implications for immune function and tissue maintenance, particularly through the modulation of Kupffer cell presentation.

Animals↗

PubMed2026

Identification and validation of a previously missed mutational signature in colorectal cancer.

Mutational signature analysis has enhanced our understanding of mutagenic processes. In a recent study, we analyzed 802 microsatellite-stable colorectal cancers (CRC) and identified a de novo signature, SBS_D, which was decomposed into SBS18. Here, we re-evaluate this decomposition and provide evidence that SBS_D represents a distinct mutational process from SBS18. Through an analysis of 2,616 CRCs across three independent cohorts, we demonstrate that SBS_D is consistently present, suggesting this signature may have been previously overlooked. We illustrate that the pattern of SBS_D better aligns with signatures associated with deficiencies in DNA repair, despite evidence that SBS_D is not driven by canonical defects in these DNA repair pathways. Overall, this study identifies a previously unrecognized mutational signature in DNA repair-proficient CRC and proposes that its etiology may be linked to DNA repair infidelity emerging late in tumor development. SBS_D has been submitted to the COSMIC database and provisionally designated as SBS111.

Colorectal Neoplasms↗

PubMed2026

Machine learning-based analysis of oral rinse samples to identify candidate proteomic signatures for severe periodontitis: a pilot study.

This pilot study investigated whether candidate protein signatures from oral rinse samples can distinguish patients with severe periodontitis (stage III/IV) and its subtypes, generalized and localized periodontitis, from non-periodontitis controls. Participants rinsed with phosphate-buffered saline, and samples were analyzed using a Proximity Extension Assay targeting 92 inflammatory and 92 immuno-oncology proteins. A machine learning approach using repeated nested cross-validation and SHAP was implemented to identify protein signatures. The study included 38 patients (18 with localized periodontitis and 20 with generalized periodontitis) and 16 controls. After data preprocessing, 54 samples and 141 proteins were retained. Proteins Gal-1, HGF, TNFSF14, CD27, and ARG1 distinguished periodontitis from controls (ROC-AUC = 0.85, 95% CI 0.82, 0.87). For generalized periodontitis, we found a protein signature including TNFSF14, Gal-1, STAMBP, MUC-16, S100A12, HGF, CASP-8, CD27, LAP TGF-β1, TNFRSF9, and uPA (ROC-AUC = 0.92, 95% CI 0.90, 0.94). For localized periodontitis, we identified ARG1 (ROC-AUC = 0.72, 95% CI 0.68, 0.76). No proteomic signature distinguishing generalized periodontitis from localized periodontitis was identified. This pilot study indicated that oral rinses are suitable for proteomic profiling, and there was a putative protein signature that could differentiate periodontitis, generalized periodontitis, and localized periodontitis from controls. These findings warrant validation in larger independent cohorts, including a clearly defined gingivitis group, before real-world non-invasive screening applications can be considered.

Humans↗