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PubMed2026

Exploring the Role of HSD17B2 in Colorectal Cancer Through Bioinformatic Analysis: Preliminary Insights for Prognostic Evaluation.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related mortality worldwide. Although screening has reduced CRC in older adults, cases in younger individuals are rising, highlighting the need for early biomarkers. Emerging research highlights the role of estrogen metabolism in CRC progression, with enzymes such as hydroxysteroid (17-beta) dehydrogenase (HSD17B) being increasingly implicated. In this study, we performed a bioinformatics analysis using publicly available datasets, including The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) cohort and two independent Gene Expression Omnibus (GEO) cohorts (GSE40967 and GSE41258), to investigate the role of HSD17B enzymes in CRC. Our results suggest that HSD17B2 is frequently downregulated in precancerous lesions and early-stage CRC, which may contribute to elevated estradiol levels and a tumor-promoting microenvironment. In advanced stages, higher HSD17B2 expression levels are associated with poorer survival outcomes in retrospective cohorts. Other HSD17B enzymes also exhibit significant expression changes, further complicating the hormonal landscape of CRC. In addition, estrone, traditionally considered a weaker estrogen, emerges as a potential driver of CRC progression. Our in-silico analyses indicate that HSD17B2 and HSD17B11 warrant further investigation as candidate biomarkers for distinguishing CRC from benign and precancerous conditions, with the combination showing strong discriminatory power in Receiver Operating Characteristic (ROC) analyses. Overall, these findings highlight the potential role of estrogen metabolism in CRC and suggest that HSD17B enzymes may hold value as candidate prognostic and diagnostic indicators, though their clinical utility remains hypothetical at this stage. Experimental and clinical validation is strictly required to confirm these in silico observations and to clarify their mechanisms in CRC.

Humans↗

PubMed2026

Artificial intelligence in molecular diagnostics for pandemic preparedness.

INTRODUCTION: Molecular diagnostics focusing on the detection and analysis of nucleic acids are indispensable tools for early pathogen identification, transmission monitoring, and genomic surveillance during pandemics. Recent technological advances have broadened the diagnostic landscape, incorporating PCR-based methods, isothermal amplification, high-CRISPR-based amplification detection, and sequencing. Despite their diagnostic potential, widespread implementation remains limited by high validation costs, time and logistical constraints, the need for specialized professional knowledge, and a lack of adaptability in resource-limited settings. Artificial intelligence (AI) is increasingly recognized as a promising but challenging approach, offering tools that streamline assay development, automate data interpretation, and optimize real-time diagnostic performance. AREAS COVERED: This review introduces recently published AI tools with potential to enhance the in-silico design validation process of oligonucleotides for molecular assays. These cover tools for initial assay design and optimization to validation and continuous assay updates. The limitations, including concerns regarding data accuracy, the lack of transparency in data processing ('black box' models), and unresolved licensing and regulatory issues, are highlighted for each tool and as expert opinion. EXPERT OPINION: Collectively, these challenges currently confine most AI-based approaches to research settings and prevent their routine implementation in clinical molecular diagnostics. Their widespread adoption depends on addressing remaining technical, regulatory, and practical challenges.

Humans↗

PubMed2026

Metabolic-cell-death gene trio predicts survival and cuproptosis sensitivity in colorectal cancer.

BACKGROUND: Metabolic cell death (MCD) modulates colorectal cancer (CRC) progression, yet its prognostic value remains unexplored. We aimed to build an MCD-centred gene signature for outcome prediction and precision therapy. METHODS: Transcriptomes of 1,174 CRC patients were integrated. Weighted gene co-expression network analysis, differential expressions and least absolute shrinkage and selection operator (LASSO) + random survival forest were successively applied to derive a three-gene (CDKN2A/MPC1/AHCY) risk model. Functional, immune-infiltration, drug-sensitivity and genomic analyses were performed, followed by validation in fresh clinical specimens and cell lines. RESULTS: Integrative metabolic-death transcriptomics identified CDKN2A, MPC1 and AHCY as the hub drivers of CRC. Their three-gene signature robustly stratified patients into high- and low-risk subsets [3-year area under the curve (AUC) 0.83-0.85, P<0.001]. High-risk tumors were enriched for extracellular matrix (ECM)-receptor-interaction pathways, displayed abundant myeloid-derived suppressor cell (MDSC) infiltration and were more vulnerable to AZD8186, AZ960 and JAK inhibitors. Guided by these in-silico findings, we functionally confirmed that CDKN2A silencing markedly repressed proliferation, invasion and migration of SW480/HCT116 cells and potentiated cuproptosis via up-regulation of lipoylated DLAT/DLST and CTR1. CONCLUSIONS: We report the first MCD-derived prognostic platform for CRC that simultaneously predicts survival and therapeutic response. Targeting CDKN2A-enhanced cuproptosis represents a promising metabolic-precision strategy for high-risk patients.

Colorectal cancer (CRC)↗

PubMed2026

Genomic and computational analysis of variants in telomere regulatory genes in subjects with bone marrow failure.

Telomere Biology Disorders (TBDs) are a genetically heterogeneous and often under-recognized cause of Bone Marrow Failure Syndromes (BMFS), driven by defective telomere maintenance and progressive telomere attrition. We performed an integrated genomic, telomeric and computational analysis in 118 subjects presenting clinical features of BMFS to delineate the contribution of Telomere Regulatory Genes (TRGs) variants to disease pathogenesis. Whole exome sequencing (WES) identified pathogenic (18.18%), likely pathogenic (27.27%) and rare variants of uncertain significance (54.54%) in 27 subjects (22.9%) across five TRGs: RTEL1, TERT, TINF2, NOP10, and WRAP53. Telomere Length (TL) assessment revealed significant telomere shortening in TRG variant-positive subjects compared with age-matched controls, with the most profound attrition observed in individuals harboring de novo TINF2 gene variants. RTEL1 emerged as the most frequently affected gene, with recurrent clustering of variants within its C-terminal regulatory region. A familial NOP10 variant, Asp12His, segregated with cutaneous pigmentation and hematological abnormalities consistent with the established role of NOP10 in dyskeratosis congenita, further broadening the known mutational spectrum of the gene. Structure-guided in-silico analyses predicted that both novel and recurrent variants disrupt protein stability, telomerase assembly or trafficking and shelterin complex integrity. Reduced TERT expression and a significant inverse correlation between telomere length and clinical severity further underscored the functional impact of TRG defects. Collectively, this study provides the first comprehensive characterization of TRG variants in the Indian BMFS cohort and highlights the utility of integrating genomic sequencing, telomere length measurement and computational modeling to improve diagnostic precision, variant interpretation and clinical stratification in TBDs.

Journal Article↗

PubMed2026

In-Silico and Functional Characterization of EcdLp, an ABC Transporter of Aspergillus nidulans NRRL11440.

Echinocandin B (ECB) biosynthesis in Aspergillus nidulans is primarily governed by multiple genes located within the biosynthetic echinocandin (ecd) gene cluster. The contributory functions of many genes, including transcription factors and tailoring enzymes of the ecd gene cluster, have been previously studied. The present study focused on determining the role of transporter proteins, EcdLp, EcdCp, and EcdDp, in ECB efflux using in silico and biochemical approaches. The molecular docking analysis revealed that ECB relatively showed higher binding affinity for EcdLp than the other co-clustered MFS transporters EcdCp and EcdDp, suggesting a preferred substrate of EcdLp. These results were further confirmed by heterologous integration of the ecdL gene in the ABC transporters-deficient Saccharomyces cerevisiae AD1-8u⁻, confirming active efflux. However, the binding of ECB in EcdLp is distinct from the R6G binding, overlapping the promiscuous site of farnesol, resulting in inhibition of R6G efflux in a dose-dependent manner. In conclusion, these results decipher the ECB binding and efflux mechanism and unveil the evolutionarily specialized architecture of EcdLp that permits targeted metabolite export in addition to environmental responsiveness, and lay the groundwork for optimizing ECB production via transporter engineering.

Aspergillus nidulans↗