J Cancer. 2025 Jan 13;16(4):1243-1263. doi: 10.7150/jca.104424. eCollection 2025.

ABSTRACT

Background: This study aimed to investigate glycogen metabolism in gastric cancer (GC) and develop a glycogen-based riskScore model for predicting GC prognosis. Methods: Patients' expression profiles for 33 tumor types were retrieved from TCGA. Four GC bulk and one single-cell sequencing datasets were obtained from GEO database. This study also enrolled a bladder urothelial carcinoma immunotherapeutic IMvigor210 cohort. The ssGSEA method was conducted to assess glycogen biosynthesis and degradation level. Consensus clustering analysis was conducted to identify different clusters. A glycogen riskScore signature was developed to evaluate prognostic value across different cohorts. Besides, in vitro experiments were conducted to further evaluate the role of glycogen metabolism related genes in GC. Results: Both glycogen biosynthesis and degradation were significantly associated with worse overall survival and were also related with malignant phenotype in GC at both bulk and single-cell levels. Differential outcomes and immune functions were verified in the three identified clusters. The constructed glycogen riskScore model accurately classified GC patients with different outcomes, genomic and immune landscape, and performed well in predicting prognosis through external validation, immunotherapy and pan-cancer cohorts. Furthermore, the riskScore could predict response to chemotherapy and immunotherapy. Functional analyses revealed the signature's connection to pro-tumor and immunosuppression related pathways across pan-cancer. Additionally, glycogen metabolism related genes were found to regulate the malignant phenotypes of GC cells. Conclusion: This study revealed important roles of glycogen metabolism in promoting progression of GC and presented a glycogen riskScore model as a novel tool for predicting prognosis and treatment response.

PMID:39895799 | PMC:PMC11786036 | DOI:10.7150/jca.104424

iScience. 2024 Dec 22;28(2):111662. doi: 10.1016/j.isci.2024.111662. eCollection 2025 Feb 21.

ABSTRACT

KRAS mutations are linked to some of the deadliest forms of cancer. Pharmacological studies suggest that co-targeting KRAS with feedback/bypass pathways could lead to enhanced anti-tumor activity. The underlying premise is that cancers display a deep-rooted hypersensitivity to KRAS inactivation. Here, we investigate the role of intratumor heterogeneity in pancreatic ductal adenocarcinoma, focusing on oncogenic KRAS addiction and treatment resistance. Integrated analysis of single-cell and bulk RNA sequencing data reveals that most tumors display a mixture of cells with vastly different degrees of KRAS dependency. We identify distinct cell populations that vary in their gene expression patterns pertaining to the predicted level of KRAS signaling activity, cell growth, and differentiation commitment within each tumor. Selective targeting of mutant KRAS suppresses the growth of tumor cells with high RAS/mitogen-activated protein kinase (MAPK) activity while sparing pre-existing subsets with low RAS signaling activity, necessitating alternative treatments. Combination immunotherapy leads to durable tumor regression in preclinical models.

PMID:39898020 | PMC:PMC11787500 | DOI:10.1016/j.isci.2024.111662

Clin Transl Med. 2025 Feb;15(2):e70225. doi: 10.1002/ctm2.70225.

ABSTRACT

BACKGROUND: Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.

METHODS: To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.

RESULTS: Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.

CONCLUSIONS: With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.

KEYPOINTS: Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

PMID:39909829 | PMC:PMC11798665 | DOI:10.1002/ctm2.70225

Nat Comput Sci. 2025 Feb 7. doi: 10.1038/s43588-024-00764-8. Online ahead of print.

ABSTRACT

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.

PMID:39920506 | DOI:10.1038/s43588-024-00764-8

Front Microbiol. 2025 Jan 22;15:1516667. doi: 10.3389/fmicb.2024.1516667. eCollection 2024.

ABSTRACT

Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.

PMID:39911715 | PMC:PMC11794229 | DOI:10.3389/fmicb.2024.1516667

Curr Genomics. 2025;26(1):48-80. doi: 10.2174/0113892029308243240709073945. Epub 2024 Jul 18.

ABSTRACT

BACKGROUND: The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.

OBJECTIVE: This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets.

METHODOLOGY: Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes.

RESULTS: The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks.

CONCLUSION: The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 top-ranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.

PMID:39911278 | PMC:PMC11793067 | DOI:10.2174/0113892029308243240709073945

Cell Metab. 2025 Jan 28:S1550-4131(24)00495-9. doi: 10.1016/j.cmet.2024.12.013. Online ahead of print.

ABSTRACT

Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment but remains effective in only a subset of patients. Emerging evidence suggests that the gut microbiome and its metabolites critically influence ICB efficacy. In this study, we performed a multi-omics analysis of fecal microbiomes and metabolomes from 165 patients undergoing anti-programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) therapy, identifying microbial and metabolic entities associated with treatment response. Integration of data from four public metagenomic datasets (n = 568) uncovered cross-cohort microbial and metabolic signatures, validated in an independent cohort (n = 138). An integrated predictive model incorporating these features demonstrated robust performance. Notably, we characterized five response-associated enterotypes, each linked to specific bacterial taxa and metabolites. Among these, the metabolite phenylacetylglutamine (PAGln) was negatively correlated with response and shown to attenuate anti-PD-1 efficacy in vivo. This study sheds light on the interplay among the gut microbiome, the gut metabolome, and immunotherapy response, identifying potential biomarkers to improve treatment outcomes.

PMID:39909032 | DOI:10.1016/j.cmet.2024.12.013

Discov Oncol. 2025 Jan 29;16(1):100. doi: 10.1007/s12672-025-01826-7.

ABSTRACT

Non-small cell lung cancer (NSCLC) remains a dire disease being the first cause of cancer death among both genders. Early-stage NSCLC often has better treatment outcomes despite it being a highly heterogeneous disease. So far, the neo-adjuvant chemotherapy strategies have led to a small benefit with an improvement of 5% in overall survival as an absolute benefit. Recently, the introduction of immune checkpoint inhibitors combined with chemotherapy has shown robust efficacy in terms of event-free survival and overall survival. Thus, these combinations are today considered a new standard of care in early-stage NSCLC. The application of these strategies to all-comer population lead to confounding definitive results regarding the efficacy and predictive biomarkers are urgently needed balancing the promise of healing than toxicities. At present, the clinical staging TNM system guides the clinical choice, however it is not entirely sufficient. Circulant tumoral DNA (ctDNA) emerged as a promising prognostic and predictive biomarker that may guide the future perioperative strategy and pave the way to personalized medicine also in this exciting field. This narrative review aims to put in the context the employment of ctDNA, give some perspective and suggestions weighing the pros and cons of this technique for our tomorrow clinical practice.

PMID:39881042 | PMC:PMC11780067 | DOI:10.1007/s12672-025-01826-7

Sci Rep. 2025 Jan 30;15(1):3843. doi: 10.1038/s41598-025-87444-3.

ABSTRACT

Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microRNA, long non-coding RNA, somatic mutation, and DNA methylation data, from the sxdyc website. We synthesized the multiomics data of patients with STAD using 10 clustering methods, construct a consensus machine learning-driven signature (CMLS)-related prognostic models by combining 10 machine learning methods, and evaluated the prognosis models using the C-index. The prognostic relationship between CMLS and STAD was assessed using Kaplan-Meier curves, and the independent prognostic value of CMLS was determined by univariate and multivariate regression analyses. we also evaluated the immune characteristics, immunotherapy response, and drug sensitivity of different CMLS groups. The results of the multiomics analysis classified STAD into three subtypes, with CS1 resulting in the best survival outcome. In total, 10 hub genes (CES3, AHCYL2, APOD, EFEMP1, CYP1B1, ASPN, CPE, CLIP3, MAP1B, and DKK1) were screened and constructed the CMLS was significantly correlated with prognosis in patients with STAD and was an independent prognostic factor for patients with STAD. Using the CMLS risk score, all patients were divided into a high CMLS group and a low CMLS group. Patients in the low-CMLS group had better survival, more enriched immune cells, and higher tumor mutation load scores, suggesting better immunotherapy responsiveness and a possible "hot tumor" phenotype. Patients in the high-CMLS group had a significantly poorer prognosis and were less sensitive to immunotherapy but were likely to benefit more from chemotherapy and targeted therapy. In this study, 10 clustering methods and 10 machine learning methods were combined to analyze the multiomics of STAD, classify STAD into three subtypes, and constructed CMLS-related prognostic model features, which are important for accurate management and effective treatment of STAD.

PMID:39885324 | DOI:10.1038/s41598-025-87444-3