Integrated multi-omics data reveals the molecular subtypes and guides the androgen receptor signalling inhibitor treatment of prostate cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Meng J
PROVIDER: S-EPMC8694501 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Limited studies have explored novel pancreatic cancer (PC) subtypes or prognostic biomarkers based on the altered activity of relevant signaling pathway gene sets. Here, we employed non-negative matrix factorization (NMF) to identify three immune subtypes of PC based on C7 immunologic signature gene set activity in PC and normal samples. Cluster 1, the immune-inflamed subtype, showed a higher response rate to immune checkpoint blockade (ICB) and had the lowest tumor immune dysfunction and exclusion (TIDE) scores. Cluster 2, the immune-excluded subtype, exhibited strong associations with stromal activation, characterized by elevated expression levels of transforming growth factor (TGF)-β, cell adhesion, extracellular matrix remodeling, and epithelial-to-mesenchymal transition (EMT) related genes. Cluster 3, the immune-desert subtype, displayed limited immune activity. For prognostic prediction, we developed an immune-related prognostic risk model (IRPM) based on four immune-related prognostic genes in pancreatic cancer, RHOF, CEP250, TSC1, and KIF20B. The IRPM demonstrated excellent prognostic efficacy and successful validation in an external cohort. Notably, the key gene in the prognostic model, RHOF, exerted significant influence on the proliferation, migration, and invasion of pancreatic cancer cells through in vitro experiments. Furthermore, we conducted a comprehensive analysis of somatic mutational landscapes and immune landscapes in PC patients with different IRPM risk scores. Our findings accurately stratified patients based on their immune microenvironment and predicted immunotherapy responses, offering valuable insights for clinicians in developing more targeted clinical strategies.
| S-EPMC10779306 | biostudies-literature
Project description:Glioblastoma is the most lethal malignant primary brain tumor; nevertheless, there remains a lack of accurate prognostic markers and drug targets. In this study, we analyzed 117 primary glioblastoma patients' data that contained SNP, DNA copy, DNA methylation, mRNA expression, and clinical information. After the quality of control examination, we conducted the single nucleotide polymorphism (SNP) analysis, copy number variation (CNV) analysis, and infiltrated immune cells estimate. And moreover, by using the cluster of cluster analysis (CoCA) methods, we finally divided these GBM patients into two novel subtypes, HX-1 (Cluster 1) and HX-2 (Cluster 2), which could be co-characterized by 3 methylation variable positions [cg16957313(DUSP1), cg17783509(PHOX2B), cg23432345(HOXA7)] and 15 (PCDH1, CYP27B1, LPIN3, GPR32, BCL6, OR4Q3, MAGI3, SKIV2L, PCSK5, AKAP12, UBE3B, MAP4, TP53BP1, F5, RHOBTB1) gene mutations pattern. Compared to HX-1 subtype, the HX-2 subtype was identified with higher gene co-occurring events, tumor mutation burden (TBM), and poor median overall survival [231.5 days (HX-2) vs. 445 days (HX-1), P-value = 0.00053]. We believe that HX-1 and HX-2 subtypes may make sense as the potential prognostic biomarkers for patients with glioblastoma.
| S-EPMC7726196 | biostudies-literature
Project description:Osteosarcoma (OS) is the most common primary bone malignancy that exhibits remarkable histologic diversity and genetic heterogeneity. The complex nature of osteosarcoma has confounded precise molecular categorization, prognosis, and prediction for this disease. In this study, we performed a comprehensive multiplatform analysis on 86 osteosarcoma tumors, including somatic copy-number alteration, gene expression and methylation, and identified three molecularly distinct and clinically relevant subtypes of osteosarcoma. The subgrouping criteria was validated on another cohort of osteosarcoma tumors. Previously unappreciated osteosarcoma-type-specific changes in specific genes' copy number, expression and methylation were revealed based on the subgrouping. The subgrouping and novel gene signatures provide insights into refining osteosarcoma therapy and relationships to other types of cancer.
| S-EPMC10605601 | biostudies-literature
Project description:The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we perform an integrative multi-omics analysis of patients diagnosed with NMIBC (n = 834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond established prognostic clinicopathological parameters. High chromosomal instability, p53-pathway disruption and APOBEC-related mutations are significantly associated with transcriptomic class 2a and poor outcome. RNA-derived immune cell infiltration is associated with chromosomally unstable tumors and enriched in class 2b. Spatial proteomics analysis confirms the higher infiltration of class 2b tumors and demonstrates an association between higher immune cell infiltration and lower recurrence rates. Finally, the independent prognostic value of the transcriptomic classes is documented in 1228 validation samples using a single sample classification tool. The classifier provides a framework for biomarker discovery and for optimizing treatment and surveillance in next-generation clinical trials.
| S-EPMC8052448 | biostudies-literature
Project description:Non-membrane-bound compartments such as P-bodies (PBs) and stress granules (SGs) play important roles in the regulation of gene expression following environmental stresses. We have systematically and quantitatively determined the protein and mRNA composition of PBs and SGs formed before and after nutrient stress. We find that high molecular weight (HMW) complexes exist prior to glucose depletion that we propose may act as seeds for further condensation of proteins forming mature PBs and SGs. We identify an enrichment of proteins with low complexity and RNA binding domains, as well as long, structured mRNAs that are poorly translated following nutrient stress. Many proteins and mRNAs are shared between PBs and SGs including several multivalent RNA binding proteins that promote condensate interactions during liquid-liquid phase separation. We uncover numerous common protein and RNA components across PBs and SGs that support a complex interaction profile during the maturation of these biological condensates. These interaction networks represent a tuneable response to stress, highlighting previously unrecognized condensate heterogeneity. These studies therefore provide an integrated and quantitative understanding of the dynamic nature of key biological condensates.
| S-EPMC8782181 | biostudies-literature
Project description:Bull fertility is of paramount importance in bovine industry because semen from a single bull is used to breed several thousands of cows; however, so far, no reliable test is available for bull fertility prediction. In the present study, spermatozoa from high- and low-fertility bulls were subjected to high-throughput transcriptomic, proteomic and metabolomic analysis. Using an integrated multi-omics approach the molecular differences between high- and low-fertility bulls were identified. We identified a total of 18,068 transcripts, 5041 proteins and 3704 metabolites in bull spermatozoa, of which the expression of 4766 transcripts, 785 proteins and 33 metabolites were dysregulated between high- and low-fertility bulls. At transcript level, several genes involved in oxidative phosphorylation pathway were found to be downregulated, while at protein level genes involved in metabolic pathways were significantly downregulated in low-fertility bulls. We found that metabolites involved in Taurine and hypotaurine metabolism were significantly downregulated in low-fertility bulls. Integrated multi-omics analysis revealed the interaction of dysregulated transcripts, proteins and metabolites in major metabolic pathways, including Butanoate metabolism, Glycolysis and gluconeogenesis, Methionine and cysteine metabolism, Phosphatidyl inositol phosphate, pyrimidine metabolism and saturated fatty acid beta oxidation. These findings collectively indicate that molecules governing sperm metabolism potentially influence bull fertility.
| S-EPMC9226030 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2024-09-05 | GSE254297 | GEO
Project description:BackgroundColon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes.MethodsIn this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR.ResultsThree clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, p<0.05). Integrative multi-omics analysis revealed biological processes contributing to colon cancer aggressiveness, recurrence, and progression. The developed MKPC score, based on gene pairs, was robust in predicting prognosis state (Log-Rank test, p<0.05), and risk-related genes were successfully verified by qPCR (t test, p<0.05). An easy-to-use web tool was created for risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.ConclusionIn conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.
| S-EPMC10067602 | biostudies-literature
Project description:High-risk neuroblastoma is a very aggressive disease, with excessive tumor growth and poor outcomes. A proper stratification of the high-risk patients by prognostic outcome is important for treatment. However, there is still a lack of survival stratification for the high-risk neuroblastoma. To fill the gap, we adopt a deep learning algorithm, Autoencoder, to integrate multi-omics data, and combine it with K-means clustering to identify two subtypes with significant survival differences. By comparing the Autoencoder with PCA, iCluster, and DGscore about the classification based on multi-omics data integration, Autoencoder-based classification outperforms the alternative approaches. Furthermore, we also validated the classification in two independent datasets by training machine-learning classification models, and confirmed its robustness. Functional analysis revealed that MYCN amplification was more frequently occurred in the ultra-high-risk subtype, in accordance with the overexpression of MYC/MYCN targets in this subtype. In summary, prognostic subtypes identified by deep learning-based multi-omics integration could not only improve our understanding of molecular mechanism, but also help the clinicians make decisions.
| S-EPMC6201709 | biostudies-literature
Project description:Integrated multi-omics analysis
| PRJNA1069651 | ENA