Dataset Information

Integrative Clustering Reveals a Novel Subtype of Soft Tissue Sarcoma With Poor Prognosis.

ABSTRACT: Background:Soft tissue sarcomas (STSs) are heterogeneous at the clinical and molecular level and need to be further sub-clustered for treatment and prognosis. Materials And Methods:STSs were sub-clustered based on RNAseq and miRNAseq data extracted from The Cancer Genome Atlas (TCGA) through the combined process of similarity network fusion (SNF) and consensus clustering (CC). The expression and clinical characteristics of each sub-cluster were analyzed. The genes differentially expressed (lncRNAs, miRNAs, and mRNAs) between the poor prognosis and good prognosis clusters were used to construct a competing endogenous RNA (ceRNA) network. Functional enrichment analysis was conducted and a hub network was extracted from the constructed ceRNA network. Results:A total of 247 STSs were classified into three optimal sub-clusters, and patients in cluster 2 (C2) had a significantly lower rate of survival. A ceRNA network with 91 nodes and 167 edges was constructed according to the hypothesis of ceRNA. Functional enrichment analysis revealed that the network was mainly associated with organism development functions. Moreover, LncRNA (KCNQ1OT1)-miRNA (has-miR-29c-3p)-mRNA (JARID2, CDK8, DNMT3A, TET1)-competing endogenous gene pairs were identified as hub networks of the ceRNA network, in which each component showed survival significance. Conclusion:Integrative clustering analysis revealed that the STSs could be clustered into three sub-clusters. The ceRNA network, especially the subnetwork LncRNA (KCNQ1OT1)-miRNA (has-miR-29c-3p)-mRNA (JARID2, CDK8, DNMT3A, TET1) was a promising therapeutic target for the STS sub-cluster associated with a poor prognosis.

SUBMITTER: Zhu Z

PROVIDER: S-EPMC7038822 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrative Clustering Reveals a Novel Subtype of Soft Tissue Sarcoma With Poor Prognosis.

Zhu Zhenhua Z Jin Zheng Z Zhang Haibo H Zhang Mei M Sun Dahui D

Frontiers in genetics 20200217

<h4>Background</h4>Soft tissue sarcomas (STSs) are heterogeneous at the clinical and molecular level and need to be further sub-clustered for treatment and prognosis.<h4>Materials and methods</h4>STSs were sub-clustered based on RNAseq and miRNAseq data extracted from The Cancer Genome Atlas (TCGA) through the combined process of similarity network fusion (SNF) and consensus clustering (CC). The expression and clinical characteristics of each sub-cluster were analyzed. The genes differentially e ...[more]

PMID: 32127798

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Project description:BackgroundSoft tissue sarcoma (STS) is a class of malignant tumors originating from mesenchymal stroma with a poor prognosis. Accumulating evidence has proved that angiogenesis is an essential hallmark of tumors. Nevertheless, there is a paucity of comprehensive research exploring the association of angiogenesis-related genes (ARGs) with STS.MethodsThe ARGs were extracted from previous literature, and the differentially expressed ARGs were screened for subsequent analysis. Next, the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were conducted to establish the angiogenesis-related signature (ARSig). The predictive performance of the novel ARSig was confirmed using internal and external validation, subgroup survival, and independent analysis. Additionally, the association of the ARSig with the tumor immune microenvironment, tumor mutational burden (TMB), and therapeutic response in STS were further investigated. Notably, we finally conducted in vitro experiments to verify the findings from the bioinformatics analysis.ResultsA novel ARSig is successfully constructed and validated. The STS with a lower ARSig risk score in the training cohort has an improved prognosis. Also, consistent results were observed in the internal and external cohorts. The receiver operating characteristic (ROC) curve, subgroup survival, and independent analysis further indicate that the novel ARSig is a promising independent prognostic predictor for STS. Furthermore, it is proved that the novel ARSig is relevant to the immune landscape, TMB, immunotherapy, and chemotherapy sensitivity in STS. Encouragingly, we also validate that the signature ARGs are significantly dysregulated in STS, and ARDB2 and SRPK1 are closely connected with the malignant progress of STS cells.ConclusionIn sum, we construct a novel ARSig for STS, which could act as a promising prognostic factor for STS and give a strategy for future clinical decisions, immune landscape, and personalized treatment of STS.

Project description:BACKGROUND:Dysfunction of the circadian clock and polymorphisms of some circadian genes have been linked to cancer development and progression. We investigated the relationship between circadian genes germline variation and susceptibility or prognosis of patients with soft tissue sarcoma. PATIENTS AND METHODS:We considered the 14 single nucleotide polymorphisms (SNPs) of 6 core circadian genes that have a minor allele frequency >?5% and that are known to be associated with cancer risk or prognosis. Genotyping was performed by q-PCR. Peripheral blood and clinic-pathological data were available for 162 patients with liposarcoma or leiomyosarcoma and 610 healthy donors. Associations between the selected clock genes polymorphisms and sarcoma susceptibility or prognosis were tested assuming 3 models of inheritance: additive, recessive and dominant. Subgroup analysis based on sarcoma histotype was performed under the additive genetic model. Multivariate logistic regression and multivariate Cox proportional hazard regression analyses were utilized to assess the association between SNPs with patient susceptibility and survival, respectively. Pathway variation analysis was conducted employing the Adaptive Rank Truncated Product method. RESULTS:Six out of the 14 analyzed SNPs were statistically significantly associated with susceptibility or prognosis of soft tissue sarcoma (P <?0.05). The present analysis suggested that carriers of the minor allele of the CLOCK polymorphism rs1801260 (C) or of PER2 rs934945 (T) had a reduced predisposition to sarcoma (26% and 35% respectively with the additive model) and liposarcoma (33% and 41% respectively). The minor allele (A) of NPAS2 rs895520 was associated with an increased predisposition to sarcoma of 33% and leiomyosarcoma of 44%. RORA rs339972 C allele was associated with a decreased predisposition to develop sarcoma assuming an additive model (29%) and leiomyosarcoma (36%). PER1 rs3027178 was associated with a reduced predisposition only in liposarcoma subgroup (32%). rs7602358 located upstream PER2 was significantly associated with liposarcoma survival (HR: 1.98; 95% CI 1.02-3.85; P?=?0.04). Germline genetic variation in the circadian pathway was associated with the risk of developing soft tissue sarcoma (P?=?0.035). CONCLUSIONS:Genetic variation of circadian genes appears to play a role in the determinism of patient susceptibility and prognosis. These findings prompt further studies to fully dissect the molecular mechanisms.

Project description:BackgroundBreast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes.MethodsTumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering.ResultsBased on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed.ConclusionsThe six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.

Dataset Information

Integrative Clustering Reveals a Novel Subtype of Soft Tissue Sarcoma With Poor Prognosis.

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Integrative Clustering Reveals a Novel Subtype of Soft Tissue Sarcoma With Poor Prognosis.

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