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

Zhu, Zhifeng; Jin, Zheng; Zhang, Haibo; Zhang, Mei; Sun, Dahui

doi:10.3389/fgene.2020.00069

Cited by 11 publications

(11 citation statements)

References 50 publications

(58 reference statements)

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“…Identifying effective biomarker and constructing an ideal prognostic signature or nomogram has long been the focus of oncologists, which can individually predict the specific outcomes to guide the management of tumor patients. At present, a great number of biomarkers have been reported, and several prognostic models for sarcoma were constructed ( Benassi et al, 2015 ; Callegaro et al, 2017 , 2019 ; Yang et al, 2017 ; Huang et al, 2019 ; Raut et al, 2019 ; Zhang et al, 2019a ; Gu et al, 2020 ; Hu et al, 2020 ; Zhu et al, 2020 ). For example, MS Benassi et al (2015) reported that the expression of IGFBP7, considered a tumor stroma marker in mesenchymal-derived cells, was highly prognostic in poor metastasis-free survival for soft tissue sarcoma.…”

Section: Discussionmentioning

confidence: 99%

“…For example, MS Benassi et al (2015) reported that the expression of IGFBP7, considered a tumor stroma marker in mesenchymal-derived cells, was highly prognostic in poor metastasis-free survival for soft tissue sarcoma. Additionally, clinicopathological variables, ncRNA data, or immune cells were also confirmed as predictors and used to develop prognostic models ( Callegaro et al, 2017 , 2019 ; Yang et al, 2017 ; Huang et al, 2019 ; Raut et al, 2019 ; Zhang et al, 2019a ; Gu et al, 2020 ; Hu et al, 2020 ; Zhu et al, 2020 ). Nevertheless, clinical practice is not optimistic.…”

Section: Discussionmentioning

confidence: 99%

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Construction of Two Alternative Polyadenylation Signatures to Predict the Prognosis of Sarcoma Patients

Liu

et al. 2021

Front. Cell Dev. Biol.

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BackgroundIncreasing evidence indicates that alternative polyadenylation (APA) is associated with the prognosis of cancers.MethodsWe obtained gene expression and APA profiles of 259 sarcoma patients from the TCGA dataportal and TC3A database, respectively. The prognostic signatures, clinical nomograms, and regulatory networks were studied by integrated bioinformatics analyses. Then, the immune cell infiltration profile was obtained from the ImmuCellAI. The association between APA-based signature and immune cells was studied.ResultsA total of 61 and 38 APA events were identified as overall survival (OS)- and progress free-survival (PFS)-related biomarkers, respectively. Two signatures were generated. The area under the curves (AUC) values of OS signature were 0.900, 0.928, and 0.963 over 2-, 4-, and 6-years, respectively. And the AUC values of PFS signature at 2-, 4-, and 6-years were 0.826, 0.840, and 0.847, respectively. Overall and subgroup analyses indicated that high-risk patients had a worse prognosis than low-risk patients (all p-values < 0.05). In addition, immunomics analyses indicated that there are different patterns of immune cell infiltration between low- and high-risk patients. Furthermore, two clinical-APA nomograms were established and the C-indexes were 0.813 and 0.809 for OS nomogram and PFS nomogram, respectively. Finally, two APA regulatory networks were constructed. FIP1L1-VPS26B was identified as a key regulating relationship and validated in the pan-cancer analyses.ConclusionIn this study, we identified prognostic predictors based on APA events with high accuracy for risk stratification in sarcoma patients and uncovered interesting regulatory networks in sarcoma that could be underlying mechanisms. This study not only provides novel potential prognostic biomarkers but promote precision medicine and provide potential novel research interests for immunotherapy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Construction of Two Alternative Polyadenylation Signatures to Predict the Prognosis of Sarcoma Patients

Liu

et al. 2021

Front. Cell Dev. Biol.

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“…Previously, several genomic and transcriptome data have focused on exploring effective diagnostic or prognostic markers in STSs, including alternative splicing, [ 8 ] copy number variation, [ 9 ] and genes expression. [ 10 ] As one of the core elements in tumorigenesis progression, DNA methylation occurs early and frequently in regulating a variety of genomic functions. [ 11 ] DNA methylation is a posttranslational modification process, which are selectively occurred on the cytosines of 5“-CpG-3” to generates 5-methyldeoxycytidine.…”

Section: Introductionmentioning

confidence: 99%

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

Li¹,

Wu²,

Yao

et al. 2021

Medicine

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Soft tissue sarcomas (STSs) are heterogeneous at the clinical with a variable tendency of aggressive behavior. In this study, we constructed a specific DNA methylation-based classification to identify the distinct prognosis-subtypes of STSs based on the DNA methylation spectrum from the TCGA database. Eventually, samples were clustered into 4 subgroups, and their survival curves were distinct from each other. Meanwhile, the samples in each subgroup reflected differentially in several clinical features. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was also conducted on the genes of the corresponding promoter regions of the above-described specific methylation sites, revealing that these genes were mainly concentrated in certain cancer-associated biological functions and pathways. In addition, we calculated the differences among clustered methylation sites and performed the specific methylation sites with LASSO algorithm. The selection operator algorithm was employed to derive a risk signature model, and a prognostic signature based on these methylation sites performed well for risk stratification in STSs patients. At last, a nomogram consisted of clinical features and risk score was developed for the survival prediction. This study declares that DNA methylation-based STSs subtype classification is highly relevant for future development of personalized therapy as it identifies the prediction value of patient prognosis.

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“…Although current frontline DLBCL therapy (the standard R-CHOP chemotherapy regimen) is associated with a high complete response rates of 70-80%, 10% to 15% of DLBCL patients are refractory, and almost 40% of cases experience relapse within 2-3 years after initial response 3,4 . An enhanced International Prognostic Index (NCCN-IPI) was built to better discriminate low-and high-risk subgroups in the rituximab era, which still needs to be further investigated on the robust capacity for risk stratification in the context of targeted therapies 5,6 . Therefore, there is an urgent need to explore potential molecular mechanism and identify more key biomarkers and therapeutic targets.…”

Section: Introductionmentioning

confidence: 99%

An IPI based immune prognostic model for diffuse large B-cell lymphoma

Shi

et al. 2021

Preprint

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Background: An enhanced International Prognostic Index (NCCN-IPI) was built to better discriminate diffuse large B-cell lymphoma (DLBCL) patients in the rituximab era. However, there is an urgent need to identify novel valuable biomarkers in the context of targeted therapies, such as immune checkpoint blockade (ICB) therapy. Methods: Gene expression data and clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. 73 immune-related hub genes in DLBCL patients with different IPI levels were identified by weighted gene co-expression network analysis (WGCNA), and 4 genes were selected to construct an IPI-based immune-related prognostic model (IPI-IPM). Afterward, the genetic, somatic mutational and molecular profiles of IPI-IPM subgroups were analyzed, as well as the potential clinical response of ICB in different IPI-IPM subgroups. Results: The IPI-IPM was constructed base on the expression of LCN2, CD5L, NLRP11 and SERPINB2, where high-risk patients had shorter overall survival (OS) than low-risk patients, consistent with the results in the GEO cohorts. The comprehensive results showed that a high IPI-IPM risk score was correlated with immune-related signaling pathways, high KMT2D and CD79B mutation rates, high infiltration of CD8+ T cells and macrophages (M1, M2), as well as up-regulation of inhibitory immune checkpoints including PD-L1, LAG3 and BTLA, indicating more potential response to ICB therapy. Conclusion: The IPI-IPM has independent prognostic significance for DLBCL patients, which provides an immunological perspective to elucidate the mechanisms on tumor progression and drug resistance, also sheds a light on developing immunotherapy for DLBCL.

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

Cited by 11 publications

References 50 publications

Construction of Two Alternative Polyadenylation Signatures to Predict the Prognosis of Sarcoma Patients

Construction of Two Alternative Polyadenylation Signatures to Predict the Prognosis of Sarcoma Patients

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

An IPI based immune prognostic model for diffuse large B-cell lymphoma

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