Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

Cao, Xiaohong; Zhang, Qingquan; Zhu, Yu; Huo, Xiaoqing; Bao, Junze; Su, Min

doi:10.3389/fonc.2022.780950

Cited by 19 publications

(14 citation statements)

References 46 publications

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“…For treating autoimmune diseases, TNFRSF1B is a powerful therapeutic target ( 34 ). Recently, AC007991.4 was shown to be associated with the prognosis of gastric and ovarian cancer ( 35 , 36 ), while LINC00996 can be used as a prognostic factor for colorectal and lung cancer ( 37 , 38 ). There are no published studies related to AL133371.2.…”

Section: Discussionmentioning

confidence: 99%

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Necroptosis-associated long noncoding RNAs can predict prognosis and differentiate between cold and hot tumors in ovarian cancer

Fang

Hu²,

Chen

et al. 2022

Front. Oncol.

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ObjectiveThe mortality rate of ovarian cancer (OC) is the highest among all gynecologic cancers. To predict the prognosis and the efficacy of immunotherapy, we identified new biomarkers.MethodsThe Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx) databases were used to extract ovarian cancer transcriptomes. By performing the co-expression analysis, we identified necroptosis-associated long noncoding RNAs (lncRNAs). We used the least absolute shrinkage and selection operator (LASSO) to build the risk model. The qRT-PCR assay was conducted to confirm the differential expression of lncRNAs in the ovarian cancer cell line SK-OV-3. Gene Set Enrichment Analysis, Kaplan-Meier analysis, and the nomogram were used to determine the lncRNAs model. Additionally, the risk model was estimated to evaluate the efficacy of immunotherapy and chemotherapy. We classified necroptosis-associated IncRNAs into two clusters to distinguish between cold and hot tumors.ResultsThe model was constructed using six necroptosis-associated lncRNAs. The calibration plots from the model showed good consistency with the prognostic predictions. The overall survival of one, three, and five-year areas under the ROC curve (AUC) was 0.691, 0.678, and 0.691, respectively. There were significant differences in the IC50 between the risk groups, which could serve as a guide to systemic treatment. The results of the qRT-PCR assay showed that AL928654.1, AL133371.2, AC007991.4, and LINC00996 were significantly higher in the SK-OV-3 cell line than in the Iose-80 cell line (P < 0.05). The clusters could be applied to differentiate between cold and hot tumors more accurately and assist in accurate mediation. Cluster 2 was more vulnerable to immunotherapies and was identified as the hot tumor.ConclusionNecroptosis-associated lncRNAs are reliable predictors of prognosis and can provide a treatment strategy by screening for hot tumors.

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Section: Discussionmentioning

confidence: 99%

“…AC011445.1 is related to SIRT2 and SPATA2. SIRT2 is an NAD+-dependent deacetylase and is the only sirtuin protein found in the cytoplasm, mitochondria, and nucleus [36]. It can regulate autophagy in high-fat-exposed immune cells (39).…”

Section: Discussionmentioning

confidence: 99%

Necroptosis-associated long noncoding RNAs can predict prognosis and differentiate between cold and hot tumors in ovarian cancer

Fang

Hu²,

Chen

et al. 2022

Front. Oncol.

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“…This study constructed a ceRNA network mediated by the MIR600HG/hsa-mir-21-5p axis and included five targeting mRNAs (BCL11B, ETS1, EPHA4, KLF12, and KMT2A). A few studies have demonstrated the function of MIR600HG, which is considered a lncRNA biomarker for predicting the progression of tumor patients ( Song et al, 2018 ; Cao et al, 2022 ). Xiao et al (2021 ) reported that MIR600HG, which was involved in the ceRNA network in the present study, influenced pancreatic adenocarcinoma progression by regulating immune cell infiltration.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of a T-cell-related MIR600HG/hsa-mir-21-5p competing endogenous RNA network in tuberculosis activation based on integrated bioinformatics approaches

Guan²,

Peng³

et al. 2022

Front. Genet.

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Background: T cells play critical roles in the progression of tuberculosis (TB); however, knowledge regarding these molecular mechanisms remains inadequate. This study constructed a critical ceRNA network was constructed to identify the potentially important role of TB activation via T-cell regulation.Methods: We performed integrated bioinformatics analysis in a randomly selected training set from the GSE37250 dataset. After estimating the abundance of 18 types of T cells using ImmuCellAI, critical T-cell subsets were determined by their diagnostic accuracy in distinguishing active from latent TB. We then identified the critical genes associated with T-cell subsets in TB activation through co-expression analysis and PPI network prediction. Then, the ceRNA network was constructed based on RNA complementarity detection on the DIANA-LncBase and mirDIP platform. The gene biomarkers included in the ceRNA network were lncRNA, miRNA, and targeting mRNA. We then applied an elastic net regression model to develop a diagnostic classifier to assess the significance of the gene biomarkers in clinical applications. Internal and external validations were performed to assess the repeatability and generalizability.Results: We identified CD4+ T, Tr1, nTreg, iTreg, and Tfh as T cells critical for TB activation. A ceRNA network mediated by the MIR600HG/hsa-mir-21-5p axis was constructed, in which the significant gene cluster regulated the critical T subsets in TB activation. MIR600HG, hsa-mir-21-5p, and five targeting mRNAs (BCL11B, ETS1, EPHA4, KLF12, and KMT2A) were identified as gene biomarkers. The elastic net diagnostic classifier accurately distinguished active TB from latent. The validation analysis confirmed that our findings had high generalizability in different host background cases.Conclusion: The findings of this study provided novel insight into the underlying mechanisms of TB activation and identifying prospective biomarkers for clinical applications.

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“…The ten lncRNAs with the largest differences (FTX, ENTPD1-AS1, AL365361.1, OIP5-AS1, SNAI3-AS1, AL132656.2, PSMD6-AS2, MALAT1, POLR2J4, and WDFY3-AS2) were then further analyzed. Except for AL132656.2, all other lncRNAs have been reported to be associated with cancers (39)(40)(41)(42)(43)(44)(45)(46)(47). However, no studies have reported that these lncRNAs play a specific role in SKCM.…”

Section: Construction Of the Lncrna-mirna-mrna Triple Regulatory Networkmentioning

confidence: 99%

Integrated analysis to reveal potential therapeutic targets and prognostic biomarkers of skin cutaneous melanoma

et al. 2022

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Skin cutaneous melanoma (SKCM) is a malignant tumor with high mortality rate in human, and its occurrence and development are jointly regulated by genes and the environment. However, the specific pathogenesis of SKCM is not completely understood. In recent years, an increasing number of studies have reported the important role of competing endogenous RNA (ceRNA) regulatory networks in various tumors; however, the complexity and specific biological effects of the ceRNA regulatory network of SKCM remain unclear. In the present study, we obtained a ceRNA regulatory network of long non-coding RNAs, microRNAs, and mRNAs related to the phosphatase and tensin homolog (PTEN) in SKCM and identified the potential diagnostic and prognostic markers related to SKCM. We extracted the above three types of RNA involved in SKCM from The Cancer Genome Atlas database. Through bioinformatics analysis, the OIP5-AS1-hsa-miR-186-5p/hsa-miR-616-3p/hsa-miR-135a-5p/hsa-miR-23b-3p/hsa-miR-374b-5p-PTPRC/IL7R/CD69 and MALAT1-hsa-miR-135a-5p/hsa-miR-23b-3p/hsa-miR-374b-5p-IL7R/CD69 ceRNA networks were found to be related to the prognosis of SKCM. Finally, we determined the OIP5-AS1-PTPRC/IL7R/CD69 and MALAT1-IL7R/CD69 axes in ceRNA as a clinical prognostic model using correlation and Cox regression analyses. Additionally, we explored the possible role of these two axes in affecting gene expression and immune microenvironment changes and the occurrence and development of SKCM through methylation and immune infiltration analyses. In summary, the ceRNA-based OIP5-AS1-PTPRC/IL7R/CD69 and MALAT1-IL7R/CD69 axes may be a novel and important approach for the diagnosis and prognosis of SKCM.

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Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

Cited by 19 publications

References 46 publications

Necroptosis-associated long noncoding RNAs can predict prognosis and differentiate between cold and hot tumors in ovarian cancer

Necroptosis-associated long noncoding RNAs can predict prognosis and differentiate between cold and hot tumors in ovarian cancer

Identification and validation of a T-cell-related MIR600HG/hsa-mir-21-5p competing endogenous RNA network in tuberculosis activation based on integrated bioinformatics approaches

Integrated analysis to reveal potential therapeutic targets and prognostic biomarkers of skin cutaneous melanoma

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