Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma

Wang, Geng; Lv, Zhi; Fan, Jinshuo; Xu, Jing; Mao, Kaimin; Yin, Zhengrong; Qing, Wanlu; Jin, Yang

doi:10.3389/fcell.2021.657667

Cited by 36 publications

(35 citation statements)

References 63 publications

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“…These mechanisms by which lncRNAs regulate DNA repair-associated genes facilitate our understanding of the link between lncRNA and GI. Moreover, several lncRNA signatures have been established for predicting the prognosis of cancer patients (16)(17)(18)(19)(20), whereas the potential biological and clinical significance of GI-associated lncRNAs in PTC remains largely unknown.…”

Section: Introductionmentioning

confidence: 99%

Genomic Instability-Related LncRNA Signature Predicts the Prognosis and Highlights LINC01614 Is a Tumor Microenvironment-Related Oncogenic lncRNA of Papillary Thyroid Carcinoma

et al. 2021

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BackgroundGenomic instability (GI) is among the top ten characteristics of malignancy. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in GI. So far, the clinical value of GI-related lncRNAs (GIlncs) in papillary thyroid cancer (PTC) has not been clarified.MethodsIntegrative analysis of lncRNA expression and somatic mutation profiles was performed to identify GIlncs. Analysis of differentially expressed lncRNAs in the group with high- and low- cumulative number of somatic mutations revealed significant GIlncs in PTC. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify hub-GIlncs.ResultsA computational model based on four lncRNAs (FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1) was identified as a quantitative index using an in-silicon discovery cohort. GILS score was significantly associated with poor prognosis, as validated in the TCGA dataset and further tested in our local RNA-Seq cohort. Moreover, a combination of clinical characteristics and the composite GILS-clinical prognostic nomogram demonstrates satisfactory discrimination and calibration. Furthermore, the GILS score and FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 were also associated with driver mutations and multiple clinical-pathological variables, respectively. Moreover, RNA-Seq confirmed the expression patterns of FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 in PTC and normal thyroid tissues. Biological experiments demonstrated that downregulated or overexpressed LINC01614 affect PTC cell proliferation, migration, and invasion in vitro. Activation of the stromal and immune cell infiltration was also observed in the high LINC01614 group in the PTC microenvironment.ConclusionIn summary, we identified a signature for clinical outcome prediction in PTC comprising four lncRNAs associated with GI. A better understanding of the GI providing an alternative evaluation of the progression risk of PTC. Our study also demonstrated LINC01614 as a novel oncogenic lncRNA and verified its phenotype in PTC.

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Section: Introductionmentioning

confidence: 99%

Genomic Instability-Related LncRNA Signature Predicts the Prognosis and Highlights LINC01614 Is a Tumor Microenvironment-Related Oncogenic lncRNA of Papillary Thyroid Carcinoma

et al. 2021

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“…Studies had found that proto-oncogenes and tumor suppressor genes in nearly 20 common malignant tumors, in which Kidney chromophobe (KICH) was the fewest with 2 genes and Uterine Corpus Endometrial Carcinoma (UCEC) were the most with 55 genes (22). Furthermore, genomic instability was widely involved in the diagnosis and prognosis of various malignant tumors, especially esophageal, bladder, breast, and lung cancers (15,17,27,28). Long non-coding RNAs (lncRNAs) were major components of the mammalian transcriptome and played central roles in a variety of cellular mechanisms.…”

Section: Discussionmentioning

confidence: 99%

“…Genomic instability plays a key role in the development of cancer. For example, structural changes in proto-oncogenes and tumor suppressor genes may lead to abnormalities in cell functions, including cell growth, cell cycle, cell senescence and apoptosis, cell invasion and metastasis (15)(16)(17)(18). The genomic instabilities of cancer mainly include gene mutation, chromosome rearrangement and aneuploidy (19).…”

Section: Introductionmentioning

confidence: 99%

Identification of a Somatic Mutation-Derived Long Non-Coding RNA Signatures of Genomic Instability in Renal Cell Carcinoma

et al. 2021

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BackgroundRenal cell carcinoma (RCC) is a malignant tumor with high morbidity and mortality. It is characterized by a large number of somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) are widely involved in the expression of genomic instability in renal cell carcinoma. But no studies have identified the genome instability-related lncRNAs (GInLncRNAs) and their clinical significances in RCC.MethodsClinical data, gene expression data and mutation data of 943 RCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the mutation data and lncRNA expression data, GInLncRNAs were screened out. Co-expression analysis, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore their potential functions and related signaling pathways. A prognosis model was further constructed based on genome instability-related lncRNAs signature (GInLncSig). And the efficiency of the model was verified by receiver operating characteristic (ROC) curve. The relationships between the model and clinical information, prognosis, mutation number and gene expression were analyzed using correlation prognostic analysis. Finally, the prognostic model was verified in clinical stratification according to TCGA dataset.ResultsA total of 45 GInLncRNAs were screened out. Functional analysis showed that the functional genes of these GInLncRNAs were mainly enriched in chromosome and nucleoplasmic components, DNA binding in molecular function, transcription and complex anabolism in biological processes. Univariate and Multivariate Cox analyses further screened out 11 GInLncSig to construct a prognostic model (AL031123.1, AC114803.1, AC103563.7, AL031710.1, LINC00460, AC156455.1, AC015977.2, ‘PRDM16-dt’, AL139351.1, AL035661.1 and LINC01606), and the coefficient of each GInLncSig in the model was calculated. The area under the curve (AUC) value of the ROC curve was 0.770. Independent analysis of the model showed that the GInLncSig model was significantly correlated with the RCC patients’ overall survival. Furthermore, the GInLncSig model still had prognostic value in different subgroups of RCC patients.ConclusionOur study preliminarily explored the relationship between genomic instability, lncRNA and clinical characteristics of RCC patients, and constructed a GInLncSig model consisted of 11 GInLncSig to predict the prognosis of patients with RCC. At the same time, our study provided theoretical support for the exploration of the formation and development of RCC.

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“…Zhu et al constructed a global triple network and found that MALAT1 and LINC00943 may be closely related to melanoma occurrence [11]. Bao et al and Geng et al explored lncRNA signatures associated with gene instability in lung adenocarcinoma and breast cancer, respectively [12,13]. Nevertheless, the relationship between lncRNA in melanoma and genetic instability was not discussed in these bioinformatic studies.…”

Section: Introductionmentioning

confidence: 99%

Gene Instability-Related lncRNA Prognostic Model of Melanoma Patients via Machine Learning Strategy

Yan

Wang

Shao

et al. 2021

Journal of Oncology

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Background. Melanoma is a common tumor characterized by a high mortality rate in its late stage. After metastasis, current treatment methods are relatively ineffective. Many studies have shown that long noncoding RNA (lncRNA) may participate in gene mutation and genomic instability in cancer. Methods. We downloaded transcriptome data, mutation data, and clinical follow-up data of melanoma patients from The Cancer Genome Atlas. We divided samples into groups according to the number of somatic cell mutations and then performed a differential analysis to screen out the differentially expressed genes. We then divided samples into genomic unstable and genomic stable groups. We compared lncRNA expression profiles in these groups and constructed a protein-coding genes network coexpressed with selected lncRNA to analyze the pathways enriched by these genes. Two machine learning methods, least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to conduct the lncRNA-related prognostic model. Afterward, we performed survival analysis, risk correlation analysis, independent prognostic analysis, and clinical subgroup model validation. Finally, through wound healing assay and transwell assay, the function of AATBC was verified by A375 cell lines. Results. We screened 61 prognostic-related lncRNAs and constructed an lncRNA-mRNA coexpression network based on these lncRNAs. Seven lncRNAs were selected as common characteristic factors based on the two machine learning methods. The model formula was as follows: risk score = 0.085 ∗ AATBC + 0.190 ∗ AC026689.1−0.117 ∗ AC083799.1 + 0.036 ∗ AC091544.6−0.039 ∗ LINC01287−0.291 ∗ SPRY4.AS1 + 0.056 ∗ ZNF667.AS1. The seven lncRNAs in this formula are key candidates. Cell experiments have verified that knocking down AATBC in A375 cell lines can reduce the proliferation and invasion ability of melanoma cells. Conclusion. The lncRNA we identified provides a new way to study lncRNA’s role in the genomic instability of melanoma. Our findings may provide essential candidate biomarkers for the diagnosis and treatment of melanoma.

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Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma

Cited by 36 publications

References 63 publications

Genomic Instability-Related LncRNA Signature Predicts the Prognosis and Highlights LINC01614 Is a Tumor Microenvironment-Related Oncogenic lncRNA of Papillary Thyroid Carcinoma

Genomic Instability-Related LncRNA Signature Predicts the Prognosis and Highlights LINC01614 Is a Tumor Microenvironment-Related Oncogenic lncRNA of Papillary Thyroid Carcinoma

Identification of a Somatic Mutation-Derived Long Non-Coding RNA Signatures of Genomic Instability in Renal Cell Carcinoma

Gene Instability-Related lncRNA Prognostic Model of Melanoma Patients via Machine Learning Strategy

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