Mutations in the TTN Gene are a Prognostic Factor for Patients with Lung Squamous Cell Carcinomas

Zhang, S.; Ye, Jiayue; Sheng, Hu; Wei, Yiping; Xu, Jianjun

doi:10.2147/ijgm.s343259

Cited by 17 publications

(11 citation statements)

References 51 publications

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“…In CC patients, the most common oncogenic mutation is P53 mutation, accounting for more than 60% 30 – 32 . A study delineated that the TTN mutant may be a potential predictor in using immune checkpoint inhibitors in lung cancer patients 33 , 34 . However, the TTN mutant has not been reported in CC.…”

Section: Discussionmentioning

confidence: 99%

Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis

Zhang

Shen

et al. 2023

Sci Rep

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Colon cancer (CC) has a poor 5-year survival rate though the treatment techniques and strategies have been improved. Succinylation and long noncoding RNAs (lncRNAs) have prognostic value for CC patients. We analyzed and obtained succinylation-related lncRNA by co-expression in CC. A novel succinylation-related lncRNA model was developed by univariate and Least absolute shrinkage and selection operator (Lasso) regression analysis and we used principal component analysis (PCA), functional enrichment annotation, tumor immune environment, drug sensitivity and nomogram to verify the model, respectively. Six succinylation-related lncRNAs in our model were finally confirmed to distinguish the survival status of CC and showed statistically significant differences in training set, testing set, and entire set. The prognosis of with this model was associated with age, gender, M0 stage, N2 stage, T3 + T4 stage and Stage III + IV. The high-risk group showed a higher mutation rate than the low-risk group. We constructed a model to predict overall survival for 1-, 3-, and 5-year with AUCs of 0.694, 0.729, and 0.802, respectively. The high-risk group was sensitive to Cisplatin and Temozolomide compounds. Our study provided novel insights into the value of the succinylation-related lncRNA signature as a predictor of prognosis, which had high clinical application value in the future.

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Section: Discussionmentioning

confidence: 99%

Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis

Zhang

Shen

et al. 2023

Sci Rep

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“…CX3CR1 [441], S100A12 [442], PF4 [443], MPO (myeloperoxidase) [444], WNT7A [445], SLC6A4 [446], BDNF (brain derived neurotrophic factor) [447], CXCL10 [448], NEK7 [449], CYP1B1 [450], ABCA3 [451], TRIB3 [452], PCSK9 [453], FGF2 [454], ACKR4 [455], FASN (fatty acid synthase) [456], VIP (vasoactive intestinal peptide) [457], KL (klotho) [458], BMPR2 [459], APOA1 [323], TLR3 [460], CCR2 [461], TLR7 [462], CAV1 [463], WWC2 [464], TFPI (tissue factor pathway inhibitor) [465], EPAS1 [466] and CCDC40 [467] could serve as a potential therapeutic for pulmonary hypertension treatment. A previous study reported that the genes include CX3CR1 [468], CSF2 [469], CLDN18 [470], TRIM58 [471], PF4 [472], FFAR2 [473], MPO (myeloperoxidase) [474], CD5L [475], SH3GL2 [476], ITGA2B [477], S100A8 [478], VEGFD (vascular endothelial growth factor D) [479], CXCL11 [480], IL1A [481], WNT7A [482], SSTR1 [483], AQP4 [484], SCD (stearoyl-CoA desaturase) [485], SLC6A4 [486], BDNF (brain derived neurotrophic factor) [487], CXCL10 [488], ODAM (odontogenic, ameloblast associated) [489], CASP5 [490], CCL8 [491], TMEM100 [492], S100A9 [493], IL1B [494], CXCR1 [495], CXCR2 [496], WNT3A [497], BMI1 [498], CYP1B1 [499], FCN3 [500], TTN (titin) [501], SHISA3 [502], AZGP1 [503], ABCA3 [504], CD36 [505], EDNRB (endothelin receptor type B) [506], BTNL9 507], CEBPA (CCAAT enhancer binding protein alpha) [508], TRIB3 [509], TNNC1 [510], PCSK9 […”

Section: Discussionmentioning

confidence: 99%

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analysis of DEGs were performed. Then, a protein protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst database was used to construct the targeted microRNAs (miRNAs) and transcription factors (TFs) of the hub genes. Finally receiver operating characteristic (ROC) curve analysis was used to validates the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8 and EFHC2 were selected. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

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“…TTN mutations associated with human conditions include nonsense mutations, frameshift mutations, missense mutations, and splice site mutations. Studies have reported that TTN missense mutations are correlated with lung squamous cell carcinoma [ 42 , 43 ]. Our study also proved that missense mutations account for the dominant mutations in patients with THCA.…”

Section: Discussionmentioning

confidence: 99%

TTN mutations predict a poor prognosis in patients with thyroid cancer

et al. 2022

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Objective: We aimed to investigate the relationship between titin (TTN) gene mutations and thyroid cancer (THCA) and to explore the feasibility of the TTN gene as a potential prognostic indicator of THCA. Methods: From TCGA-THCA cohort, we performed a series of analyses to evaluate the prognostic value and potential mechanism of TTN in THCA. These patients were divided into the mutant-type (MUT) group and the wild-type (WT) group. Differentially expressed genes (DEGs) in the two groups were screened using the ‘DESeq2’ R package. Functional enrichment analysis was performed, and the protein–protein interaction (PPI) network, transcription factor (TF)-target interaction networks, and competitive endogenous RNA (ceRNA) regulatory networks were established for the DEGs. The TIMER database was applied for immune cell infiltration. Survival analysis and Cox regression analysis were used to analyze the potential prognostic value of the TTN gene. Results: Differential expression analysis showed that 409 genes were significantly up-regulated and 36 genes were down-regulated. Functional enrichment analysis revealed that TTN gene mutations played a potential role in the development of THCA. Analysis of the immune microenvironment indicated that TTN gene mutations were significantly associated with enrichment of M0 macrophages. Survival analysis showed that the MUT group predicted poorer prognosis than the WT group. Cox regression analysis demonstrated that TTN gene mutations were an independent risk factor for THCA. Nomograms also confirmed the prognostic values of the TTN gene in THCA. Conclusions In summary, our results demonstrated that TTN gene mutations predict poor prognosis in patients with THCA. This is the first study to research TTN gene mutations in THCA and to investigate their prognostic value in THCA.

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Mutations in the TTN Gene are a Prognostic Factor for Patients with Lung Squamous Cell Carcinomas

Cited by 17 publications

References 51 publications

Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis

Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

TTN mutations predict a poor prognosis in patients with thyroid cancer

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