Aggregate analysis based on TCGA: TTN missense mutation correlates with favorable prognosis in lung squamous cell carcinoma

Cheng, Xingyu; Yin, Huaizhi; Fu, Juan; Chun-ya, Chen; An, Jianhong; Guan, Jingyan; Duan, Rong; Li, Hengming; Shen, Hong

doi:10.1007/s00432-019-02861-y

Cited by 64 publications

(48 citation statements)

References 21 publications

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“…Missense mutation of titin (TTN) is correlated with favorable prognosis in lung squamous cell carcinoma [35] In multiple tumor cohorts, the progression-free survival time or overall survival time of patients with TTN mutations is higher than that of patients with wild-type TTN, which is not consistent with our results.…”

Section: Discussioncontrasting

confidence: 81%

Prediction of Prognosis in Patients with Prostate Cancer by DNA Methylation Classification

Chen

Pan

et al. 2020

Preprint

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Abstract Background: Prostate cancer is the second most frequently diagnosed cancer and the fifth leading cause of cancer-related death. It is estimated that the incidence of prostate cancer is on the rise worldwide. Epigenetic changes in tumors play an important role in the occurrence and development of prostate cancer. DNA methylation is one of the mechanisms of tumor epigenetic regulation and may be a new biomarker that has great potential in early tumor screening, treatment guidance and prognosis prediction. The purpose of this study was to explore a classification method from the perspective of DNA methylation.Methods: The least absolute shrinkage and selection operator (LASSO) method was used to analyze DNA methylation and RNA-seq data from the Cancer Genome Atlas (TCGA). The methylation sites with small differences were eliminated, and the 21 methylation sites with the most significant differences were retained for analysis. Using their corresponding gene expression levels, a recurrence prediction model for prostate cancer patients was constructed to distinguish high-risk, medium-risk, and low-risk cases. Immune cell abundance analysis, gene enrichment analysis, Tumor burden mutation analysis and gene copy number variation analysis were then used to analyze the differences among these three subtypes and their underlying mechanisms. Results: We observed the difference in disease-free survival (DFS) of the three methylated subtypes in the test set, which was verified in the validation set. We found three subtypes have different proportions of immune cells, especially in memory B cells, M2 macrophages, Treg cells. GSVA and GSEA analysis revealed that the relevant metastasis gene sets of prostate cancer were enriched in high-risk cases. In addition, the mutation frequencies of TP53, TTN and KMT2D were the highest, and gradually increased in the three genotypes according to Tumor burden mutation (TMB) analysis. Gene copy number variation (CNV) showed that AR, LAPTM4B, and MTDH were significantly amplified, while ATP1B2 and FAM92B were significantly deleted. Finally, univariate and multivariate analysis showed that there were statistical differences among the three methylation subtypes, which can be used as an index to predict prostate cancer recurrence.Conclusions: Our study suggests that classification based on DNA methylation is an independent factor for predicting tumor recurrence in patients with prostate cancer.

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Section: Discussioncontrasting

confidence: 81%

Prediction of Prognosis in Patients with Prostate Cancer by DNA Methylation Classification

Chen

Pan

et al. 2020

Preprint

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“…The prevalence of MSI in GC is relatively high, and as MSI-H GCs are strongly associated with PD-L1 positivity, they could be applicable targets of anti-PD-1 therapies ( Kim et al, 2018 ). The mutations of TTN and MUC16 were announced to be correlated with better survival result in lung and GCs ( Cheng et al, 2019 ; Li et al, 2018a ).…”

Section: Discussionmentioning

confidence: 99%

Genome‐wide identification of CpG island methylator phenotype related gene signature as a novel prognostic biomarker of gastric cancer

Zeng

Xie

Gong

2020

PeerJ

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Background Gastric cancer (GC) is one of the most fatal cancers in the world. Results of previous studies on the association of the CpG island methylator phenotype (CIMP) with GC prognosis are conflicting and mainly based on selected CIMP markers. The current study attempted to comprehensively assess the association between CIMP status and GC survival and to develop a CIMP-related prognostic gene signature of GC. Methods We used a hierarchical clustering method based on 2,082 GC-related methylation sites to stratify GC patients from the cancer genome atlas into three different CIMP subgroups according to the CIMP status. Gene set enrichment analysis, tumor-infiltrating immune cells, and DNA somatic mutations analysis were conducted to reveal the genomic characteristics in different CIMP-related patients. Cox regression analysis and the least absolute shrinkage and selection operator were performed to develop a CIMP-related prognostic signature. Analyses involving a time-dependent receiver operating characteristic (ROC) curve and calibration plot were adopted to assess the performance of the prognostic signature. Results We found a positive relationship between CIMP and prognosis in GC. Gene set enrichment analysis indicated that cancer-progression-related pathways were enriched in the CIMP-L group. High abundances of CD8+ T cells and M1 macrophages were found in the CIMP-H group, meanwhile more plasma cells, regulatory T cells and CD4+ memory resting T cells were detected in the CIMP-L group. The CIMP-H group showed higher tumor mutation burden, more microsatellite instability-H, less lymph node metastasis, and more somatic mutations favoring survival. We then established a CIMP-related prognostic gene signature comprising six genes (CST6, SLC7A2, RAB3B, IGFBP1, VSTM2L and EVX2). The signature was capable of classifying patients into high‐and low‐risk groups with significant difference in overall survival (OS; p < 0.0001). To assess performance of the prognostic signature, the area under the ROC curve (AUC) for OS was calculated as 0.664 at 1 year, 0.704 at 3 years and 0.667 at 5 years. When compared with previously published gene-based signatures, our CIMP-related signature was comparable or better at predicting prognosis. A multivariate Cox regression analysis indicated the CIMP-related prognostic gene signature was an independent prognostic indicator of GC. In addition, Gene ontology analysis indicated that keratinocyte differentiation and epidermis development were enriched in the high-risk group. Conclusion Collectively, we described a positive association between CIMP status and prognosis in GC and proposed a CIMP-related gene signature as a promising prognostic biomarker for GC.

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“…In the low-immunity cohort, 129 genes were mutated in more than 10% of the samples while only 62 genes met this criterion in the high-immunity cohort, of which there was an overlap of 56 genes. The top 15 most frequently mutated genes in the corresponding cohorts are illustrated in Figure 3 E. Interestingly, TP53 , TTN , and MUC16 occupy the top three positions in both cohorts, and there were interactions among them that were regulating various tumor-associated biological processes in LUAD, 42 , 43 , 44 which indicates that they may be less involved in the immune infiltration process but are mainly involved in tumor progression. Next, we investigated the co-occurring and exclusive mutations of the top 25 most frequently mutated genes by using the CoMEt algorithm.…”

Section: Resultsmentioning

confidence: 99%

Multi-omics Data Analyses Construct TME and Identify the Immune-Related Prognosis Signatures in Human LUAD

Zhang

Yang

et al. 2020

Molecular Therapy - Nucleic Acids

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Lung cancer has been the focus of attention for many researchers in recent years for the leading contribution to cancer-related death worldwide, in which lung adenocarcinoma (LUAD) is the most common histological type. However, the potential mechanism behind LUAD initiation and progression remains unclear. Aiming to dissect the tumor microenvironment of LUAD and to discover more informative prognosis signatures, we investigated the immune-related differences in three types of genetic or epigenetic characteristics (expression status, somatic mutation, and DNA methylation) and considered the potential roles that these alterations have in the immune response and both the immune-related metabolic and neural systems by analyzing the multi-omics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct the prognostic prediction model. For the prognostic predictions on the independent test set, the performance of the trained models (average concordance index [C-index] = 0.839) is satisfied, with average 1-year, 3-year, and 5-year areas under the curve (AUCs) equal to 0.796, 0.786, and 0.777. Finally, the overall model was constructed based on all samples, which comprised 27 variables and achieved a high degree of accuracy on the 1-year (AUC = 0.861), 3-year (AUC = 0.850), and 5-year (AUC = 0.916) survival predictions.

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Aggregate analysis based on TCGA: TTN missense mutation correlates with favorable prognosis in lung squamous cell carcinoma

Cited by 64 publications

References 21 publications

Prediction of Prognosis in Patients with Prostate Cancer by DNA Methylation Classification

Prediction of Prognosis in Patients with Prostate Cancer by DNA Methylation Classification

Genome‐wide identification of CpG island methylator phenotype related gene signature as a novel prognostic biomarker of gastric cancer

Multi-omics Data Analyses Construct TME and Identify the Immune-Related Prognosis Signatures in Human LUAD

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