Opposite Roles of BAP1 in Overall Survival of Uveal Melanoma and Cutaneous Melanoma

Background: BRCA1-Associated Protein 1 (BAP1) germline mutations predispose individuals to cancers, including uveal melanoma (UM) and cutaneous melanoma (CM). BAP1 loss is common in UM and is associated with a worse prognosis. BAP1 loss is rare in CM and the outcome is unclear. Methods: UM and CM data was retrieved from The Cancer Genome Atlas (TCGA) database. Cox regression model was performed to examine whether BAP1 mRNA levels or copy number variations were associated with overall survival (OS). Results: BAP1-low mRNA predicted a poor OS in UM (HR = 9.57, 95% CI: 2.82, 32.5) but a contrasting better OS in CM (HR = 0.73, 95% CI: 0.56, 0.95). These results remained unchanged after adjusting for sex, age, and stage in UM and CM, or after adjusting for ulceration or Breslow depth in CM. Additionally, low BAP1 mRNA predicted a better OS in CM patients older than 50 years but not in younger patients. Co-expression and enrichment analysis revealed differential genes and mutations that were correlated with BAP1 expression levels in UM and CM tumors. Conclusions: Low BAP1 mRNA was significantly associated with a better OS in CM patients, in sharp contrast to UM. High BAP1 expression in CM was significantly associated with over-expressed CDK1, BCL2, and KIT at the protein level which may explain the poor OS in this sub-group of patients. Function of BAP1 was largely different in CM and UM despite of a small subset of shared co-expressed genes.

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Opposite roles of BAP1 in overall survival of uveal melanoma and cutaneous melanoma

10.21203/rs.2.15589/v1 ◽

2019 ◽

Author(s):

Feng Liu-Smith

Keyword(s):

Overall Survival ◽

Uveal Melanoma ◽

Cutaneous Melanoma ◽

Cox Regression ◽

Survival Function ◽

Poor Overall Survival ◽

Cox Regression Analysis ◽

Oncogenic Pathways ◽

Cellular Context ◽

Age Of Diagnosis

Abstract Background: BAP1 germline mutations predispose individuals to a number of cancer types including uveal melanoma (UM) and cutaneous melanoma (CM) which are distinctively different in the oncogenic pathways. BAP1 loss was common in UM and was associated with a worse prognosis. BAP1 loss was rare in CM and the outcome was unclear. Methods: This study used TCGA UM and CM databases for survival analysis for patients with different BAP1 status and mRNA expression levels. Cox regression model was used for adjusting to known prognosis factors. Results: BAP1- (loss or low expression) predicted a poor overall survival in UM (Cox HR = 0.062, logrank p =0.007) but a contrasting better overall survival in CM (HR = 1.69, p =0.009). Multi-covariate Cox regression analysis indicated BAP1 was a significant predictor for overall survival after adjusting for age of diagnosis, presence of ulceration, Breslow depth and CM stages in patients older than 50 years but not in younger patients. Co-expression analysis revealed no shared genes in BAP1 altered UM and CM tumors, further supporting a completely distinctive role of BAP1 in CM and UM. Conclusions: low BAP1 mRNA was significantly associated with a better overall survival in CM patients, in sharp contrast to its tumor suppressor role in UM where low or loss of BAP1 indicated a worse overall survival. Function of BAP1 may be dependent on cellular context.

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The Prognostic Value of m6A-related LncRNAs in Patients with HNSCC

10.21203/rs.3.rs-778502/v1 ◽

2021 ◽

Author(s):

Liu-qing Zhou ◽

Jie-yu Zhou ◽

Yao Hu

Keyword(s):

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Predictive Ability ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Cancer Genome Atlas

Abstract Background: N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. m6A modifications are known to modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood.Methods: Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a risk prognostic model, and consensus clustering analysis, we analyzed the 12 m6A-related lncRNAs in HNSCC samples data using the data from The Cancer Genome Atlas (TCGA) database.Results: We found twelve m6A-related lncRNAs in the training cohort and validated in all cohorts by Kaplan-Meier and Cox regression analyses, and revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC prognosis. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs.Conclusions: In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC prognosis and provide potential prediction outcome and new therapeutic target for HNSCC patients.

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CXCL5 Has Potential to Be a Marker for Hepatocellular Carcinoma Prognosis and Was Correlating With Immune Infiltrates

Frontiers in Oncology ◽

10.3389/fonc.2021.637023 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yuan Nie ◽

Mei-chun Jiang ◽

Cong Liu ◽

Qi Liu ◽

Xuan Zhu

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Hospital Data ◽

Gene Set Enrichment ◽

Local Hospital ◽

High Expression Group ◽

Cancer Genome Atlas

BackgroundsTumor microenvironment (TME) plays a crucial role in the initiation and progression of Hepatocellular Carcinoma (HCC), especially immune infiltrates. However, there is still a challenge in understanding the modulation of the immune and stromal components in TME, especially TME related genes.MethodsThe proportion of tumor-infiltrating immune cells (TICs) and the immune and stromal scores in 374 HCC patients from The Cancer Genome Atlas (TCGA) database were determined using CIBERSORT and ESTIMATE computational methods. The final screened genes were confirmed by the PPI network and univariate Cox regression of the differentially expressed genes based on different immune or stromal scores. The correlation between the expression levels of the final gene interactions and the clinical characteristics was based on TCGA database and local hospital data. Gene set enrichment analysis (GSEA) and the effect of CXCL5 expression on TICs were conducted.ResultsThere were correlations between the expression of CXCL5 and survival of HCC patients and TMN classification both in TCGA database and local hospital data. The immune-related activities were enriched in the high-expression group; however, the metabolic pathways were enriched in the low-expression group. The result of CIBERSORT analyzing had indicated that CXCL5 expression were correlated with the proportion of NK cells activated, macrophages M0, Mast cells resting, Neutrophils.ConclusionsCXCL5 was a potential prognostic marker for HCC and provides clues regarding immune infiltrates, which offers extra insight for therapeutics of HCC, however, more independent cohorts and functional experiments of CXCL5 are warranted.

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Angiogenin Upregulation Independently Predicts Unfavorable Overall Survival in Proneural Subtype of Glioblastoma

Technology in Cancer Research & Treatment ◽

10.1177/1533033819846636 ◽

2019 ◽

Vol 18 ◽

pp. 153303381984663

Author(s):

Ji-liang Hu ◽

Wei-Jian Luo ◽

Hao Wang

Keyword(s):

Overall Survival ◽

Survival Data ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Isocitrate Dehydrogenase 1 ◽

Study Results ◽

Cancer Genome Atlas ◽

Secondary Study ◽

Independent Indicator ◽

Temozolomide Chemotherapy

Objective: Angiogenin is a small protein that exerts potent stimulating effects on angiogenesis. In this study, we aimed to examine the expression of angiogenin in different subtypes of glioblastoma and estimated its independent prognostic value. Methods: The genomic and survival data from The Cancer Genome Atlas-glioblastoma were extracted for a secondary study. Results The expression of angiogenin was upregulated in glioblastoma tissues and varied significantly in different subtypes. Although the proneural subtype had the lowest angiogenin expression, high angiogenin expression was associated with significantly worse overall survival. However, this association was not observed in other subtypes. By performing univariate and multivariate analysis using Cox regression model, we observed that high angiogenin expression was an independent indicator of shorter overall survival in proneural glioblastoma (hazard ratio: 1.669, 95% confidence interval: 1.033-2.696, P = .036), after adjustment of age, gender, isocitrate dehydrogenase 1 mutation, temozolomide chemotherapy and radiation therapy. In addition, we also observed a correlation between elevated angiogenin expression and the hypomethylated status of its DNA. The hypermethylation group had significantly better overall survival. Conclusions: Angiogenin upregulation might serve as a biomarker for unfavorable overall survival in the proneural subtype of glioblastoma.

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The Microenvironment Combining with Immune Infiltration of Uveal Melanoma

10.21203/rs.3.rs-254958/v1 ◽

2021 ◽

Author(s):

Daowei Zhang ◽

Jiawen Wu ◽

Jihong Wu ◽

Shenghai Zhang

Keyword(s):

Uveal Melanoma ◽

Immune Cells ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Malignant Tumours ◽

Cancer Genome Atlas ◽

Key Genes ◽

Low Expression

Abstract Background: Uveal melanoma (UM) is the most common intraocular malignancy in adults. Although immunotherapy provided novel options in the disease's progression, it only benefits a minority of patients. The understanding of the UM microenvironment and the potential therapeutic targets in the microenvironment is still undefined. We aimed to propose a novel classification of UM microenvironment to identify ideal biomarkers for prognosis and potential targets for effective immunotherapy.Methods: In this study, we obtained the gene expression profile of 80 UM patients from the Cancer Genome Atlas (TCGA) and calculated immune/stromal scores based on the Estimation of stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm dividing patients into the high- and low-expression groups. Then, 1024 immune-related differently expressed genes (DEGs) were selected in total following the annotation and enrichment analysis and key genes were screened by PPI network. Consequently, we conducted CIBERSORT algorithm and TIMER database to analyses the correlation between key genes and immune cells as well as utilized GeneCards database to analyses the correlation between key genes and the disease. Additionally, GSVA was performed to enrich the nonparametric and unsupervised signaling pathways of key genes and the drug sensitivity of them were predicted.Results: Based on high- and low-expression groups, we found that there were 888 up-regulated DEGs and 126 down-regulated DEGs in total, which were mainly enriched in 4 pathways in GO and 3 pathways in KEGG. Combining with the 10 genes screened by PPI, KM-plot showed that B2M and HLA-B were significantly affected the survival of UM patients. Among 22 immune cells, B2M and HLA-B were mainly corelated with 11 of them. GSVA results revealed 42 pathways significantly enriched of B2M while 41 pathways enriched of HLA-B. Finally, we predicted the Paclitaxel as the hopefully treatment for HLA-B.Conclusions: Our study not only understands deeper of fundamental biological features of microenvironment but also with potential therapeutic targets of UM.

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Identification of prognostic immune-related lncRNA signature predicting the overall survival for colorectal cancer

10.21203/rs.3.rs-620796/v1 ◽

2021 ◽

Author(s):

Jianxin Li ◽

Ting Han ◽

Xin Wang ◽

Yinchun Wang ◽

Qingqiang Yang

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Regression Analyses

Abstract Background Long non-coding RNA (lncRNA) is an important regulator of gene expression and serves fundamental role in immune regulation. The present study aimed to develop a novel immune-related lncRNA signature to accurately assess the prognosis of patients with colorectal cancer (CRC). Methods Transcriptome data and clinical information of patients with CRC were downloaded from The Cancer Genome Atlas (TCGA), and the immune-related mRNAs were extracted from immunomodulatory gene datasets IMMUNE RESPONSE and IMMUNE SYSTEM PROCESS based on the Molecular Signatures Database (MSigDB). Then, the immune-related lncRNAs were identified by a correlation analysis between immune-related mRNAs and lncRNAs. Subsequently, univariate, lasso and multivariate Cox regression were used to identify an immune-related lncRNA signature in training cohort, and the predict ability of the signature was further confirmed in the testing cohort and the entire TCGA cohort. Finally, the lncRNA-mRNA co-expression network was established to explore the biological role of the immune-related lncRNA signature. Results In total, 272 Immune-related lncRNAs were identified, five of which were applied to construct an immune-related lncRNA signature based on univariate, lasso and multivariate Cox regression analyses. The signature divided patients with CRC into low- and high-risk groups, and patients with CRC in high-risk group had poorer overall survival than those in low-risk group. Univariate and multivariate Cox regression analyses confirmed that the signature could be an independent prognostic factor in human CRC. Furthermore, functional enrichment analysis revealed that the immune-related lncRNA signature was significantly enriched in immune process and tumor classical pathways. Conclusions The present study revealed that the novel immune-related lncRNA signature could be exploited as underlying molecular biomarkers and therapeutic targets for the patients with CRC.

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A Ferroptosis-Related Gene Model Predicts Prognosis and Immune Microenvironment for Cutaneous Melanoma

Frontiers in Genetics ◽

10.3389/fgene.2021.697043 ◽

2021 ◽

Vol 12 ◽

Author(s):

Congcong Xu ◽

Hao Chen

Keyword(s):

Cutaneous Melanoma ◽

Prognostic Model ◽

Cox Regression ◽

Tumor Biology ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Related Gene ◽

Immune Microenvironment ◽

Cancer Genome Atlas ◽

Prognostic Prediction

BackgroundCutaneous melanoma is a common but aggressive tumor. Ferroptosis is a recently discovered cell death with important roles in tumor biology. Nevertheless, the prognostic power of ferroptosis-linked genes remained unclear in cutaneous melanoma.MethodsCutaneous melanoma patients of TCGA (The Cancer Genome Atlas) were taken as the training cohort while GSE65904 and GSE22153 as the validation cohorts. Multifactor Cox regression model was used to build a prognostic model, and the performance of the model was assessed. Functional enrichment and immune infiltration analysis were used to clarify the mechanisms.ResultsA five ferroptosis-linked gene predictive model was developed. ALOX5 and GCH1 were illustrated as independent predictive factors. Functional assessment showed enriched immune-linked cascades. Immune infiltrating analysis exhibited the distinct immune microenvironment.ConclusionHerein, a novel ferroptosis-related gene prognostic model was built in cutaneous melanoma. This model could be used for prognostic prediction, and maybe helpful for the targeted and immunotherapies.

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Identification of Biomarkers of Hepatocellular Carcinoma Gene Prognosis Based on Immune-Related lncRNA Signature of Transcriptome Data

10.21203/rs.3.rs-57303/v1 ◽

2020 ◽

Author(s):

Andi Ma ◽

Yukai Sun ◽

Racheal O. Ogbodu ◽

Ling Xiao ◽

Haibing Deng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis

Abstract Background: It is well known that long non-coding RNAs (lncRNAs) play a vital role in cancer. We aimed to explore the prognostic value of potential immune-related lncRNAs in hepatocellular carcinoma (HCC). Methods: Validated the established lncRNA signature of 343 patients with HCC from The Cancer Genome Atlas (TCGA) and 81 samples from Gene Expression Omnibus (GEO). Immune-related lncRNAs for HCC prognosis were evaluated using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. LASSO analysis was performed to calculate a risk score formula to explore the difference in overall survival between high- and low-risk groups in TCGA, which was verified using GEO, Gene Ontology (GO), and pathway-enrichment analysis. These analyses were used to identify the function of screened genes and construct a co-expression network of these genes. Results: Using computational difference algorithms and lasso Cox regression analysis, the differentially expressed and survival-related immune-related genes (IRGs) among patients with HCC were established as five novel immune-related lncRNA signatures (AC099850.3, AL031985.3, PRRT3-AS1, AC023157.3, MSC-AS1). Patients in the low‐risk group showed significantly better survival than patients in the high‐risk group ( P = 3.033e−05). The signature identified can be an effective prognostic factor to predict patient survival. The nomogram showed some clinical net benefits predicted by overall survival. In order to explore its underlying mechanism, several methods of enrichment were elucidated using Gene Set Enrichment Analysis. Conclusion: Identifying five immune-related lncRNA signatures has important clinical implications for predicting patient outcome and guiding tailored therapy for patients with HCC with further prospective validation.

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Development and validation of the trans-omics model for pancreatic adenocarcinoma

Epigenomics ◽

10.2217/epi-2020-0184 ◽

2020 ◽

Author(s):

Weiguo Huang ◽

Wanqing Weng ◽

Boda Wu ◽

Tingbo Ye ◽

Zhuo Lin ◽

...

Keyword(s):

Overall Survival ◽

Pancreatic Adenocarcinoma ◽

Cox Regression ◽

Operator Method ◽

Comprehensive Analysis ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Cancer Genome Atlas ◽

Selection Operator ◽

Development And Validation

Aim: To develop a trans-omics-based molecular clinicopathological algorithm for predicting pancreatic adenocarcinoma prognosis, we performed a comprehensive analysis of the expression levels of mRNA, DNA methylation and DNA copy number in The Cancer Genome Atlas dataset. Materials & methods: Based on the least absolute shrinkage and selection operator method – COX regression analysis, a trans-omics-based classifier was established to predict overall survival. Nomogram was constructed by combining the classifier band clinical pathological characterization. Results: Based on trans-omics, we developed a 10-gene-based classifier and a molecular-clinicopathologic nomogram for predicting overall survival with satisfactory accuracy. Conclusion: Trans-omics-based classifier and molecule-clinicopathological nomogram based on the classifier can accurately predict the prognosis of pancreatic adenocarcinoma patients

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Identification of lnc RNAs Related to Prognosis of Patients With Colorectal Cancer

Technology in Cancer Research & Treatment ◽

10.1177/1533033820962120 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096212

Author(s):

Yuqi Sun ◽

Peng Peng ◽

Lanlan He ◽

Xueren Gao

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

High Expression ◽

Cox Regression Analysis ◽

Cancer Genome Atlas ◽

Score Model ◽

Prognostic Prediction

The purpose of this study was to identify long noncoding RNAs (lncRNAs) related to prognosis of patients with colorectal cancer (CRC) and develop a prognostic prediction model for CRC. Transcriptome data and survival information of CRC patients were downloaded from The Cancer Genome Atlas. The differentially expressed lncRNAs (DElncRNAs) between CRC and normal colorectal tissues were identified by the edgeR package. The association of DElncRNAs expression with prognosis of CRC patients was analyzed by the survival package. A nomogram predicting 3- and 5- year overall survival of CRC patients was drawn by the rms package. A total of 1046 DElncRNAs were identified, including 271 down-regulated and 775 up-regulated lncRNAs in CRC. Multivariate Cox regression analysis showed 10 lncRNAs related to the prognosis of CRC patients. Thereinto high expression of AC004009.1, LHX1-DT, ELFN1-AS1, AL136307.1, AC087379.2, RBAKDN and AC078820.1 was associated with poorer prognosis of CRC patients. High expression of LINC01055, AL590483.1 and AC008514.1 was associated with better prognosis of CRC patients. Furthermore, the risk score model developed based on the 10 lncRNAs could effectively predict overall survival of CRC patients. In conclusion, 10 prognostic biomarkers for CRC were identified, which would be helpful to understand the role of lncRNAs in CRC progression.

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