Exploration of the Tumor Mutational Burden as a Prognostic Biomarker and Related Hub Gene Identification in Prostate Cancer

To explore the signature function of the tumor mutational burden (TMB) and potential biomarkers in prostate cancer (PCa), transcriptome profiles, somatic mutation data, and clinicopathologic feature information were downloaded from The Cancer Genome Atlas (TCGA) database. R software package was used to generate a waterfall plot to summarize the specific mutation information and calculate the TMB value of PCa. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select the hub genes related to the TMB from the ImmPort network to build a risk score (RS) model to evaluate prognostic values and plot Kaplan–Meier (K-M) curves to predict PCa patients survival. The results showed that PCa patients with a high TMB exhibited higher infiltration of CD8+ T cells and CD4+ T cells and better overall survival (OS) than those with a low TMB. The anti-Mullerian hormone (AMH), baculoviral IAP repeat-containing 5 (BIRC5), and opoid receptor kappa 1 (OPRK1) genes were three hub genes and their copy number variation (CNV) was relatively likely to affect the infiltration of immune cells. Moreover, PCa patients with low AMH or BIRC5 expression had a longer survival time and lower cancer recurrence, while elevated AMH or BIRC5 expression favored PCa progression. In contrast, PCa patients with low OPRK1 expression had poorer OS in the early stage of PCa and a higher recurrent rate than those with high expression. Taken together, these results suggest that the TMB may be a promising prognostic biomarker for PCa and that AMH, OPRK1, and BIRC5 are hub genes affecting the TMB; AMH, OPRK1, and BIRC5 could serve as potential immunotherapeutic targets for PCa treatment.

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Screening and Identification of Prognostic Tumor-Infiltrating Immune Cells and Genes of Endometrioid Endometrial Adenocarcinoma: Based on The Cancer Genome Atlas Database and Bioinformatics

Frontiers in Oncology ◽

10.3389/fonc.2020.554214 ◽

2020 ◽

Vol 10 ◽

Author(s):

Bingnan Chen ◽

Di Wang ◽

Jiapo Li ◽

Yue Hou ◽

Chong Qiao

Keyword(s):

T Cells ◽

Immune Cells ◽

Cox Regression ◽

Endometrial Adenocarcinoma ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Regulation Mechanism ◽

Differential Analysis ◽

Hub Genes ◽

Cox Regression Analysis

BackgroundEndometrioid endometrial adenocarcinoma (EEA) is one of the most common tumors in the female reproductive system. With the further understanding of immune regulation mechanism in tumor microenvironment, immunotherapy is emerging in tumor treatment. However, there are few systematic studies on EEA immune infiltration.MethodsIn this study, prognostic tumor-infiltrating immune cells (TIICs) and related genes of EEA were comprehensively analyzed for the first time through the bioinformatics method with CIBERSORT algorithm as the core. Gene expression profile data were downloaded from the TCGA database, and the abundance ratio of TIICs was obtained. Kaplan–Meier analysis and Cox regression analysis were used to identify prognostic TIICs. EEA samples were grouped according to the risk score in Cox regression model. Differential analysis and functional enrichment analyses were performed on high- and low-risk groups to find survival-related hub genes, which were verified by Tumor Immune Estimation Resource (TIMER).ResultFour TIICs including memory CD4+ T cells, regulatory T cells, natural killer cells and dendritic cells were identified. And two hub gene modules were found, in which six hub genes including APOL1, CCL17, RBP4, KRT15, KRT71, and KRT79 were significantly related to overall survival and were closely correlated with some certain TIICs in the validation of TIMER.ConclusionIn this study, four prognostic TIICs and six hub genes were found to be closely related to EEA. These findings provided new potential targets for EEA immunotherapy.

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Identification of a prognostic long non-coding RNA signature in breast cancer

10.21203/rs.3.rs-27286/v1 ◽

2020 ◽

Author(s):

Gaochen Lan ◽

Xiaoling Yu ◽

Yanna Zhao ◽

Jinjian Lan ◽

Wan Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Prognostic Biomarker ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

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Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer

Frontiers in Surgery ◽

10.3389/fsurg.2021.665115 ◽

2021 ◽

Vol 8 ◽

Author(s):

Daojun Lv ◽

Zanfeng Cao ◽

Wenjie Li ◽

Haige Zheng ◽

Xiangkun Wu ◽

...

Keyword(s):

Prostate Cancer ◽

Survival Analysis ◽

Biochemical Recurrence ◽

Cox Regression ◽

Characteristic Curve ◽

The Cancer Genome Atlas ◽

Medical Decision ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Protein Signature

Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.Subjects: Bioinformatics, oncology, urology.

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Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

Technology in Cancer Research & Treatment ◽

10.1177/1533033820963578 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096357

Author(s):

Xiaoyong Gong ◽

Bobin Ning

Keyword(s):

Prostate Cancer ◽

Network Analysis ◽

Cox Regression ◽

Characteristic Curve ◽

Interaction Network ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Normal Prostate ◽

Cox Regression Analysis ◽

Incidence And Mortality

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

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Identification and validation of a prognostic 5-protein signature for biochemical recurrence following radical prostatectomy in prostate cancer

10.21203/rs.3.rs-29488/v1 ◽

2020 ◽

Author(s):

Xiangkun Wu ◽

Wenjie Li ◽

Daojun Lv ◽

Yongda Liu ◽

Di Gu

Keyword(s):

Prostate Cancer ◽

Regression Analysis ◽

Biochemical Recurrence ◽

Cox Regression ◽

Characteristic Curve ◽

Cox Proportional Hazards ◽

The Cancer Genome Atlas ◽

Medical Decision ◽

Cox Regression Analysis ◽

Protein Signature

Abstract Background : Biochemical recurrence (BCR) is considered as an indicator for prostate cancer (PCa)-specific recurrence and mortality. However, lack of effective prediction model to assess the prognosis of patients for optimization of treatment. The aim of this work was to construct a protein-based nomogram that could predict BCR for PCa.Materials and methods: Univariate Cox regression analysis was conducted to identify candidate proteins from the Cancer Genome Atlas (TCGA) database. LASSO Cox regression was further conducted to pick out the most significant prognostic proteins and formulate the proteins signature for predicting BCR. Additionally, a nomogram was constructed by multivariate Cox proportional hazards regression.Results: We established a 5‐protein-based signature which was well used to identify PCa patients into high‐ and low‐risk groups. Kaplan-Meier analysis demonstrated patients with higher BCR generally had significantly worse survival than those with lower BCR (p<0.0001). Time-dependent receiver operating characteristic curve expounded that ours signature had excellent prognostic efficiency for 1‐, 3‐ and 5‐year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariable and multivariate Cox regression analysis showed that this 5‐protein signature was an independent of several clinical signatures including age, Gleason score, T stage, N status, PSA and residual tumor. Moreover, a nomogram was constructed and calibration plots confirmed the its predictive value in 3-, 5- and 10-year BCR overall survival.Conclusion: Our study identified a 5-protein-based signature and constructed a prognostic nomogram that reliably predicts BCR in prostate cancer. The findings might be of paramount importance in tumor prognosis and medical decision-making.

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FABP4 Might be an Indicator of the Status of Tumor Microenvironment in Colorectal Cancer

10.21203/rs.3.rs-498848/v1 ◽

2021 ◽

Author(s):

Lijun Ning ◽

Yuqing Yan ◽

Tianying Tong ◽

Ziyun Gao ◽

Zhe Cui ◽

...

Keyword(s):

Colorectal Cancer ◽

T Cells ◽

Tumor Microenvironment ◽

Cox Regression ◽

Nk Cell ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Fatty Acid Binding ◽

Cox Regression Analysis ◽

The Status

Abstract Background: As tumor microenvironment (TME) play an indispensable role in tumorigenesis of colorectal cancer, this study performs a bunch of bioinformatics analysis to identify the indicator of the status of TME in Colorectal cancer (CRC). Results: In the presented study, we applied CIBERSORT and ESTIMATE computational methods to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in 444 COAD-READ cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein–protein interaction (PPI) network construction. Then, fatty acid-binding protein four ( FABP4 ) was determined as a predictive factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that FABP4 expression was positively correlated with the clinical pathologic characteristics (clinical stage, distant metastasis) and negatively correlated with the survival of CRC patients. Gene Set Enrichment Analysis (GSEA) showed that the genes in the high-expression FABP4 group were mainly enriched in immune-related activities. In the low-expression FABP4 group, the genes were enriched in metabolic pathways. CIBERSORT analysis for the proportion of TICs revealed that NK cell, CD4 + T cells and CD8 + T cells were negatively correlated with FABP4 expression, suggesting that FABP4 might be a potential prognostic factor of CRC patients. Conclusion: Our study has developed a new biomarker (FABP4) that can predict the status of tumor microenvironment in Colorectal cancer. Keywords: FABP4, tumor microenvironment, ESTIMATE, CIBERSORT, colorectal cancer

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Identification of Novel Diagnostic Biomarkers in Prostate Adenocarcinoma Based on the Stromal-Immune Score and Analysis of the WGCNA and ceRNA Network

Disease Markers ◽

10.1155/2022/1909196 ◽

2022 ◽

Vol 2022 ◽

pp. 1-10

Author(s):

Tengfei Zhang ◽

Yaxuan Wang ◽

Yiming Dong ◽

Lei Liu ◽

Yikai Han ◽

...

Keyword(s):

Prostate Cancer ◽

Tumor Microenvironment ◽

Cox Regression ◽

Differentially Expressed Gene ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Biomarkers ◽

Mechanism Study ◽

Cox Regression Analysis ◽

Immune Score

Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.

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Serine Incorporator 2 (SERINC2) Expression Predicts an Unfavorable Prognosis of Low-Grade Glioma (LGG): Evidence from Bioinformatics Analysis

Journal of Molecular Neuroscience ◽

10.1007/s12031-020-01620-w ◽

2020 ◽

Vol 70 (10) ◽

pp. 1521-1532

Author(s):

Chunxiao Qi ◽

Lei Lei ◽

Jinqu Hu ◽

Gang Wang ◽

Jiyuan Liu ◽

...

Keyword(s):

Bioinformatics Analysis ◽

Cox Regression ◽

Membrane Lipids ◽

Transmembrane Protein ◽

Low Grade Glioma ◽

The Cancer Genome Atlas ◽

Low Grade ◽

Normal Brain ◽

Hub Genes ◽

Cox Regression Analysis

Abstract Serine Incorporator 2 (SERINC2) is a transmembrane protein that incorporates serine into membrane lipids. The function of SERINC2 in tumors has been reported, but the role of SERINC2 in gliomas is not fully understood. RNA-sequencing data from The Cancer Genome Atlas (TCGA) (530 cases of low-grade glioma (LGG) and 173 cases of glioblastoma multiforme (GBM)) and microarray data from Gene Expression Omnibus (GEO) (Accession No. GSE16011, 284 cases gliomas were included) were acquired. Bioinformatics analysis was performed as the primary method to examine the function of SERINC2 and its correlated genes in glioma. SERINC2 was highly expressed in GBM compared with LGG and normal brain tissues. Elevated SERINC2 expression predicted shorter 5-, 10-, and 15-year overall survival (OS) of LGG patients and isocitrate dehydrogenase-1 (IDH-1) mutation-type LGG patients but had no effect on the OS of GBM patients. Cox regression analysis showed that SERINC2 was an independent factor in LGG OS. Methylation analysis found that 13 CpG methylation sites (methylation450k) correlated with SERINC2 expression in LGG. The mRNA expression level of SERINC2 was significant lower in the DNA deletion group than in the intact and amplification groups. A total of 390 copositive and 244 conegative correlation genes with SERINC2 were obtained from LGG in TCGA-LGG and GSE16011. Gene ontology (GO) category and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that the copositive correlation genes were primarily enriched in the mitotic process and cell cycle. Combining the results from the protein-protein interaction (PPI) network of SERINC2 correlation genes and CytoHubba led to the selection of 10 hub genes (CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE). OncoLnc analysis confirmed that high expression levels of these hub genes were associated with poor OS in LGG. Our results suggested that aberrant SERINC2 expression existed in glioma and that its expression might be a potential prognostic marker in LGG patients. CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE may be potential biomarkers and therapeutic targets for LGG.

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Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis

International Journal of Genomics ◽

10.1155/2020/1097602 ◽

2020 ◽

Vol 2020 ◽

pp. 1-23

Author(s):

Yutao Wang ◽

Jiaxing Lin ◽

Kexin Yan ◽

Jianfeng Wang

Keyword(s):

Prostate Cancer ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Test Set ◽

Cox Regression Analysis ◽

Significant Difference ◽

Robust Likelihood

Aim. In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer. Method. We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P<0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the “Rbsurv” package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032. Results. FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗FAM72D + 1.737∗ARHGAP33 − 0.737∗TACR2 − 0.651∗PLEK2 − 0.793∗FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC=0.6039) and GSE21032 GPL5188 (140 samples; AUC=0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups. Conclusion. A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.

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Development of a prognostic signature for esophageal cancer based on nine immune related genes

10.21203/rs.3.rs-56334/v2 ◽

2020 ◽

Author(s):

zhi Zhang ◽

cheng chen ◽

ying fang ◽

sheng Li ◽

xiaohua Wang ◽

...

Keyword(s):

T Cells ◽

Esophageal Cancer ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Receptor Interaction ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background: Function of the immune system is correlated with the prognosis of the tumor. The effect of immune microenvironment on esophageal cancer (EC) development has not been fully investigated. Methods: This study aimed to explore a prognostic model based on immune-related genes (IRGs) for EC. We obtained the RNA-seq dataset and clinical information of EC from the Cancer Genome Atlas (TCGA). Results: We identified 247 upregulated IRGs and 56 downregulated IRGs. Pathway analysis revealed that the most differentially expressed IRGs were enriched in Cytokine-cytokine receptor interaction. We further screened 13 survival-related IRGs and constructed regulatory networks involving related transcription factors (TFs). Finally, a prognostic model was constructed with 9 IRGs (HSPA6, S100A12, CACYBP, NOS2, DKK1, OSM ,STC2 ,NGPTL3 and NR2F2) by multivariate Cox regression analysis. The patients were classified into two subgroups with different outcomes. When adjusted with clinical factors, this model was verified as an independent predictor, which performed accurately in prognostic prediction. Next, M0 and M2 macrophages and activated mast cells were significantly enriched in high-risk group, while CD8 T cells and regulatory T cells (Tregs) were significantly enriched in low-risk group. Conclusions: Prognosis related IRGs were identified and a prognostic signature for esophageal cancer based on nine IRGs was developed.

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