Identifying a Ferroptosis-Related Gene Signature for Predicting Biochemical Recurrence of Prostate Cancer

Ferroptosis induced by lipid peroxidation is closely related to cancer biology. Prostate cancer (PCa) is not only a malignant tumor but also a lipid metabolic disease. Previous studies have identified ferroptosis as an important pathophysiological pathway in PCa development and treatment, but its role in the prognosis of PCa is less well known. In this study, we constructed a nine-ferroptosis-related gene risk model that demonstrated strong prognostic and therapeutic predictive power. The higher risk score calculated by the model was significantly associated with a higher ferroptosis potential index, higher Ki67 expression, higher immune infiltration, higher probability of biochemical recurrence, worse clinicopathological characteristics, and worse response to chemotherapy and antiandrogen therapy in PCa. The mechanisms identified by the gene set enrichment analysis suggested that this signature can accurately distinguish high- and low-risk populations, which is possibly closely related to variations in steroid hormone secretion, regulation of endocrine processes, positive regulation of humoral immune response, and androgen response. Results of this study were confirmed in two independent PCa cohorts, namely, The Cancer Genome Atlas cohort and the MSK-IMPACT Clinical Sequencing Cohort, which contributed to the body of scientific evidence for the prediction of biochemical recurrence in patients with PCa. In addition, as the main components of this signature, the effects of the AIFM2 and NFS1 genes on ferroptosis were evaluated and verified by in vivo and in vitro experiments, respectively. The above findings provided new insights and presented potential clinical applications of ferroptosis in PCa.

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Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model

Frontiers in Oncology ◽

10.3389/fonc.2020.598801 ◽

2020 ◽

Vol 10 ◽

Author(s):

Yanlong Zhang ◽

Ruiqiao Zhang ◽

Fangzhi Liang ◽

Liyun Zhang ◽

Xuezhi Liang

Keyword(s):

Prostate Cancer ◽

Drug Sensitivity ◽

Risk Model ◽

External Validation ◽

Disease Free Survival ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Predictive Values ◽

Cox Analysis

BackgroundDespite being the second most common tumor in men worldwide, the tumor metabolism-associated mechanisms of prostate cancer (PCa) remain unclear. Herein, this study aimed to investigate the metabolism-associated characteristics of PCa and to develop a metabolism-associated prognostic risk model for patients with PCa.MethodsThe activity levels of PCa metabolic pathways were determined using mRNA expression profiling of The Cancer Genome Atlas Prostate Adenocarcinoma cohort via single-sample gene set enrichment analysis (ssGSEA). The analyzed samples were divided into three subtypes based on the partitioning around medication algorithm. Tumor characteristics of the subsets were then investigated using t-distributed stochastic neighbor embedding (t-SNE) analysis, differential analysis, Kaplan–Meier survival analysis, and GSEA. Finally, we developed and validated a metabolism-associated prognostic risk model using weighted gene co-expression network analysis, univariate Cox analysis, least absolute shrinkage and selection operator, and multivariate Cox analysis. Other cohorts (GSE54460, GSE70768, genotype-tissue expression, and International Cancer Genome Consortium) were utilized for external validation. Drug sensibility analysis was performed on Genomics of Drug Sensitivity in Cancer and GSE78220 datasets. In total, 1,039 samples and six cell lines were concluded in our work.ResultsThree metabolism-associated clusters with significantly different characteristics in disease-free survival (DFS), clinical stage, stemness index, tumor microenvironment including stromal and immune cells, DNA mutation (TP53 and SPOP), copy number variation, and microsatellite instability were identified in PCa. Eighty-four of the metabolism-associated module genes were narrowed to a six-gene signature associated with DFS, CACNG4, SLC2A4, EPHX2, CA14, NUDT7, and ADH5 (p <0.05). A risk model was developed, and external validation revealed the strong robustness our risk model possessed in diagnosis and prognosis as well as the association with the cancer feature of drug sensitivity.ConclusionsThe identified metabolism-associated subtypes reflected the pathogenesis, essential features, and heterogeneity of PCa tumors. Our metabolism-associated risk model may provide clinicians with predictive values for diagnosis, prognosis, and treatment guidance in patients with PCa.

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RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.650499 ◽

2022 ◽

Vol 12 ◽

Author(s):

Lan-Xin Mu ◽

You-Cheng Shao ◽

Lei Wei ◽

Fang-Fang Chen ◽

Jing-Wei Zhang

Keyword(s):

Breast Cancer ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Impact ◽

Immune Microenvironment ◽

Model Based ◽

Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

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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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Intratumoral Adipocyte-High Breast Cancer Enrich for Metastatic and Inflammation-Related Pathways but Associated with Less Cancer Cell Proliferation

International Journal of Molecular Sciences ◽

10.3390/ijms21165744 ◽

2020 ◽

Vol 21 (16) ◽

pp. 5744 ◽

Cited By ~ 8

Author(s):

Yoshihisa Tokumaru ◽

Masanori Oshi ◽

Eriko Katsuta ◽

Li Yan ◽

Jing Li Huang ◽

...

Keyword(s):

Breast Cancer ◽

Cell Proliferation ◽

Cancer Progression ◽

Cancer Biology ◽

The Cancer Genome Atlas ◽

Related Gene ◽

Immune Microenvironment ◽

Gene Sets ◽

Tumor Immune Microenvironment ◽

Er Positive

Cancer-associated adipocytes are known to cause inflammation, leading to cancer progression and metastasis. The clinicopathological and transcriptomic data from 2256 patients with breast cancer were obtained based on three cohorts: The Cancer Genome Atlas (TCGA), GSE25066, and a study by Yau et al. For the current study, we defined the adipocyte, which is calculated by utilizing a computational algorithm, xCell, as “intratumoral adipocyte”. These intratumoral adipocytes appropriately reflected mature adipocytes in a bulk tumor. The amount of intratumoral adipocytes demonstrated no relationship with survival. Intratumoral adipocyte-high tumors significantly enriched for metastasis and inflammation-related gene sets and are associated with a favorable tumor immune microenvironment, especially in the ER+/HER2- subtype. On the other hand, intratumoral adipocyte-low tumors significantly enriched for cell cycle and cell proliferation-related gene sets. Correspondingly, intratumoral adipocyte-low tumors are associated with advanced pathological grades and inversely correlated with MKI67 expression. In conclusion, a high amount of intratumoral adipocytes in breast cancer was associated with inflammation, metastatic pathways, cancer stemness, and favorable tumor immune microenvironment. However, a low amount of adipocytes was associated with a highly proliferative tumor in ER-positive breast cancer. This cancer biology may explain the reason why patient survival did not differ by the amount of adipocytes.

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ETS-related gene(ERG) expression as a predictor of oncological outcomes in patients with high-grade prostate cancer treated with primary androgen deprivation therapy: a cohort study

BMJ Open ◽

10.1136/bmjopen-2018-025161 ◽

2019 ◽

Vol 9 (3) ◽

pp. e025161

Author(s):

Mark Rezk ◽

Ashish Chandra ◽

Daniel Addis ◽

Henrik Møller ◽

Mina Youssef ◽

...

Keyword(s):

Prostate Cancer ◽

Cohort Study ◽

Prostate Specific Antigen ◽

Outcome Measures ◽

Androgen Deprivation Therapy ◽

Gleason Score ◽

Biochemical Recurrence ◽

Specific Antigen ◽

Androgen Deprivation ◽

Related Gene

ObjectivesTo determine whetherETS-related gene(ERG) expression can be used as a biomarker to predict biochemical recurrence and prostate cancer-specific death in patients with high Gleason grade prostate cancer treated with androgen deprivation therapy (ADT) as monotherapy.MethodsA multicentre retrospective cohort study identifying 149 patients treated with primary ADT for metastatic or non-metastatic prostate cancer with Gleason score 8–10 between 1999 and 2006. Patients planned for adjuvant radiotherapy at diagnosis were excluded. Age at diagnosis, ethnicity, prostate-specific antigen and Charlson-comorbidity score were recorded. Prostatic tissue acquired at biopsy or transurethral resection surgery was assessed for immunohistochemical expression ofERG. Failure of ADT defined as prostate specific antigen nadir +2. Vital status and death certification data determined using the UK National Cancer Registry. Primary outcome measures were overall survival (OS) and prostate cancer specific survival (CSS). Secondary outcome was biochemical recurrence-free survival (BRFS).ResultsThe median OS of our cohort was 60.2 months (CI 52.0 to 68.3).ERGexpression observed in 51/149 cases (34%). Multivariate Cox proportional hazards analysis showed no significant association betweenERGexpression and OS (p=0.41), CSS (p=0.92) and BRFS (p=0.31). Cox regression analysis showed Gleason score (p=0.003) and metastatic status (p<1×10-5) to be the only significant predictors of prostate CSS.ConclusionsNo significant association was found betweenERGstatus and any of our outcome measures. Despite a limited sample size, our results suggest thatERGdoes not appear to be a useful biomarker in predicting response to ADT in patients with high risk prostate cancer.

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Prediction of Biochemical Recurrence-Free Survival of Prostate Cancer Patients Leveraging Multiple Gene Expression Profiles in Tumor Microenvironment

Frontiers in Oncology ◽

10.3389/fonc.2021.632571 ◽

2021 ◽

Vol 11 ◽

Author(s):

Rui Zhou ◽

Yuanfa Feng ◽

Jianheng Ye ◽

Zhaodong Han ◽

Yuxiang Liang ◽

...

Keyword(s):

Prostate Cancer ◽

Tumor Microenvironment ◽

Cancer Patients ◽

Cancer Progression ◽

Biochemical Recurrence ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Cell Contact ◽

Recurrence Free Survival ◽

Free Survival

Tumor-adjacent normal (TAN) tissues, which constitute tumor microenvironment and are different from healthy tissues, provide critical information at molecular levels that can be used to differentiate aggressive tumors from indolent tumors. In this study, we analyzed 52 TAN samples from the Cancer Genome Atlas (TCGA) prostate cancer patients and developed a 10-gene prognostic model that can accurately predict biochemical recurrence-free survival based on the profiles of these genes in TAN tissues. The predictive ability was validated using TAN samples from an independent cohort. These 10 prognostic genes in tumor microenvironment are different from the prognostic genes detected in tumor tissues, indicating distinct progression-related mechanisms in two tissue types. Bioinformatics analysis showed that the prognostic genes in tumor microenvironment were significantly enriched by p53 signaling pathway, which may represent the crosstalk tunnels between tumor and its microenvironment and pathways involving cell-to-cell contact and paracrine/endocrine signaling. The insight acquired by this study has advanced our knowledge of the potential role of tumor microenvironment in prostate cancer progression.

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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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AC099850.3 Promotes Cell Proliferation and is One of Five Costimulatory Molecule-Related LncRNAs That Predict Overall Survival of Liver Hepatocellular Carcinoma

10.21203/rs.3.rs-1069383/v1 ◽

2021 ◽

Author(s):

Meimei Liu ◽

Qiong Fang ◽

Yanping Huang ◽

Jin Zhou ◽

Qi Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Proliferation ◽

Risk Model ◽

Cox Regression ◽

Costimulatory Molecule ◽

Costimulatory Molecules ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Liver Hepatocellular Carcinoma

Abstract Background: Extensive research has revealed that costimulatory molecules play central roles in mounting anti-tumor immune responses and long non‐coding RNA (lncRNA) is an important regulatory factor in the development of various cancers. However, their roles in liver hepatocellular carcinoma (HCC) remain unexplored. In this study, we aimed to explore costimulatory molecule-related lncRNAs in HCC and construct a prognostic signature to predict prognosis and improve clinical outcomes with HCC patients.Methods: The data we used for bioinformatics analysis were downloaded from The Cancer Genome Atlas database. Costimulatory molecules were obtained from the known literature. The R software, SPSS and GraphPad Prism were used for mapping and statistical analysis.Results: A five costimulatory molecule-related lncRNAs based risk model was initially constructed through lasso and Cox regression analysis. Moreover, multivariate regression suggested that the risk score was a significant prognostic risk factor in HCC. Samples in high- and low-risk groups exhibited significantly different in gene set enrichment analysis and immune infiltration analysis. Importantly, we found that the AC099850.3 were significantly related to cell proliferation in HCC according to the colony formation and CCK8 assays.Conclusion: In summary, we first identified and validated a novel costimulatory molecule-related survival model and we found that AC099850.3 is closely associated with clinical stage and could remarkably facilitate cell proliferation in HCC, making it potential to be a novel therapeutic target.

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Angiogenesis-Related Gene Expression Signatures Predicting Prognosis in Gastric Cancer Patients

Cancers ◽

10.3390/cancers12123685 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3685

Author(s):

Haoyu Ren ◽

Jiang Zhu ◽

Haochen Yu ◽

Alexandr Bazhin ◽

Christoph Westphalen ◽

...

Keyword(s):

Gastric Cancer ◽

Risk Score ◽

Cox Regression ◽

Clinical Stage ◽

External Validation ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Related Gene ◽

Angiogenesis Related Gene

Increasing evidence indicates that angiogenesis is crucial in the development and progression of gastric cancer (GC). This study aimed to develop a prognostic relevant angiogenesis-related gene (ARG) signature and a nomogram. The expression profile of the 36 ARGs and clinical information of 372 GC patients were extracted from The Cancer Genome Atlas (TCGA). Consensus clustering was applied to divide patients into clusters 1 and 2. Least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to identify the survival related ARGs and establish prognostic gene signatures, respectively. The Asian Cancer Research Group (ACRG) (n = 300) was used for external validation. Risk score of ARG signatures was calculated, and a prognostic nomogram was developed. Gene set enrichment analysis of the ARG model risk score was performed. Cluster 2 patients had more advanced clinical stage and shorter survival rates. ARG signatures carried prognostic relevance in both cohorts. Moreover, ARG-risk score was proved as an independent prognostic factor. The predictive value of the nomogram incorporating the risk score and clinicopathological features was superior to tumor, lymph node, metastasis (TNM) staging. The high-risk score group was associated with several cancer and metastasis-related pathways. The present study suggests that ARG-based nomogram could serve as effective prognostic biomarkers and allow a more precise risk stratification.

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Using machine learning method to identify MYLK as a novel marker to predict biochemical recurrence in prostate cancer

Biomarkers in Medicine ◽

10.2217/bmm-2020-0495 ◽

2021 ◽

Vol 15 (1) ◽

pp. 29-41

Author(s):

Peng Qiao ◽

Di Zhang ◽

Song Zeng ◽

Yicun Wang ◽

Biao Wang ◽

...

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Biochemical Recurrence ◽

Surgical Margin ◽

Gene Expression Omnibus ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Support Vector ◽

Cancer Genome Atlas ◽

Genome Atlas

Aim: This study aims to identify novel marker to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy with negative surgical margin. Materials & methods: The Cancer Genome Atlas database, Gene Expression Omnibus database and Cancer Cell Line Encyclopedia database were employed. The ensemble support vector machine-recursive feature elimination method was performed to select crucial gene for BCR. Results: We identified MYLK as a novel and independent biomarker for BCR in The Cancer Genome Atlas training cohort and confirmed in four independent Gene Expression Omnibus validation cohorts. Multi-omic analysis suggested that MYLK was a DNA methylation-driven gene. Additionally, MYLK had significant positive correlations with immune infiltrations. Conclusion: MYLK was identified and validated as a novel, robust and independent biomarker for BCR in prostate cancer.

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