Identification of a Ubiquitin Related Genes Signature for Predicting Prognosis of Prostate Cancer
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ABSTRACT:
SUBMITTER: Song G
PROVIDER: S-EPMC8801742 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cervical cancer (CC) is one of the most common malignancies encountered in gynecology practice. However, there is a paucity of information about specific biomarkers that assist in the diagnosis and prognosis of CC. Pyroptosis is a form of programmed cell death whose different elements are related to the occurrence, invasion, and metastasis of tumors. However, the role of pyroptosis phenomena in the progression of CC has not yet been elucidated. This study focuses on the development of a pyroptosis-associated prognostic signature for CC using integrated bioinformatics to delineate the relationships among the signature, tumor microenvironment, and immune response of the patients. In this respect, we identified a prognostic signature that depends on eight pyroptosis-related genes (PRGs) that designate with better prognostic survival in the low-risk group (P<0.05) and where AUC values were greater than 0.7. A multi-factor Cox regression analysis indicated that such a signature could be used as an independent prognostic factor, and both the DCA and the Nomogram suggested that the proposed prognostic signature had good predictive capabilities. Interestingly, this prognostic signature can be applied to multiple tumors and thus, is versatile from a clinical point of view. In addition, there were significant differences in the tumor microenvironment and immune infiltration status between the high- and low-risk groups (P<0. 05). The core gene granzyme B (GZMB) was screened and the CC-associated regulatory axis, GZMB/ miR-378a/TRIM52-AS1, was constructed, which may promote CC progression, and further experimentation is needed to validate these results.
| S-EPMC8660613 | biostudies-literature
Project description:BackgroundOvarian cancer is one of the most common malignancies often resulting in a poor prognosis. 5-methylcytosine (m5C) is a common epigenetic modification with roles in eukaryotes. However, the expression and function of m5C regulatory factors in ovarian cancer remained unclear.ResultsTwo molecular subtypes with different prognostic and clinicopathological features were identified based on m5C regulatory factors. Meanwhile, functional annotation showed that in the two subtypes, 452 differentially expressed genes were significantly related to the malignant progression of ovarian cancer. Subsequently, four m5C genes were screened to construct a risk marker predictive of overall survival and indicative of clinicopathological features of ovarian cancer, also the robustness of the risk marker was verified in external dataset and internal validation set. multifactorial cox regression analysis and nomogram demonstrated that risk score was an independent prognostic factor for ovarian cancer prognosis.ConclusionIn conclusion, our results revealed that m5C-related genes play a critical role in tumor progression in ovarian cancer. Further detection of m5C methylation could provide a novel targeted therapy for treating ovarian cancer.
| S-EPMC8240302 | biostudies-literature
Project description:Background Colorectal cancer (CRC) is the third most common cancer. Nearly a decade of studies had shown that cancer regimens tailored to molecular and pathological features lead to improved overall survival. Oxidative stress (OS) refers to a state in which oxidation and antioxidant effects are unbalanced in the body. However, the molecular mechanism of OS-related CRC remains unclear. Methods Univariate Cox regression analysis gained OS signature genes related to CRC prognosis, and then, different CRC molecular subtypes were obtained by consensus clustering analysis. Differential expression analysis and least absolute shrinkage and selection operator (LASSO) algorithm were used to obtain prognostic-related signature genes. Significantly, risk score was calculated by RiskScore = Σβi × Expi. Moreover, the Kaplan-Meier survival analysis, immune cell infiltration, and sensitivity to treatment regimens were performed to assess the model's validity and adaptability. Finally, RiskScore incorporated clinicopathological features to further improve prognostic models and survival prediction. Results 63 OS-related prognostic genes were obtained, and four distinct molecular subtypes of CRC were identified based on the expression characteristics. 230 differentially expressed genes (DEGs) between different molecular subtypes were compressed by LASSO algorithm, and finally, 6 OS-related genes were obtained. The Kaplan-Meier survival analysis indicated that the high RiskScore groups had poorer prognosis and the RiskScore model showed better predictive performance in all three other independent datasets. Moreover, immunotherapy/chemosensitivity analysis found that the low-risk group was more sensitive to different treatment options and could achieve better treatment outcomes. Conclusion Oxidative stress-related RiskScore model built in this work has good predictive performance for CRC.
| S-EPMC9936508 | biostudies-literature
Project description:Background: Systemic metastasis is the main cause of death in patients with prostate cancer. It is necessary to establish a more accurate model to distinguish and predict patients with a high risk of metastasis to optimize individualized treatment. Methods: In this study, it was determined that hypoxia could affect the metastasis-free survival of patients with prostate cancer, and a hypoxia-related gene signature composed of seven genes for predicting metastasis was established and verified in different cohorts. The study further evaluated the effects of ALDOB expression on the proliferation and invasion of the LNCaP and DU145 cell lines under hypoxia and finally constructed a nomogram containing specific clinical characteristics of prostate cancer combined with the hypoxia gene signature to quantify the metastasis risk of individual patients. Results: The hypoxia-related gene signature was identified as an independent risk factor for metastasis-free survival in patients with prostate cancer. The expression of ALDOB increased under hypoxia and promoted the proliferation and invasion of LNCaP and DU145 cells. In addition, patients with a high risk score showed therapeutic resistance and immunosuppression. Compared with other parameters, the nomogram had the strongest predictive power and net clinical benefit. Conclusion: The study established a hypoxia-related gene signature and a nomogram to distinguish and predict patients with a high risk of prostate cancer metastasis, which may help to optimize individualized treatment and explore possible therapeutic targets.
| S-EPMC8548828 | biostudies-literature
Project description:Costimulatory molecules have been proven to enhance antitumor immune responses, but their roles in prostate cancer (PCa) remain unexplored. In this study, we aimed to explore the gene expression profiles of costimulatory molecule genes in PCa and construct a prognostic signature to improve treatment decision making and clinical outcomes. Five prognosis-related costimulatory molecule genes (RELT, TNFRSF25, EDA2R, TNFSF18, and TNFSF10) were identified, and a prognostic signature was constructed based on these five genes. This signature was an independent prognostic factor according to multivariate Cox regression analysis; it could stratify PCa patients into two subgroups with different prognoses and was highly associated with clinical features. The prognostic significance of the signature was well validated in four different independent external datasets. Moreover, patients identified as high risk based on our prognostic signature exhibited a high mutation frequency, a high level of immune cell infiltration and an immunosuppressive microenvironment. Therefore, our signature could provide clinicians with prognosis predictions and help guide treatment for PCa patients.
| S-EPMC8415313 | biostudies-literature
Project description:BackgroundImmune-related genes (IRGs) play important roles in the tumor immune microenvironment and can affect the prognosis of cancer. This study aimed to construct a novel IRG signature for prognostic evaluation of stage II colorectal cancer (CRC).MethodsGene expression profiles and clinical data for stage II CRC patients were collected from the Cancer Genome Atlas and Gene Expression Omnibus database. Univariate, multivariate Cox regression, and least absolute shrinkage and selection operator regression were used to develop the IRG signature, namely IRGCRCII. A nomogram was constructed, and the "Cell Type Identification by Estimating Relative Subsets of RNA Transcripts" (CIBERSORT) method was used to estimate immune cell infiltration. The expression levels of genes and proteins were validated by qRT-PCR and immunohistochemistry in 30 pairs of primary stage II CRC and matched normal tissues.ResultsA total of 466 patients with stage II CRC were included, and 274 differentially expressed IRGs were identified. Six differentially expressed IRGs were detected and used to construct the IRGCRCII signature, which could significantly stratify patients into high-risk and low-risk groups in terms of disease-free survival in three cohorts: training, test, and external validation (GSE39582). Receiver operating characteristics analysis revealed that the area under the curves of the IRGCRCII signature were significantly greater than those of the OncotypeDX colon signature at 1 (0.759 vs. 0.623), 3 (0.875 vs. 0.629), and 5 years (0.906 vs. 0.698) disease-free survival, respectively. The nomogram performed well in the concordance index (0.779) and calibration curves. The high-risk group had a significantly higher percentage of infiltrated immune cells (e.g., M2 macrophages, plasma cells, resting mast cells) than the low-risk group. Finally, the results of qRT-PCR and immunohistochemistry experiments performed on 30 pairs of clinical specimens were consistent with bioinformatics analysis.ConclusionThis study developed and validated a novel immune prognostic signature based on six differentially expressed IRGs for predicting disease-free survival and immune status in patients with stage II CRC, which may reflect immune dysregulation in the tumor immune microenvironment.
| S-EPMC8129521 | biostudies-literature
Project description:BackgroundAccumulating evidences demonstrated tumor microenvironment (TME) of bladder cancer (BLCA) may play a pivotal role in modulating tumorigenesis, progression, and alteration of biological features. Currently we aimed to establish a prognostic model based on TME-related gene expression for guiding clinical management of BLCA.MethodsWe employed ESTIMATE algorithm to evaluate TME cell infiltration in BLCA. The RNA-Seq data from The Cancer Genome Atlas (TCGA) database was used to screen out differentially expressed genes (DEGs). Underlying relationship between co-expression modules and TME was investigated via Weighted gene co-expression network analysis (WGCNA). COX regression and the least absolute shrinkage and selection operator (LASSO) analysis were applied for screening prognostic hub gene and establishing a risk predictive model. BLCA specimens and adjacent tissues from patients were obtained from patients. Bladder cancer (T24, EJ-m3) and bladder uroepithelial cell line (SVHUC1) were used for genes validation. qRT-PCR was employed to validate genes mRNA level in tissues and cell lines.Results365 BLCA samples and 19 adjacent normal samples were selected for identifying DEGs. 2141 DEGs were identified and used to construct co-expression network. Four modules (magenta, brown, yellow, purple) were regarded as TME regulatory modules through WGCNA and GO analysis. Furthermore, seven hub genes (ACAP1, ADAMTS9, TAP1, IFIT3, FBN1, FSTL1, COL6A2) were screened out to establish a risk predictive model via COX and LASSO regression. Survival analysis and ROC curve analysis indicated our predictive model had good performance on evaluating patients prognosis in different subgroup of BLCA. qRT-PCR result showed upregulation of ACAP1, IFIT3, TAP1 and downregulation of ADAMTS9, COL6A2, FSTL1,FBN1 in BLCA specimens and cell lines.ConclusionsOur study firstly integrated multiple TME-related genes to set up a risk predictive model. This model could accurately predict BLCA progression and prognosis, which offers clinical implication for risk stratification, immunotherapy drug screen and therapeutic decision.
| S-EPMC8194149 | biostudies-literature
Project description:BackgroundLiver cancer (LC) is one of the most fatal cancers throughout the world. More efficient and sensitive gene signatures that could accurately predict survival in LC patients are vitally needed to promote a better individualized and effective treatment.Material/methods422 LC and adjacent normal tissues with both RNA-Seq and clinical data in TCGA were embedded in our study. Gene set enrichment analysis (GSEA) was applied to identify genes and hallmark gene sets that are more valuable for liver cancer therapy. Cox regression analysis was used to identify genes related to overall survival (OS) and build the prediction model. cBioPortal database was used to examine the alterations of the panel mRNA signature. ROC curves and Kaplan-Meier curves were used to validate the prediction model. Besides, the expression of the genes in the model were validated using quantitative real-time PCR in clinical tissue specimens.ResultsThe panel of DNA repair-related mRNA signature consisted of seven mRNAs: RFC4 (replication factor C subunit 4), ZWINT (ZW10 interacting kinetochore protein), UPF3B (UPF3B regulator of nonsense mediated mRNA decay), NCBP2 (nuclear cap binding protein subunit 2), ADA (adenosine deaminase), SF3A3 (splicing factor 3a subunit 3) and GTF2H1 (general transcription factor IIH subunit 1). On-line analysis of cBioPortal database found that the expression of the panel mRNA has a wide variation ranging from 7 to 10%. All the mRNAs were significantly upregulated in LC tissues compared to normal tissues (P?<?0.05). The risk model is closely related to the OS of LC patients. The hazard ratio (HR) is 2.184 [95% CI (confidence interval) 1.523-3.132] and log-rank P-value?<?0.0001. For clinical specimen validation, we found that all of the genes in the model upregulated in liver cancer tissues versus normal liver tissues, which was consistent with the results predicted.ConclusionsOur study demonstrated a mRNA signature including seven mRNA for prognosis prediction of LC. This panel gene signature provides a new criterion for accurate diagnosis and therapeutic target of LC.
| S-EPMC7847017 | biostudies-literature
Project description:ObjectiveTo identify the gene subtypes related to immune cells of cholangiocarcinoma and construct an immune score model to predict the immunotherapy efficacy and prognosis for cholangiocarcinoma.MethodsBased on principal component analysis (PCA) algorithm, The Cancer Genome Atlas (TCGA)-cholangiocarcinoma, GSE107943 and E-MTAB-6389 datasets were combined as Joint data. Immune genes were downloaded from ImmPort. Univariate Cox survival analysis filtered prognostically associated immune genes, which would identify immune-related subtypes of cholangiocarcinoma. Least absolute shrinkage and selection operator (LASSO) further screened immune genes with prognosis values, and tumor immune score was calculated for patients with cholangiocarcinoma after the combination of the three datasets. Kaplan-Meier curve analysis determined the optimal cut-off value, which was applied for dividing cholangiocarcinoma patients into low and high immune score group. To explore the differences in tumor microenvironment and immunotherapy between immune cell-related subtypes and immune score groups of cholangiocarcinoma.Results34 prognostic immune genes and three immunocell-related subtypes with statistically significant prognosis (IC1, IC2 and IC3) were identified. Among them, IC1 and IC3 showed higher immune cell infiltration, and IC3 may be more suitable for immunotherapy and chemotherapy. 10 immune genes with prognostic significance were screened by LASSO regression analysis, and a tumor immune score model was constructed. Kaplan-Meier (KM) and receiver operating characteristic (ROC) analysis showed that RiskScore had excellent prognostic prediction ability. Immunohistochemical analysis showed that 6 gene (NLRX1, AKT1, CSRP1, LEP, MUC4 and SEMA4B) of 10 genes were abnormal expressions between cancer and paracancer tissue. Immune cells infiltration in high immune score group was generally increased, and it was more suitable for chemotherapy. In GSE112366-Crohn's disease dataset, 6 of 10 immune genes had expression differences between Crohn's disease and healthy control. The area under ROC obtained 0.671 based on 10-immune gene signature. Moreover, the model had a sound performance in Crohn's disease.ConclusionThe prediction of tumor immune score model in predicting immune microenvironment, immunotherapy and chemotherapy in patients with cholangiocarcinoma has shown its potential for indicating the effect of immunotherapy on patients with cholangiocarcinoma.
| S-EPMC9932535 | biostudies-literature
Project description:Accumulating evidence indicates that hypoxia is highly associated with bladder cancer genesis, progression, and immune microenvironment. Nevertheless, few studies have identified the role of hypoxia-related genes as a prognostic signature in bladder cancer. This study aimed to establish a hypoxia-related signature with high accuracy for prognosis and immune microenvironment prediction in bladder cancer. We obtained expression profiles and clinical information from Gene Expression Omnibus and The Cancer Genome Atlas. Then the univariate Cox regression, random survival forest algorithm, and multivariate Cox regression analysis were conducted to identify the core genes and four hypoxia-related genes (ANXA2, GALK1, COL5A1, and HS3ST1) were selected to construct the signature. Kaplan-Meier survival analysis demonstrated that patients with a low-risk score had a higher disease-specific survival rate (p < 0.0001). The areas under the curve of the signature were 0.829 at 1 year, 0.869 at 3 years, and 0.848 at 5 years, respectively. Additionally, we found this hypoxia-related signature was highly correlated with tumor immune microenvironment and had the potential to predict the efficacy of immunotherapy. In summary, our study developed a hypoxia-related signature, which had high accuracy for prognosis prediction and the potential to guide the immunotherapy for bladder cancer patients.
| S-EPMC8138130 | biostudies-literature