Hypoxia-related Genes as Prognostic Signature to Predict Prognosis and Immune Microenvironment of Colorectal Cancer Patients

Abstract Background: Hypoxia is widespread in solid tumors and is directly associated with colorectal cancer (CRC) aggressiveness, poor prognosis, and immunotherapy resistance. In this study, we aimed at developing a hypoxia-related marker to improve the prognosis prediction in CRC.Methods: We used gene expression data of CRC samples from the Cancer Genome Atlas Database and the hypoxia gene set to obtain a hypoxia gene expression matrice of 479 CRC patients. The prognostic model was constructed by screening hypoxia risk genes that were significantly associated with prognosis by univariate and multivariate Cox regression analysis. The predictive performance of the prognostic model was evaluated by Kaplan-Meier survival curve analyses and ROC curve analysis and validated in the GSE17536 dataset of Gene Expression Omnibus database. Finally, we analyzed the immune cell infiltration and expression of immunosuppressive genes in CRC patients at high and low risk of hypoxia.Results: We constructed a hypoxia risk prognostic model composed of two hypoxia-related genes (SLC2A3 and ENO3), which was proved to have better sensitivity and specificity after a series of validation. Independent prognostic analysis revealed that the risk score can serve as an independent prognostic factor for CRC. The infiltration of natural killer resting cells, activated master cells and T-cell regulatory cells were significantly increased in the hypoxia high-risk group, and Gene Set Enrichment Analysis showed that gene sets involved in tumor proliferation and differentiation, immune tolerance as well as hypoxia were also significantly enriched in this group. Negatively regulated genes in the Cancer Immunity Cycle, as well as immune checkpoints, were upregulated in the high hypoxia risk group, forming an immunosuppressive microenvironment, and mediating the immune escape. Conclusions: In summary, we constructed and validated a reliable hypoxia risk model that can independently predict the prognosis of CRC patients and reflect the status of the immune microenvironment, which is beneficial for screening CRC prognostic biomarkers and therapeutic targets.

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An autophagy-related long non-coding RNA signature for patients with colorectal cancer

Physiology International ◽

10.1556/2060.2021.00125 ◽

2021 ◽

Author(s):

Dongyan Zhao ◽

Xizhen Sun ◽

Sidan Long ◽

Shukun Yao

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

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Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.618976 ◽

2021 ◽

Vol 11 ◽

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Gene Set Enrichment ◽

Cox Regression Analysis ◽

Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

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Construction of a Prognostic Model for Predicting Overall Survival of Patients with Colorectal Cancer

10.21203/rs.3.rs-26225/v1 ◽

2020 ◽

Author(s):

Shuwen Han ◽

Kefeng Ding

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Levulinic Acid ◽

Prognostic Model ◽

Cox Regression ◽

Aminolevulinic Acid ◽

Risk Group ◽

Prognostic Models ◽

Cox Regression Analysis

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies. The purpose of this study is to construct a prognostic model for predicting the overall survival (OS) in patients with CRC. Methods: The mRNA-seq and miRNA-seq data of colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) were downloaded from The Cancer Genome Atlas (TCGA) database. The differentially expressed RNAs (DE-RNAs) between tumor and normal tissues were screened. The Kaplan-Meier and univariate Cox regression analysis were used to screen the survival-related genes. Functional enrichment analysis of survival-related genes was conducted, followed by protein-protein interaction (PPI) analysis. Subsequently, the potential drugs targeting differentially expressed mRNAs (DE-mRNAs) were investigated. Multivariate Cox regression analysis was then conducted to screen the independent prognostic factors, and these genes were used to establish a prognostic model. A receiver operator characteristic (ROC) curve was constructed, and the area under the curve (AUC) value of ROC was calculated to evaluate the specificity and sensitivity of the model. Results: A total of 855 survival-related genes were screened. These genes were mainly enriched in Gene Ontology (GO) terms, such as methylation, synapse organization, and methyltransferase activity; and pathway analysis showed that these genes were significantly involved in N-Glycan biosynthesis and the calcium signaling pathway. PPI analysis showed that aminolevulinate dehydratase (ALAD) and cholinergic receptor muscarinic 2 (CHRM2) served vital roles in the development of CRC. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for CRC treatment. The prognostic models were built and the patients were divided into high-risk and low-risk groups based on the median of risk score (RS) as screening threshold. The OS for patients in the high-risk group was markedly shorter than that for patients in the low-risk group. Meanwhile, kazal type serine peptidase inhibitor domain 1 (KAZALD1), hippocalcin like 4 (HPCAL4), cadherin 8 (CDH8), synaptopodin 2 (SYNPO2), cyclin D3 (CCND3), and hsa_mir_26b may be independent prognostic factors that could be considered as therapeutic targets for CRC.Conclusion: We established prognostic models that could predict the OS for CRC patients and may assist clinicians in providing personalized and precision treatment in this patient population.Highlights:1. ALAD served a vital role in the development of CRC.2. CHRM2 played a role in CRC development by affecting the calcium signaling pathway.3. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for treating CRC.4. KAZALD1 and HPCAL4 were associated with the OS of CRC.5. CDH8, SYNPO2, CCND3, and hsa-mir-26b were closely related to the prognostic of CRC staging.

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Exploring the Pyroptosis-related Genes of Breast Cancer and their Effects on Tumor Immune Microenvironment

10.21203/rs.3.rs-1049643/v1 ◽

2021 ◽

Author(s):

Congli Jia ◽

Fu Yang ◽

Ruining Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment

Abstract Background: Breast cancer （BC） is the most common cancer among women, with high rates of metastasis and recurrence. Some studies have confirmed that pyroptosis is an immune-related programmed cell death. However, the correlation between the expression of pyroptosis-related genes in BC and its prognosis remains unclear. Methods: In this study, we identified 38 pyroptosis-related genes that were differentially expressed between BC and normal tissues. The prognostic value of each pyroptosis-related gene was evaluated using patient data from The Cancer Genome Atlas (TCGA). The Cox regression method was performed to establish a prognostic model for 16-gene signature, classifying all BC patients in the TCGA database into a low-or high-risk group. Results: The survival rate of BC patients in the high-risk group was significantly lower than that in the low-risk group (P＜0.01). Prognostic model is independent prognostic factor for BC patients compared to clinical features. Single sample gene set enrichment analysis (ssGSEA) showed a decrease for immune cells and immune function in the high-risk group. Conclusions: Pyroptosis-related genes influence the tumor immune microenvironment and can predict the prognosis of BC.

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Immune prognostic implications of PSMD14 and its associated genes signatures in hepatocellular carcinoma

10.21203/rs.3.rs-20812/v1 ◽

2020 ◽

Author(s):

Chuan Tian ◽

Mubalake Abudoureyimu ◽

Xinrong Lin ◽

Hao Zhou ◽

Xiaoyuan Chu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

Clinicopathologic Characteristics ◽

Data Set ◽

Cox Regression Analysis ◽

Prognostic Implications

Abstract Background PSMD14 played a vital roles initiation and progression of hepatocellular carcinoma (HCC). However, PSMD14 and its-related genes for the immune prognostic implications of HCC patients have rarely been analyzed. Therefore, we aimed to explore gene signatures and immune prognostic values of PSMD14 and its-related genes in HCC. Method Analyzed the expression of PSMD14 in multiple databases, and clinicopathologic characteristics associated with PSMD14 overall survival using Wilcoxon signed-ranktest, logistic and Cox regression, Kaplan-Meier method. An immune prognostic signature (including RBM45, PSMD1, OLA1, CCT6A, LCAT and IVD) was constructed and validated using the co-expression and cox regression analyses in TCGA, ICGC and TIMER datasets. Gene Set Enrichment Analysis (GSEA) was performed using TCGA data set. Results Increased PSMD14 expression in HCC was significantly associated with poor prognosis and clinicopathologic characteristics (grade, histologic stage, surgical approach and T stage, all p-values < 0.05). A total of six PSMD14-related genes were detected, which markedly related to overall survival and immune infiltrating levels in HCC patients. Using cox regression analysis, the PSMD14 and its-related genes were found to be an independent prognostic factor for HCC survival. Calibration curves confirmed good consistency between clinical nomogram prediction and actual observation. Immune prognostic model suggests that patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. Conclusion We screened potential immune prognostic genes and constructed and verified a novel PSMD14-based prognostic model of HCC, which provides new potential prognostic biomarkers and therapeutic targets and lays a theoretical foundation for immunotherapy of HCC.

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Identification and validation of a novel zinc finger protein-related gene-based prognostic model for breast cancer

PeerJ ◽

10.7717/peerj.12276 ◽

2021 ◽

Vol 9 ◽

pp. e12276

Author(s):

Min Ye ◽

Liang Li ◽

Donghua Liu ◽

Qiuming Wang ◽

Yunuo Zhang ◽

...

Keyword(s):

Zinc Finger ◽

Prognostic Model ◽

Cox Regression ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Immune Microenvironment ◽

Gene Set Enrichment ◽

Model Estimate ◽

Gene Set ◽

Tumor Immune Microenvironment

Background Breast invasive carcinoma (BRCA) is a commonly occurring malignant tumor. Zinc finger proteins (ZNFs) constitute the largest transcription factor family in the human genome and play a mechanistic role in many cancers’ development. The prognostic value of ZNFs has yet to be approached systematically for BRCA. Methods We analyzed the data of a training set from The Cancer Genome Atlas (TCGA) database and two validation cohort from GSE20685 and METABRIC datasets, composed of 3,231 BRCA patients. After screening the differentially expressed ZNFs, univariate Cox regression, LASSO, and multiple Cox regression analysis were performed to construct a risk-based predictive model. ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and gene set enrichment analyses (GSEA) were utilized to assess the potential relations among the tumor immune microenvironment and ZNFs in BRCA. Results In this study, we profiled ZNF expression in TCGA based BRCA cohort and developed a novel prognostic model based on 14 genes with ZNF relations. This model was composed of high and low-score groups for BRCA classification. Based upon Kaplan-Meier survival curves, risk-status-based prognosis illustrated significant differences. We integrated the 14 ZNF-gene signature with patient clinicopathological data for nomogram construction with accurate 1-, 3-, and 5-overall survival predictive capabilities. We then accessed the Genomics of Drug Sensitivity in Cancer database for therapeutic drug response prediction of signature-defined BRCA patient groupings for our selected TCGA population. The signature also predicts sensitivity to chemotherapeutic and molecular-targeted agents in high- and low-risk patients afflicted with BRCA. Functional analysis suggested JAK STAT, VEGF, MAPK, NOTCH TOLL-like receptor, NOD-like receptor signaling pathways, apoptosis, and cancer-based pathways could be key for ZNF-related BRCA development. Interestingly, based on the results of ESTIMATE, ssGSEA, and GSEA analysis, we elucidated that our ZNF-gene signature had pivotal regulatory effects on the tumor immune microenvironment for BRCA. Conclusion Our findings shed light on the potential contribution of ZNFs to the pathogenesis of BRCA and may inform clinical practice to guide individualized treatment.

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Prognostic Model of Colorectal Cancer Constructed by Eight Immune-Related Genes

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.604252 ◽

2020 ◽

Vol 7 ◽

Author(s):

Shuting Wen ◽

Long He ◽

Zhuotai Zhong ◽

Hong Mi ◽

Fengbin Liu

Keyword(s):

Colorectal Cancer ◽

Prognostic Model ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Immune Cell Infiltration ◽

Cox Regression Analysis ◽

Prognostic Prediction ◽

Immune Related Genes

BackgroundColorectal cancer (CRC) is a common malignant tumor of the digestive tract with a high mortality rate. Growing evidence demonstrates that immune-related genes play a prominent role in the occurrence and development of CRC. The aim of this study was to investigate the prognostic value of immune-related genes in CRC.MethodsGene expression profiles and clinical data of 568 CRC and 44 non-tumorous tissues were obtained from The Cancer Genome Atlas (TCGA) database. First, we performed a differentially expressed gene (DEG) analysis and univariate Cox regression analysis to determine the DEGs associated with overall survival. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were subsequently performed for prognostic immune-related genes. Then, a multivariate Cox regression analysis was performed to establish the immune prognostic model and identify the independent prognostic factors of CRC. Next, in vitro experiments were done to further validate the model. Finally, we analyzed the correlation among immune-related genes, clinical traits, and immune cell infiltration.ResultsIn total, 3,702 DEGs were obtained, and 338 prognostic immune-related genes were identified. Among them, 45 genes were significantly correlated with the prognosis of CRC patients. A TF-mediated network was set up to explore its internal mechanism. GO and KEGG analyses further illustrated that these genes were enriched in immune-and inflammatory-related pathways. Then, a prognostic prediction model composed of eight immune-related genes (SLC10A2, UTS2, FGF2, UCN, IL1RL2, ESM1, ADIPOQ, and VIP) was constructed. The AUC of the ROC curve for 1, 3, 5, and 10 years overall survival (OS) was 0.751, 0.707, 0.680, and 0.729, respectively. The survival analysis suggested that the OS of the high-risk group was significantly poorer than that of the low-risk group. Meanwhile, in vitro assays revealed that ESM1 and SLC10A2 exert opposing roles in colon cancer cell proliferation, validating the accuracy of the model. The correlation analysis indicated that immune cell infiltration was positively related to the model.ConclusionThis study screened prognosis-related immune genes and developed a prognostic prediction model of CRC. These findings may help provide potential novel prognostic biomarkers and therapeutic targets for CRC. At the same time, the understanding of the CRC immune microenvironment status was deepened.

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Construction and Analysis of a Prognostic Model of Osteosarcoma Based on m6A-Related lncRNA

10.21203/rs.3.rs-966782/v1 ◽

2021 ◽

Author(s):

Zhian Ling ◽

Yuting Liang ◽

Suping Wei ◽

Yuanming Chen ◽

Jinmin Zhao

Keyword(s):

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lasso Regression ◽

Gene Set Enrichment ◽

Cox Regression Analysis

Abstract Background N6-methylandenosine (m6A) methylation is one of the most common methylation modifications in RNA. At present, a large number of studies have found that m6A methylation can regulate the occurrence and development of tumors by modifying mRNA. However, it is still unclear how m6A modifies Long non-coding RNA (lncRNA) that regulates mRNA expression by interacting with miRNA to affect the occurrence and development of osteosarcoma(OS). Therefore, exploring the lncRNAs related to m6A methylation and identifying lncRNAs that have both prognostic effects and immune functions are things that need to be solved urgently. Methods The published gene expression data of OS and complete clinical annotation files were obtained from the TARGET database. LncRNAs with P <0.001 from the results of Pearson correlation coefficient analysis as m6A-related lncRNAs were screened. Single-factor Cox regression analysis was used to screening prognostic- related lncRNA combined with the clinical information of patients and constructed a prognostic model based on lasso regression analysis. Then we explored the differences in survival and immune function of different subtypes that be obtained using the Consensus Cluster. The enrichment of differential genes between high and low risk groups in the KEGG pathway is achieved through Gene set enrichment analysis(GSEA). Results We obtained 706 lncRNAs in the TARGET database. Consensus clustering method were used to divide patients with OS into subgroups based on the expression of 26 prognostic-related lncRNAs. Through Kaplan-Meier survival analysis, there are significant differences between the two subgroups. The average immune score (P = 0.02), stromal score(P =0.027), and estimate score༈P = 0.015༉were higher in cluster 1 than in cluster 2. We found that compared with cluster 2, SIGLEC15, HAVCR2, LAG3, and PDCD1 were highly expressed in cluster 1.We obtain a prognostic model by lasso regression analysis. In the training group and the text group, the OS curve showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. In the training set, univariate Cox regression analysis and multivariate Cox regression analysis showed that the risk score was correlated with the prognosis of OS patients. In the high-risk group, the Linoleic acid metabolism and the Glycine, serine and threonine metabolism pathway were mainly involved by Gene Set Enrichment analysis. The abundance of Mast cells activated (P ≦0.024) and T cells CD4 (P ≦0.0044) naive were positively association the risk score. Conclusions This study clarified the important role of m6A-related lncRNAs in the prognosis and immune microenvironment of patients with OS, and indicate that m6A-related prognostic lncRNA signals may provide new targets for the diagnosis and treatment of OS.

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Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer

PeerJ ◽

10.7717/peerj.11219 ◽

2021 ◽

Vol 9 ◽

pp. e11219

Author(s):

Yandong Miao ◽

Hongling Zhang ◽

Bin Su ◽

Jiangtao Wang ◽

Wuxia Quan ◽

...

Keyword(s):

Colorectal Cancer ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Cox Regression Analysis ◽

Diagnosis And Prognosis

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

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Immune Prognostic Implications of PSMD14 and its Associated Genes Signatures in Hepatocellular Carcinoma

10.21203/rs.3.rs-132707/v1 ◽

2020 ◽

Author(s):

tian chuan ◽

Abudoureyimu Mubalake ◽

Lin xinrong ◽

Zhou hao ◽

Chu Xiaoyuan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

Clinicopathologic Characteristics ◽

Data Set ◽

Cox Regression Analysis ◽

Prognostic Implications

Abstract Background: PSMD14 played a vital roles initiation and progression of hepatocellular carcinoma (HCC). However, PSMD14 and its-related genes for the immune prognostic implications of HCC patients have rarely been analyzed. Therefore, we aimed to explore gene signatures and immune prognostic values of PSMD14 and its-related genes in HCC.Methods: Analyzed the expression of PSMD14 in multiple databases, and clinicopathologic characteristics associated with PSMD14 overall survival using Wilcoxon signed-ranktest, logistic and Cox regression, Kaplan-Meier method. An immune prognostic signature (including RBM45, PSMD1, OLA1, CCT6A, LCAT and IVD) was constructed and validated using the co-expression and cox regression analyses in TCGA, ICGC and TIMER datasets and CIBERSORT computational methods. Gene Set Enrichment Analysis (GSEA) was performed using TCGA data set. RT-PCR further validates the expression of seven immune genes in Hepatocellular carcinoma cells.Results: Increased PSMD14 expression in HCC was significantly associated with poor prognosis and clinicopathologic characteristics (grade, histologic stage, surgical approach and T stage, all p-values < 0.05 ). A total of six PSMD14-related genes were detected, which markedly related to overall survival and immune infiltrating levels in HCC patients. Using cox regression analysis, the PSMD14 and its-related genes were found to be an independent prognostic factor for HCC survival. Calibration curves confirmed good consistency between clinical nomogram prediction and actual observation. Immune prognostic model suggests that patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group.Conclusion: We screened potential immune prognostic genes and constructed and verified a novel PSMD14-based prognostic model of HCC, which provides new potential prognostic biomarkers and therapeutic targets and lays a theoretical foundation for immunotherapy of HCC.

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