A Prognostic Autophagy-Related Gene Pair Signature and Small-Molecule Drugs for Hepatocellular Carcinoma.
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ABSTRACT:
SUBMITTER: Song Z
PROVIDER: S-EPMC8419440 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Abnormal autophagic levels have been implicated in the pathogenesis of multiple cancers, however, its role in tumors is complex and has not yet been explored clearly. Hence, we aimed to explore the prognostic values of autophagy-related genes (ARGs) for kidney renal clear cell carcinoma (KIRC). Differentially expressed ARGs and transcription factors (TFs) were identified in KIRC patients obtaining from the The Cancer Genome Atlas (TCGA) database. Then, networks between TFs and ARGs, gene ontology functional annotations and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted. Next, we performed consensus clustering, COX regression analysis and Lasso regression analysis to identify the prognostic ARGs. Finally, an individual prognostic index (PI, riskScore) was established. Based on TCGA cohort and ArrayExpress cohort, Survival analysis, ROC curve, independent prognostic analysis, and clinical correlation analysis were also performed to evaluate this PI. Based on differentially expressed ARGs, KIRC patients were successfully divided into two clusters (P = 5.916e-04). AS for PI, it was constructed based on 11 ARGs and significantly classified KIRC patients into high-risk group and low-risk group in terms of OS (P = 4.885e-15 for TCGA cohort, P = 6.366e-03 for ArrayExpress cohort). AUC of its ROC curve reached 0.747 for TCGA cohort and 0.779 for ArrayExpress cohort. What's more, this PI was proven to be a valuable independent prognostic factor in both univariate and multivariate COX regression analysis (P < .001). Prognostic nomograms were also performed to visualize the relationship between individual predictors and survival rates in patients with KIRC. By means of connectivity map database, emetine, cephaeline and co-dergocrine mesilate related to ARGs were found to be negatively correlated with KIRC. This study provided an effective PI for KIRC and also displayed networks between TFs and ARGs. KIRC patients were successfully divided into two clusters based on differentially expressed ARGs. Besides, small molecule drugs related to ARGs were also identified for KIRC.
| S-EPMC7541166 | biostudies-literature
Project description:ObjectiveTo explore the autophagy-related prognostic signature (ARPs) via data mining in gene expression profiles for glioblastoma (GBM).MethodsUsing the Cancer Genome Atlas (TCGA) database, we obtained 156 GBM samples and 5 adjacent normal samples, and denoted them as discovery cohort. Univariate Cox regression analysis was used to screen autophagy genes that related to GBM prognosis. Then, the least absolute shrinkage and selection operator Cox regression model was used to construct an autophagy-based ARPs, which was validated in an external cohort containing 80 GBM samples. The patients in the above-mentioned cohorts were divided into low-risk group and high-risk group according to the median prognostic risk score, and the diagnostic performance of the model was assessed by receiver operating characteristic curve analyses. The gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were performed between the high-risk and low-risk patients. Additionally, the genetic features of ARPs, such as genetic variation profiles, correlations with tumor-infiltrating lymphocytes (TILs), and potential drug sensitivity, were further assessed in the TCGA-GBM data set.ResultsA signature of ARPs including NDUFB9, BAK1, SUPT3H, GAPDH, CDKN1B, CHMP6, and EGFR were detected and validated. We identified a autophagy-related prognosis 7-gene signature correlated survival prognosis, immune infiltration, level of cytokines, and cytokine receptor in tumor microenvironment. Furthermore, the signature was tested in several pathways related to disorders of tumor microenvironment, as well as cancer-related pathways. Additionally, a range of small molecular drugs, shown to have a potential therapeutic effect on GBM.ConclusionsWe constructed an autophagy-based 7-gene signature, which could serve as an independent prognostic indicator for cases of GBM and sheds light on the role of autophagy as a potential therapeutic target in GBM.
| S-EPMC9097333 | biostudies-literature
Project description:BackgroundlncRNA may be involved in the occurrence, metastasis, and chemical reaction of hepatocellular carcinoma (HCC) through various pathways associated with autophagy. Therefore, it is urgent to reveal more autophagy-related lncRNAs, explore these lncRNAs' clinical significance, and find new targeted treatment strategies.MethodsThe corresponding data of HCC patients and autophagy genes were obtained from the TCGA database, and the human autophagy database respectively. Based on the co-expression and Cox regression analysis to construct prognostic prediction signature.ResultsFinally, a signature containing seven autophagy-related lncRNAs (PRRT3-AS1, RP11-479G22.8, RP11-73M18.8, LINC01138, CTD-2510F5.4, CTC-297N7.9, RP11-324I22.4) was constructed. Based on the risk score of signature, Overall survival (OS) curves show that the OS of high-risk patients is significantly lower than that of low-risk patients (P = 2.292e-10), and the prognostic prediction accuracy of risk score (AUC = 0.786) is significantly higher than that of ALBI (0.532), child_pugh (0.573), AFP (0.5751), and AJCC_stage (0.631). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are obviously accuracy by the combined analysis of the risk score, child_pugh, age, M_stage, and Grade (The AUC of 1- and 3-years are 0.87, and 0.855). Remarkably, the 7 autophagy-related lncRNAs may participate in Spliceosome, Cell cycle, RNA transport, DNA replication, and mRNA surveillance pathway and be related to the biological process of RNA splicing and mRNA splicing.ConclusionIn conclusion, the 7 autophagy-related lncRNAs might be promising prognostic and therapeutic targets for HCC.
| S-EPMC8080392 | biostudies-literature
Project description:This study aimed to develop a prognostic model for hepatocellular carcinoma (HCC) based on immune-related genes and to identify new potential small-molecule drugs. A differential gene expression analysis of high-throughput microarray data from The Cancer Genome Atlas (TCGA) was performed to identify immune-related genes. By comparison with an immune-related genome, nine genes with important prognostic value for HCC were identified. The prognostic characteristics were established based on univariate and multivariate COX and Lasso regression analyzes. Subsequently, immune-related HCC risk signatures were constructed based on these identified nine immune-related genes and patients were classified as being at high or low risk according to these signatures. The overall survival (OS) time of high-risk patients was significantly shorter than that of low-risk patients. When studied as an independent prognostic factor of HCC, the significant prognostic value of this feature can be seen in the stratified cohorts. For clinical application, it was developed a nomogram that includes nine clinical risk factors and the prognostic model built based on the identified immune-related genes. Internal and external verification on 243 HCC tissues through International Cancer Genome Consortium (ICGC) database were performed to make this model more accurate and reliable. In addition, it was observed a positive regulation between the identified immune-related genes and their transcription factors found in HCC patients. Moreover, physiological function and signaling pathway of identified immune-related genes were studied by GO and KEGG enrichment analysis. Finally, several new small molecular drugs with potential for the treatment of HCC have been identified in the CMap database.
| S-EPMC7540131 | biostudies-literature
Project description:Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.
| S-EPMC7831610 | biostudies-literature
Project description:Due to the high mortality of hepatocellular carcinoma (HCC), its prognostic models are urgently needed. Bile acid (BA) metabolic disturbance participates in hepatocarcinogenesis. We aim to develop a BA-related gene signature for HCC patients. Research data of HCC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) online databases. After least absolute shrinkage and selection operator (LASSO) regression analysis, we developed a BA-related prognostic signature in TCGA cohort based on differentially expressed prognostic BA-related genes. Then, the predictive performance of the signature was evaluated and verified in TCGA and ICGC cohort respectively. We obtained the risk score of each HCC patient according to the model. The differences of immune status and drug sensitivity were compared in patients that were stratified based on risk score. The protein and mRNA levels of the modeling genes were validated in the Human Protein Atlas database and our cell lines, respectively. In TCGA cohort, we selected 4 BA-related genes to construct the first BA-related prognostic signature. The risk signature exhibited good discrimination and predictive ability, which was verified in ICGC cohort. Patients were classified into high- and low-risk groups according to their median scores. The occurrence of death increased with increasing risk score. Low-risk patients owned favorable overall survival. High-risk patients possessed high immune checkpoint expression and low IC50 values for sorafenib, cisplatin and doxorubicin. Real-time quantitative PCR and immunohistochemical results validate expression of modeling genes in the signature. We constructed the first BA-related gene signature, which might help to identify HCC patients with poor prognosis and guide individualized treatment.
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Project description:Hepatocellular carcinoma (HCC) is one of the deadliest cancers. Fatty acid metabolism (FAM) is associated with the development and treatment of HCC. This study aimed to build a FAM-related gene model to assess the prognosis of HCC and provide guidance for individual treatment. RNA-sequencing data of patients with HCC from The Cancer Genome Atlas and Gene Expression Omnibus database (GSE14520) were extracted as the training and validation sets, respectively. A FAM-related gene predictive signature was built, and the performance of prognostic model was assessed. The immune infiltration and drug sensitivity were also evaluated. Quantitative real-time polymerase chain reaction and western blot were performed to evaluate the levels of the model genes. A 12-gene FAM-related risk signature was constructed; patients with a higher risk score had poorer prognosis than those with a lower risk score. Risk score was shown as an independent risk factor for overall survival of HCC, and the signature was further confirmed as an effective and accurate model. A nomogram was constructed, and it exhibited the good performance in the prognostic prediction. In addition, the immune cell infiltration and sensitivity to chemotherapy drugs were correlated with different risk levels. Finally, quantitative real-time polymerase chain reaction and western blot proved the changes of above genes. Differential expression of FAM-related genes can be used to predict response to immunotherapy and chemotherapy, and improve the clinical prognosis evaluation of patients with HCC, which provides new clues for further experimental exploration and verification on FAM-related genes in HCC.
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Project description:Hepatocellular carcinoma (HCC) has been recognized as the third leading cause of cancer-related deaths worldwide. There is increasing evidence that the abnormal expression of autophagy-related genes plays an important role in the occurrence and development of HCC. Therefore, the study of autophagy-related genes can further elucidate the genetic drivers of cancer and provide valuable therapeutic targets for clinical treatment. In this study, we used 232 autophagy-related genes extracted from the Human Autophagy Database (HADb) and Molecular Signatures Database (MSigDB) to construct 1884 autophagy-related gene pairs. On this basis, we developed a prognostic model based on autophagy-related gene pairs using least absolute shrinkage and selection operator (LASSO) Cox regression to evaluate the prognosis of patients after liver cancer resection. We then used 845 liver cancer samples from three different databases to test the reliability of the risk signature through survival analysis, receiver operating characteristic (ROC) curve analysis, univariate and multivariate analysis. To further explore the underlying biological mechanisms, we conducted an enrichment analysis of autophagy-related genes. Finally, we combined the signature with independent prognostic factors to construct a nomogram. Based on the autophagy-related gene pair (ARGP) signature, we can divide patients into high- or low-risk groups. Survival analysis and ROC curve analysis verified the validity of the signature (AUC: 0.786-0.828). Multivariate Cox regression showed that the risk score can be used as an independent predictor of the clinical outcomes of liver cancer patients. Notably, this model has a more accurate predictive effect than most prognostic models for hepatocellular carcinoma. Moreover, our model is a powerful supplement to the HCC staging indicator, and a nomogram comprising both indicators can provide a better prognostic effect. Based on pairs of multiple autophagy-related genes, we proposed a prognostic model for predicting the overall survival rate of HCC patients after surgery, which is a promising prognostic indicator. This study confirms the importance of autophagy in the occurrence and development of HCC, and also provides potential biomarkers for targeted treatments.
| S-EPMC8173133 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is a highly malignant and aggressive cancer with high recurrence rates and mortality. Some studies have illustrated that RNA binding proteins (RBPs) were involved in the carcinogenesis and development of multiple cancers, but the roles in HCC were still unclear. We downloaded the RNA-seq and corresponding clinical information of HCC from The Cancer Genome Atlas (TCGA) database, and 330 differentially expressed RBPs were identified between normal and HCC tissues. Through series of the univariate, the least absolute shrinkage selection operator (LASSO), and the stepwise multivariate Cox regression analyses, six prognosis-related key RBPs (CNOT6, UPF3B, MRPL54, ZC3H13, IFIT5, and PPARGC1A) were screened out from DE RBPs, and a six-RBP gene risk score signature was constructed in training set. Survival analysis indicated that HCC patients with high-risk scores had significantly worse overall survival than low-risk patients, and furthermore, the signature can be used as an independent prognostic indicator. The good accuracy of this prognostic signature was confirmed by the ROC curve analysis and was further validated in the International Cancer Genome Consortium (ICGC) HCC cohort. Besides, a nomogram based on six RBP genes was established and internally validated in the TCGA cohort. Gene set enrichment analysis demonstrated some cancer-related phenotypes were significantly gathered in the high-risk group. Overall, our study first identified an RBP-related six-gene prognostic signature, which could serve as a promising prognostic biomarker and provide some potential therapeutic targets for HCC.
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Project description:Hepatocellular carcinoma (HCC) is a cancer with a poor prognosis. Many recent studies have suggested that pyroptosis is important in tumour progression. However, the role of pyroptosis-related genes (PRGs) in HCC remains unclear. We identified differentially expressed PRGs in tumours versus normal tissues. Through univariate, LASSO, and multivariate Cox regression analyses, a prognostic PRG signature was established. The signature effectiveness was evaluated by time-dependent receiver operating characteristic (t-ROC) curve and Kaplan-Meier (KM) survival analysis. The signature was validated in the ICGC (LIRI-JP) cohort. In addition, single-sample gene enrichment analysis (ssGSEA) showed the infiltration of major immune cell types and the activity of common immune pathways in different subgroups. Twenty-nine pyroptosis-related DEGs from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset were detected, and four genes (CTSV, CXCL8, MKI67 and PRF1) among them were selected to construct a prognostic signature. Then, the patients were divided into high- and low-risk groups. The pyroptosis-related signature was significantly associated with overall survival (OS). In addition, the patients in the high-risk group had lower levels of immune infiltration. The prognostic signature for HCC based on 4 pyroptosis-related genes has reliable prognostic and predictive value for HCC patients.
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