A novel RNA binding protein-associated prognostic model to predict overall survival in hepatocellular carcinoma patients

Hepatocellular carcinoma (HCC) is 1 of the deadliest malignancies worldwide. Despite significant advances in diagnosis and treatment, the mortality rate from HCC persists at a substantial level. Construction of a prognostic model that can reliably predict HCC patients’ overall survival is urgently needed.Two RNA-seq dataset (the Cancer Genome Atlas and International Cancer Genome Consortium) and 1 microarray dataset ({"type":"entrez-geo","attrs":{"text":"GSE14520","term_id":"14520"}}GSE14520) were included in our study. RNA-binding proteins (RBPs) in HCC patients was examined by differentially expressed genes analysis, functional enrichment analysis and protein-protein interaction network analysis. Subsequently, the Cancer Genome Atlas dataset was randomly divided into training and testing cohort with a prognostic model developed in the training cohort. In order to evaluate the prognostic value of the model, a comprehensive survival assessment was conducted.Five RBPs (ribosomal protein L10-like, enhancer of zeste homolog 2 (EZH2), peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A), zinc finger protein 239, interferon-induced protein with tetratricopeptide repeats 1) were used to construct the model. The model accurately predicted the prognosis of liver cancer patients in both the training cohort and validation cohort. HCC patients could be assigned into a high-risk group and a low-risk group by this model, and the overall survival of these 2 groups was significantly different (P < .05). Furthermore, the risk scores obtained by this model were highly correlated with immune cell infiltration.The prognostic model helps to identify HCC patients at high risk of mortality, which optimizes decision-making for individualized treatment.     

Integrated analysis of ceRNA network in hepatocellular carcinoma using bioinformatics analysis

Background:Long noncoding RNAs (lncRNAs) can work as microRNA (miRNA) sponges through a competitive endogenous RNA (ceRNA) mechanism. LncRNAs and miRNAs are important components of competitive endogenous binding, and their expression imbalance in hepatocellular carcinoma (HCC) is closely related to tumor development, diagnosis, and prognosis. This study explored the potential impact of the ceRNA regulatory network in HCC on the prognosis of HCC patients.Methods:We thoroughly researched the differential expression profiles of lncRNAs, miRNAs, and mRNAs from 2 HCC Gene Expression Omnibus datasets ({"type":"entrez-geo","attrs":{"text":"GSE98269","term_id":"98269"}}GSE98269 and {"type":"entrez-geo","attrs":{"text":"GSE60502","term_id":"60502"}}GSE60502). Then, a dysregulated ceRNA network was constructed by bioinformatics. In addition, hub genes in the ceRNA network were screened by Cytoscape, these hub genes functional analysis was performed by gene set enrichment analysis, and the expression of these hub genes in tumors and their correlation with patient prognosis were verified with Gene Expression Profiling Interactive Analysis.Results:A ceRNA network was successfully constructed in this study including 4 differentially expressed (DE) lncRNAs, 7 DEmiRNAs, and 166 DEmRNAs. Importantly, 4 core genes (CCNA2, CHEK1, FOXM1, and MCM2) that were significantly associated with HCC prognosis were identified.Conclusions:Our study provides comprehensive and meaningful insights into HCC tumorigenesis and the underlying molecular mechanisms of ceRNA. Furthermore, the specific ceRNAs can be further used as potential therapeutic targets and prognostic biomarkers for HCC.     

Identification and validation of miR-509-5p as a prognosticator for favorable survival in osteosarcoma

Osteosarcoma (OS) is the most common primary bone cancer diagnosed in children. This study aims to explore the aberrantly expressed miRNAs that are prognostically related and to provide potential biomarkers for the prognosis prediction of OS. The miRNA profiles of OS and adjacent normal controls were obtained from 2 gene expression omnibus cohorts (i.e., {"type":"entrez-geo","attrs":{"text":"GSE28423","term_id":"28423"}}GSE28423 and {"type":"entrez-geo","attrs":{"text":"GSE65071","term_id":"65071"}}GSE65071). {"type":"entrez-geo","attrs":{"text":"GSE39058","term_id":"39058"}}GSE39058 and Therapeutically Applicable Research to Generate Effective Treatments cohorts, which respectively contained 91 and 85 OS samples with both miRNA expression and clinical characteristics, were employed to perform survival and multivariate Cox regression analyses. Lymphocyte infiltration abundance between distinct subgroups was evaluated with the CIBERSORT algorithm and a previously proposed method. Gene set enrichment analysis was used to infer the dysregulated signaling pathways within each subgroup. Of the 31 differentially expressed miRNAs, miR-509-5p (miR-509) was the most significantly prognostic miRNA in the {"type":"entrez-geo","attrs":{"text":"GSE39058","term_id":"39058"}}GSE39058 cohort and its high expression was associated with the better OS prognosis (Log-rank P = .008). In the Therapeutically Applicable Research to Generate Effective Treatments validation cohort, the association of high miR-509 expression with favorable survival was also observed (Log-rank P = .014). The results remained still significant even adjusted for clinical confounding factors in multivariate Cox regression models. Further immunology analyses demonstrated that elevated infiltration of lymphocytes, decreased infiltration of immune-suppressive cells, and immune response-related pathways were significantly enriched in patients with miR-509 high expression. Our study suggests that miR-509 may serve as a potential biomarker for evaluating OS prognosis and provides clues for tailoring OS immunotherapy strategies.     

A novel risk signature with 6 RNA binding proteins for prognosis prediction in patients with glioblastoma

Recent studies suggested that RNA binding proteins (RBPs) were related to the tumorigenesis and progression of glioma. This study was conducted to identify prognostic RBPs of glioblastoma (GBM) and construct an RBP signature to predict the prognosis of GBM.Univariate Cox regression analysis was carried out to identify the RBPs associated with overall survival of GBM in the The Cancer Genome Atlas (TCGA), {"type":"entrez-geo","attrs":{"text":"GSE16011","term_id":"16011"}}GSE16011, and Repository for Molecular Brain Neoplasia data (Rembrandt) datasets, respectively. Overlapping RBPs from the TCGA, {"type":"entrez-geo","attrs":{"text":"GSE16011","term_id":"16011"}}GSE16011, and Rembrandt datasets were selected. The biological role of prognostic RBPs was assessed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein–protein interaction analyses. Least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis were used to construct an RBP-related risk signature. The prognostic value of RBP signature was measured by Kaplan–Meier method and time-dependent receiver operating characteristic curve. A nomogram based on independent prognostic factors was established to predict survival for GBM. The CGGA cohort was used as the validation cohort for external validation.This study identified 27 RBPs associated with the prognosis of GBM and constructed a 6-RPBs signature. Kaplan–Meier curves suggested that high-risk score was associated with a poor prognosis. Area under the curve of 1-, 3-, and 5-year overall survival was 0.618, 0.728, and 0.833 for TCGA cohort, 0.655, 0.909, and 0.911 for {"type":"entrez-geo","attrs":{"text":"GSE16011","term_id":"16011"}}GSE16011 cohort, and 0.665, 0.792, and 0.781 for Rembrandt cohort, respectively. A nomogram with 4 parameters (age, chemotherapy, O⁶-methylguanine-DNA methyltransferase promoter status, and risk score) was constructed. The calibration curve showed that the nomogram prediction was in good agreement with the actual observation.The 6-RBPs signature could effectively predict the prognosis of GBM, and our findings supplemented the prognostic index of GBM to a certain extent.     

Identification of Key Genes for Hepatitis Delta Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis

Background: It has been proposed that hepatitis delta virus (HDV) induces hepatic carcinogenesis by distinct molecular events compared with hepatocellular carcinoma (HCC) that is commonly induced by other hepatitis viruses. This study aimed to explore the underlying mechanism by identifying the key genes for HDV-HCC using bioinformatics analysis.Methods: The {"type":"entrez-geo","attrs":{"text":"GSE107170","term_id":"107170"}}GSE107170 dataset was downloaded and the differentially expressed genes (DEGs) were obtained by the online tool GEO2R. Gene otology (GO) functional analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R packages. The protein-protein interaction (PPI) network was constructed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Hub genes were selected by Cytoscape software according to degree algorithm. The hub genes were further validated in terms of expression and survival analysis based on public databases.Results: A total of 93 commonly upregulated genes and 36 commonly downregulated genes were found. The top 5 upregulated hub genes were TFRC, ACTR2, ARPC1A, ARPC3, and ARPC2. The top 5 downregulated hub genes were CTNNB1, CCND1, CDKN1B, CDK4, and CDKN1A. In the validation analysis, the expressions of ARPC1A, ARPC3, and CDK4 were promoted in general liver cancer samples. Higher expressions of ARPC2 and CDK4 and lower expressions of CDKN1A, CCND1, and CDKN1B were associated with worse prognosis in general HCC patients.Conclusion: The present study identifies a series of key genes that may be involved in the carcinogenesis of HDV-HCC and used as prognostic factors.     

Exploring new targets for the treatment of hepatitis-B virus and hepatitis-B virus-associated hepatocellular carcinoma: A new perspective in bioinformatics

Background:Hepatitis B Virus (HBV) infection is a global public health problem. After infection, patients experience a natural course from chronic hepatitis to cirrhosis and even Hepatitis B associated Hepatocellular Carcinoma (HBV-HCC). With the multi-omics research, many differentially expressed genes from chronic hepatitis to HCC stages have been discovered. All these provide important clues for new biomarkers and therapeutic targets. The purpose of this study is to explore the differential gene expression of HBV and HBV-related liver cancer, and analyze their enrichments and significance of related pathways.Methods:In this study, we downloaded four microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE121248","term_id":"121248"}}GSE121248, {"type":"entrez-geo","attrs":{"text":"GSE67764","term_id":"67764"}}GSE67764, {"type":"entrez-geo","attrs":{"text":"GSE55092","term_id":"55092"}}GSE55092, {"type":"entrez-geo","attrs":{"text":"GSE55092","term_id":"55092"}}GSE55092 and {"type":"entrez-geo","attrs":{"text":"GSE83148","term_id":"83148"}}GSE83148 from the Gene Expression Omnibus (GEO) database. Using these four datasets, patients with chronic hepatitis B (CHB) differentially expressed genes (CHB DEGs) and patients with HBV-related HCC differentially expressed genes (HBV-HCC DEGs) were identified. Then Protein–protein Interaction (PPI) network analysis, Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to excavate the functional interaction of these two groups of DEGs and the common DEGs. Finally, the Kaplan website was used to analyze the role of these genes in HCC prognostic.Results:A total of 241 CHB DEGs, 276 HBV-HCC DEGs, and 4 common DEGs (cytochrome P450 family 26 subfamily A member 1 (CYP26A1), family with sequence similarity 110 member C(FAM110C), SET and MYND domain containing 3(SMYD3) and zymogen granule protein 16(ZG16)) were identified. CYP26A1, FAM110C, SMYD3 and ZG16 exist in 4 models and interact with 33 genes in the PPI network of CHB and HBV-HCC DEGs,. GO function analysis showed that: CYP26A1, FAM110C, SMYD3, ZG16, and the 33 genes in their models mainly affect the regulation of synaptic vesicle transport, tangential migration from the subventricular zone to the olfactory bulb, cellular response to manganese ion, protein localization to mitochondrion, cellular response to dopamine, negative regulation of neuron death in the biological process of CHB. In the biological process of HBV-HCC, they mainly affect tryptophan catabolic process, ethanol oxidation, drug metabolic process, tryptophan catabolic process to kynurenine, xenobiotic metabolic process, retinoic acid metabolic process, steroid metabolic process, retinoid metabolic process, steroid catabolic process, retinal metabolic process, and rogen metabolic process. The analysis of the 4 common DEGs related to the prognosis of liver cancer showed that: CYP26A1, FAM110C, SMYD3 and ZG16 are closely related to the development of liver cancer and patient survival. Besides, further investigation of the research status of the four genes showed that CYP26A1 and SMYD3 could also affect HBV replication and the prognosis of liver cancer.Conclusion:CYP26A1, FAM110C, SMYD3 and ZG16 are unique genes to differentiate HBV infection and HBV-related HCC, and expected to be novel targets for HBV-related HCC occurrence and prognostic judgement.     

Development of a prognostic scoring system for resectable hepatocellular carcinoma

AIMTo develop a prognostic scoring system for overall survival (OS) of patients undergoing liver resection (LR) for hepatocellular carcinoma (HCC).METHODSConsecutive patients who underwent curative LR for HCC between 2000 and 2013 were identified. The series was randomly divided into a training and a validation set. A multivariable Cox model for OS was fitted to the training set. The beta coefficients derived from the Cox model were used to define a prognostic scoring system for OS. The survival stratification was then tested, and the prognostic scoring system was compared with the European Association for the Study of the Liver (EASL)/American Association for the Study of Liver Diseases (AASLD) surgical criteria by means of Harrell’s C statistics.RESULTSA total of 917 patients were considered. Five variables independently correlated with post-LR survival: Model for End-stage Liver Disease score, hepatitis C virus infection, number of nodules, largest diameter and vascular invasion. Three risk classes were identified, and OS for the three risk classes was significantly different both in the training (P < 0.0001) and the validation set (P = 0.0002). Overall, 69.4% of patients were in the low-risk class, whereas only 37.8% were eligible to surgery according to EASL/AASLD. Survival of patients in the low-risk class was not significantly different compared with surgical indication for EASL/AASLD guidelines (77.2 mo vs 82.5 mo respectively, P = 0.22). Comparison of Harrell’s C statistics revealed no significant difference in predictive power between the two systems (-0.00999, P = 0.667).CONCLUSIONThis study established a new prognostic scoring system that may stratify HCC patients suitable for surgery, expanding surgical eligibility with respect to EASL/AASLD criteria with no harm on survival.     

Construction and validation of a robust ferroptosis-associated gene signature predictive of prognosis in lung adenocarcinoma

To construct and validate a ferroptosis-associated signature predictive of prognosis in lung adenocarcinoma (LUAD), and systematically evaluate the underlying molecular connections in cancer biology.We retrieved mRNAs sequencing profiles of LUAD from the cancer genome atlas (TCGA) data portal and clinical information from the cBio Cancer Genomics Portal. The differentially expressed ferroptosis-associated genes (DEFAGs) were screened between normal samples and LUAD by packages “limma” in R. Then the total TCGA cohort was randomly divided into training set and testing set. Based on the training set, a DEFAG signature was built and further validated in the test set, the total TCGA cohort and other independent cohorts from the gene expression omnibus data portal. A nomogram was constructed and validated, and the correlation between high-risk group and cancer biology was further evaluated.We initially identified 68 DEFAGs from TCGA cohort. A 6 DEFAG signature was built and further validated in the test set, the total TCGA cohort and other 2 independent cohorts including {"type":"entrez-geo","attrs":{"text":"GSE31210","term_id":"31210"}}GSE31210 and {"type":"entrez-geo","attrs":{"text":"GSE72094","term_id":"72094"}}GSE72094 from gene expression omnibus data portal. Further exploration indicated that high-risk group combined with TP53 mutation harbored the most unfavorable prognosis while low-risk group with TP53 wild-type status had the most favorable survival advantage over other groups. Moreover, high-risk group was associated with higher cancer stemness, tumor mutation burden, and CD274 (programmed cell death 1 ligand 1) expression.We constructed a robust ferroptosis-associated gene signature and a nomogram predictive of prognosis in LUAD, and provided a new perspective on associations between ferroptosis and cancer.     

Identification of candidate biomarkers correlated with the pathogenesis and prognosis of breast cancer via integrated bioinformatics analysis

Background:This study was carried out to identify potential key genes associated with the pathogenesis and prognosis of breast cancer (BC).Methods:Seven GEO datasets ({"type":"entrez-geo","attrs":{"text":"GSE24124","term_id":"24124"}}GSE24124, {"type":"entrez-geo","attrs":{"text":"GSE32641","term_id":"32641"}}GSE32641, {"type":"entrez-geo","attrs":{"text":"GSE36295","term_id":"36295"}}GSE36295, {"type":"entrez-geo","attrs":{"text":"GSE42568","term_id":"42568"}}GSE42568, {"type":"entrez-geo","attrs":{"text":"GSE53752","term_id":"53752"}}GSE53752, {"type":"entrez-geo","attrs":{"text":"GSE70947","term_id":"70947"}}GSE70947, {"type":"entrez-geo","attrs":{"text":"GSE109169","term_id":"109169"}}GSE109169) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between BC and normal breast tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Hub genes related to the pathogenesis and prognosis of BC were verified by employing protein–protein interaction (PPI) network.Results:Ten hub genes with high degree were identified, including CDK1, CDC20, CCNA2, CCNB1, CCNB2, BUB1, BUB1B, CDCA8, KIF11, and TOP2A. Lastly, the Kaplan–Meier plotter (KM plotter) online database demonstrated that higher expression levels of these genes were related to lower overall survival. Experimental validation showed that all 10 hub genes had the same expression trend as predicted.Conclusion:The findings of this research would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of BC, which could be used as a new biomarker for diagnosis and to guide the combination medicine of BC.     

Diagnosis,clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction—a bioinformatics analysis

BackgroundAccurate myocardial infarction (AMI) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) modification plays an important role in the development of cardiac remodeling and the cardiomyocyte contractile function. The aim of this study is to analyze the m6A-related molecular biological mechanisms of AMI in terms of accurate diagnosis and prognosis.MethodsThe platform data and probe data of the {"type":"entrez-geo","attrs":{"text":"GSE66360","term_id":"66360"}}GSE66360 data set were downloaded. The differential analysis was conducted by combining the m6A-related gene expression. Thereafter, a diagnostic model was established using the random-forest method. The diagnostic accuracy of the diagnostic models was assessed by using the area under the receiver operating characteristic (ROC) curve (AUC). Next, the patients with AMI were clustered by unsupervised machine learning using the R software. Finally, an immune cell clustering analysis for each cluster was conducted to determine the correlations between m6A-related gene expression and the infiltration amount of the immune cells. The case and control groups were not matched in terms of demographics.ResultsThe {"type":"entrez-geo","attrs":{"text":"GSE6636","term_id":"6636"}}GSE6636 data set comprised 99 participants (49 patients with AMI and 50 without in control group). The differential analysis identified 10 m6A-related genes: 5 writers [Methyltransferase-like 3 (METTL3), Methyltransferase-like 14 (METTL14), Wilms tumor 1-associated protein (WTAP), Zinc Finger CCCH-Type Containing 13 (ZC3H13), and Casitas B-lineage proto-oncogene like 1 (CBLL1)], 4 readers [YT521-B homology domain-containing family 3 (YTHDF3), Fragile X mental retardation type 1 (FMR1), YT521-B homology-domain-containing protein 1 (YTHDC1), and insulin-like growth factor binding protein 3 (IGFBP3)] and 1 eraser [fat mass and obesity associated (FTO) gene]. The Mean Decrease Gini (MDG) values of these 10 genes were greater than 2. The FTO, WTAP, YTHDC1, IGFBP3, and CBLL1 were included in the model with a C index of 0.842. METTL3, ZC3H13, WTAP, and CBLL1 were highly expressed in Type A, and YTHDF3 was highly expressed in Type B.ConclusionsA diagnostic model of AMI was established based on the genes of FTO, WTAP, YTHDC1, IGFBP3, and CBLL1. Additionally, 2 molecular subtypes were successfully identified from the above-mentioned gene. Our findings could provide a novel method for the accurate diagnosis of AMI.     

Huanglian decoction suppresses the growth of hepatocellular carcinoma cells by reducing CCNB1 expression

BACKGROUNDHepatocellular carcinoma (HCC) is one of the most prevalent cancers in human populations worldwide. Huanglian decoction is one of the most important Chinese medicine formulas, with the potential to treat cancer.AIMTo investigate the role and mechanism of Huanglian decoction on HCC cells.METHODSTo identify differentially expressed genes (DEGs), we downloaded gene expression profile data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma and Gene Expression Omnibus ({"type":"entrez-geo","attrs":{"text":"GSE45436","term_id":"45436"}}GSE45436) databases. We obtained phytochemicals of the four herbs of Huanglian decoction from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. We also established a regulatory network of DEGs and drug target genes and subsequently analyzed key genes using bioinformatics approaches. Furthermore, we conducted in vitro experiments to explore the effect of Huanglian decoction and to verify the predictions. In particular, the CCNB1 gene was knocked down to verify the primary target of this decoction. Through the identification of the expression levels of key proteins, we determined the primary mechanism of Huanglian decoction in HCC.RESULTSBased on the results of the network pharmacological analysis, we revealed 5 bioactive compounds in Huanglian decoction that act on HCC. In addition, a protein-protein interaction network analysis of the target genes of these five compounds as well as expression and prognosis analyses were performed in tumors. CCNB1 was confirmed to be the primary gene that may be highly expressed in tumors and was significantly associated with a worse prognosis. We also noted that CCNB1 may serve as an independent prognostic indicator in HCC. Moreover, in vitro experiments demonstrated that Huanglian decoction significantly inhibited the growth, migration, and invasiveness of HCC cells and induced cell apoptosis and G2/M phase arrest. Further analysis showed that the decoction may inhibit the growth of HCC cells by downregulating the CCNB1 expression level. After Huanglian decoction treatment, the expression levels of Bax, caspase 3, caspase 9, p21 and p53 in HCC cells were increased, while the expression of CDK1 and CCNB1 was significantly decreased. The p53 signaling pathway was also found to play an important role in this process.CONCLUSIONHuanglian decoction has a significant inhibitory effect on HCC cells. CCNB1 is a potential therapeutic target in HCC. Further analysis showed that Huanglian decoction can inhibit HCC cell growth by downregulating the expression of CCNB1 to activate the p53 signaling pathway.     

CircRNA expression profile and potential role of hsa_circ_0040039 in intervertebral disc degeneration

Purpose:Circular RNAs (circRNAs) play an critical role in the pathological processes associated with IDD. However, the potential roles of circRNAs in IDD remain largely unclear. Here, we identify the circRNAs expression profiles and elucidate the potential role of candidate circRNAs in the pathogenesis of intervertebral disc degeneration (IDD) through microarray data and bioinformatics analyses.Methods:We obtained the datasets of microarrays ({"type":"entrez-geo","attrs":{"text":"GSE67566","term_id":"67566"}}GSE67566 and {"type":"entrez-geo","attrs":{"text":"GSE116726","term_id":"116726"}}GSE116726) from the Gene Expression Omnibus database. The differentially expressed circRNAs and miRNAs were identified using the Limma R package. The target miRNAs and target genes of the candidate circRNAs were predicted using an online tool. Functional enrichment analyses of the target genes were performed using the clusterProfiler R package. A protein-protein interaction (PPI) network was constructed using STRING.Results:A total of 104 differentially expressed circRNAs were identified between the IDD and the control groups, including 41 upregulated circRNAs and 63 downregulated circRNAs (cutoff criteria (|log₂ fold change| > 2, P < .05)). Hsa_circ_0040039, which was the most upregulated circRNA (log₂ fold change = 2.95), was selected for further analysis. The regulatory circRNA-miRNA-mRNA network comprised hsa_circ_0040039, 2 target miRNAs (hsa-miR-424-5p and hsa-miR-15b-5p), and 77 target genes. Functional enrichment analysis showed that the 77 promising target genes are mainly enriched in the ubiquitin proteasome system and Wnt signaling pathway. Further, the PPI network showed that the top 3 hub genes are BRTC, SIAH1, and UBE2V1.Conclusions:A total of 104 differentially expressed circRNAs were identified between the IDD and control groups. Hsa_circ_0040039 may serve as a sponge of hsa-miR-424-5p and hsa-miR-15b-5p, to regulate the expression of downstream genes (such as BRTC, SIAH1, and UBE2V1); thus, it may be involved in IDD-associated pathological processes via the Wnt/β-catenin signaling pathway. Further studies are required to confirm the potential roles of hsa_circ_0040039 in IDD.     

Machine learning-based screening of the diagnostic genes and their relationship with immune-cell infiltration in patients with lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the most common type of lung cancer, and has a dismal mortality rate of 80%, mainly due to diagnosis at an advanced stage. Biomarkers with high specificity and sensitivity for the early diagnosis of LUAD are sparse. This study aimed to identify markers for the early diagnosis of LUAD.MethodsThe {"type":"entrez-geo","attrs":{"text":"GSE32863","term_id":"32863"}}GSE32863 and {"type":"entrez-geo","attrs":{"text":"GSE75037","term_id":"75037"}}GSE75037 data sets were standardized and merged to screen for differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. The intersected DEGs from the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) regression analyses were considered the hub genes. Then the diagnostic ability and expression of hub genes was tested in {"type":"entrez-geo","attrs":{"text":"GSE63459","term_id":"63459"}}GSE63459 data set, Finally, CIBERSORT was used to analyze the correlation between the immune-infiltrating cells and hub genes.ResultsThe following 7 DEGs were intersected by the LASSO and SVM regression analyses: Locus 401286 (LOC401286), flavin-containing monooxygenase 2 (FMO2), XLKD1, Ras homolog family member J (RHOJ), scavenger receptor Class A member 5 (SCARA5), heat shock protein beta-2 (HSPB2), and serine incorporator 2 (SERINC2). The area under the receiver operating characteristic curve (AUC) of LOC401286, FMO2, XLKD1, RHOJ, SCARA5, HSPB2, and SERINC2 was 0.99, 1.00, 0.99, 1.00, 0.99, 0.99, and 0.98, respectively in the training groups. The AUC of LOC401286, FMO2, XLKD1, RHOJ, SCARA5, HSPB2, and SERINC2 was 0.97, 0.96, 0.94, 0.88, 0.85, 0.94 and 0.89, respectively in the validation group. The immune-cell infiltrations of naive B cells, memory B cells, plasma cells, naive cluster of differentiation (CD) 4 T cells, T follicular helper cells, regulatory T cells, gamma delta T cells, monocytes, M0 macrophages, M1 macrophages, resting mast cells, activated mast cells, and neutrophils were different between the normal and tumor tissues. Notably, these immune cells were correlated with the above-mentioned 7 diagnostic genes.ConclusionsWe identified 7 DEGs in LUAD tissue that can be considered diagnostic genes based on 2 machine-learning regression methods, which could be very helpful for the early diagnosis of LUAD in clinical practice.     

Development of preoperative prognostic models including radiological features for survival of singular nodular HCC patients

BackgroundEarly singular nodular hepatocellular carcinoma (HCC) is an ideal surgical indication in clinical practice. However, almost half of the patients have tumor recurrence, and there is no reliable prognostic prediction tool. Besides, it is unclear whether preoperative neoadjuvant therapy is necessary for patients with early singular nodular HCC and which patient needs it. It is critical to identify the patients with high risk of recurrence and to treat these patients preoperatively with neoadjuvant therapy and thus, to improve the outcomes of these patients. The present study aimed to develop two prognostic models to preoperatively predict the recurrence-free survival (RFS) and overall survival (OS) in patients with singular nodular HCC by integrating the clinical data and radiological features.MethodsWe retrospective recruited 211 patients with singular nodular HCC from December 2009 to January 2019 at Eastern Hepatobiliary Surgery Hospital (EHBH). They all met the surgical indications and underwent radical resection. We randomly divided the patients into the training cohort (n =132) and the validation cohort (n = 79). We established and validated multivariate Cox proportional hazard models by the preoperative clinicopathologic factors and radiological features for association with RFS and OS. By analyzing the receiver operating characteristic (ROC) curve, the discrimination accuracy of the models was compared with that of the traditional predictive models.ResultsOur RFS model was based on HBV-DNA score, cirrhosis, tumor diameter and tumor capsule in imaging. RFS nomogram had fine calibration and discrimination capabilities, with a C-index of 0.74 (95% CI: 0.68-0.80). The OS nomogram, based on cirrhosis, tumor diameter and tumor capsule in imaging, had fine calibration and discrimination capabilities, with a C-index of 0.81 (95% CI: 0.74-0.87). The area under the receiver operating characteristic curve (AUC) of our model was larger than that of traditional liver cancer staging system, Korea model and Nomograms in Hepatectomy Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma, indicating better discrimination capability. According to the models, we fitted the linear prediction equations. These results were validated in the validation cohort.ConclusionsCompared with previous radiography model, the new-developed predictive model was concise and applicable to predict the postoperative survival of patients with singular nodular HCC. Our models may preoperatively identify patients with high risk of recurrence. These patients may benefit from neoadjuvant therapy which may improve the patients’ outcomes.     

New prognostic model for patients with advanced gastric cancer: Fluoropyrimidine/platinum doublet for first-line chemotherapy

BACKGROUNDNew prognostic factors have been reported in patients with metastatic or recurrent gastric cancer (MRGC), necessitating modifications to the previous prognostic model.AIMTo develop a new model, MRGC patients who received fluoropyrimidines/ platinum doublet chemotherapy between 2008 and 2015 were analyzed.METHODSA total of 1883 patients was divided into a training set (n = 937) and an independent validation set (n = 946).RESULTSMultivariate analysis showed that the following six factors were associated with poor overall survival (OS) in the training set: Eastern Cooperative Oncology Group performance score ≥ 2 and bone metastasis (2 points each), peritoneal metastasis, high alkaline phosphatase level, low albumin level, and high neutrophil-lymphocyte ratio (1 point each). A prognostic model was developed by stratifying patients into good (0-1 point), moderate (2-3 points), and poor (≥ 4 points) risk groups. In the validation set, the median OS of the three risk groups was 15.8, 10.1, and 5.7 mo, respectively, and those differences were significant (P < 0.001).CONCLUSIONWe identified six factors readily measured in clinical practice that are predictive of poor prognosis in patients with MRGC. The new model is simpler than the old and more easily predicts OS.     

A novel seven-gene risk profile in BALF to identify high-risk patients with idiopathic pulmonary fibrosis

BackgroundIdiopathic pulmonary fibrosis (IPF) is a fatal heterogeneous disease with a varied clinical course that is difficult to predict. Accurate predictive models are urgently needed to identify individuals with poor survival for the optimal timing of referral for transplantation and provide some clues for mechanistic research on disease progression.MethodsWe obtained the gene expression profiles of bronchoalveolar lavage fluid (BALF) from the Gene Expression Omnibus. Individuals from the {"type":"entrez-geo","attrs":{"text":"GPL14550","term_id":"14550"}}GPL14550 platform were assigned to the derivation cohort (n=112) and individuals from the {"type":"entrez-geo","attrs":{"text":"GPL17077","term_id":"17077"}}GPL17077 platform to the validation cohort (n=64). Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were applied to select candidate genes for overall survival. A nomogram model was constructed based on Cox hazard regression analysis. The model was assessed by C-statistic, calibration curve, and decision curve analysis (DCA) and was externally validated.ResultsA nomogram model comprising seven genes was constructed. Excellent discrimination and calibration were observed in the derivation (C-index 0.815) and validation (C-index 0.812) cohorts. The AUCs for predicting 1-, 2- and 3-year survival were 0.857, 0.918, 0.930 in the derivation cohort and 0.850, 0.880, 0.925 in the validation cohort, respectively. DCA confirmed the clinical applicability of the model. A risk score based on the model was an independent prognostic predictor and could divide patients into high- and low-risk groups. The Kaplan-Meier analysis displayed that high-risk patients exhibited significantly poorer survival compared with low-risk patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk patients were primarily enriched in inflammatory hallmarks, and single sample GSEA (ssGSEA) indicated that the high-risk group is closely correlated with the immune process. These lead to increased insight into mechanisms associated with IPF progression that inflammation mediated by immune response might be involved in the disease progression.ConclusionsThe novel BALF seven-gene model performed well in risk stratification and individualized survival prediction for patients with IPF, facilitating personalized management of IPF patients. It deepened the understanding of the role of inflammation in IPF progression, which needs to be further studied.     

Identification of potential core genes in esophageal carcinoma using bioinformatics analysis

Background:Esophageal squamous cell carcinoma (ESCC) is a common human malignancy worldwide. The tumorigenesis mechanism in ESCC is unclear.Materials and methods:To explore potential therapeutic targets for ESCC, we analyzed 3 microarray datasets ({"type":"entrez-geo","attrs":{"text":"GSE20347","term_id":"20347"}}GSE20347, {"type":"entrez-geo","attrs":{"text":"GSE38129","term_id":"38129"}}GSE38129, and {"type":"entrez-geo","attrs":{"text":"GSE67269","term_id":"67269"}}GSE67269) derived from the gene expression omnibus (GEO) database. Then, the GEO2R tool was used to screen out differently expressed genes (DEGs) between ESCC and normal tissue. Gene ontology function and kyoto encyclopedia of genes and genomes pathway enrichment analysis were performed using the database for annotation, visualization and integrated discovery to identify the pathways and functional annotation of DEGs. Protein–protein interaction of these DEGs was analyzed based on the search tool for the retrieval of interacting genes database and visualized by Cytoscape software. In addition, we used encyclopedia of RNA interactomes (ENCORI), gene expression profiling interactive analysis (GEPIA), and the human protein atlas to confirm the expression of hub genes in ESCC. Finally, GEPIA was used to evaluate the prognostic value of hub genes expression in ESCC patients and we estimated the associations between hub genes expression and immune cell populations (B Cell, CD8⁺ T Cell, CD4⁺ T Cell, Macrophage, Neutrophil, and Dendritic Cell) in esophageal carcinoma (ESCA) using tumor immune estimation resource (TIMER).Results:In this study, 707 DEGs (including 385 upregulated genes and 322 downregulated genes) and 6 hub genes (cyclin B1 [CCNB1], cyclin dependent kinase 1 [CDK1], aurora kinase A [AURKA], ubiquitin conjugating enzyme E2C [UBE2C], cyclin A2 [CCNA2], and cell division cycle 20 [CDC20]) were identified. All of the 6 hub genes were highly expressed in ESCC tissues. Among of them, only CCNB1 and CDC20 were associated with stage of ESCC and all of them were not associated with survival time of patients.Conclusion:DEGs and hub genes were confirmed in our study, providing a thorough, scientific and comprehensive research goals for the pathogenesis of ESCC.     

Identification of G6PC as a potential prognostic biomarker in hepatocellular carcinoma based on bioinformatics analysis

Hepatocellular carcinoma (HCC) has high mortality and incidence rates around the world with limited therapeutic options. There is an urgent need for identification of novel therapeutic targets and biomarkers for early diagnosis and predicting patient survival with HCC.Several studies ({"type":"entrez-geo","attrs":{"text":"GSE102083","term_id":"102083"}}GSE102083, {"type":"entrez-geo","attrs":{"text":"GSE29722","term_id":"29722"}}GSE29722, {"type":"entrez-geo","attrs":{"text":"GSE101685","term_id":"101685"}}GSE101685, and {"type":"entrez-geo","attrs":{"text":"GSE112790","term_id":"112790"}}GSE112790) from the GEO database in HCC were screened and analyzed by GEO2R, gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted with the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction network was plotted and the module analysis was performed using Search Tool for the Retrieval of Inter-acting Genes/Proteins database and Cytoscape. The expression and survival of key genes were identified using UALCAN, Kaplan–Meier Plotter and ONCOMINE online databases, and the immune infiltration level of key genes was analyzed via the Tumor Immune Estimation Resource (TIMER) database.Through database analysis, eight key genes were finally screened out, and the expressions of cyclin-dependent kinase regulatory subunit 2 and glucose-6-phosphatase catalytic (G6PC), which were closely related to the survival of HCC patients, was detected by using UALCAN. Further analysis on the differential expression of G6PC in multiple cancerous tumors and normal tissues revealed low expression in many solid tumors by Oncomine and TIMER. In addition, Kaplan–Meier plotter and UALCAN database analysis to access diseases prognosis suggested that low expression of G6PC was significantly associated with poor overall survival in HCC patients. Finally, TIMER database analysis showed a significant negative correlation between G6PC and infiltration levels of six kinds of immune cells. The somatic copy number alterations of G6PC were associated with B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, dentritic cells and neutrophils.These bioinformatic data identified G6PC as a potential key gene in the diagnosis and prognosis of HCC.     

Tumor size as a predictor for prognosis of patients with surgical IIIA-N2 non-small cell lung cancer after surgery

BackgroundThe 8^th edition of the American Joint Committee on Cancer staging system for lung cancer made major revisions to T staging, especially the size division of stage II/III patients. However, the value of tumor size in the postoperative prognosis of IIIA–N2 non-small cell lung cancer (NSCLC) is seldom mentioned, and survival data of such patients should be re-evaluated according to the 8^th edition staging system.MethodsPatients with IIIA-N2 NSCLC after surgery were identified in the Surveillance, Epidemiology, and End Results database (n=4,128). All patients were stratified according to tumor size, 5-year overall survival (OS) was then compared. Cox regression analysis was used to determine the value of size to discriminate patients with prognostic differences and establish a predictive nomogram system. Patients with IIIA-N2 NSCLC from our own institute (n=583) were used to validate the results.ResultsThe prognosis of patients with tumor sizes of 0–2, 2–4 and 4–5 cm differed greatly from each other in the training cohort, with 5-year OS rates of 53.7%, 43.9% and 36.9% respectively (P<0.001), in the validation cohort, the rates were 54.1%, 38.4% and 33.8% respectively. Tumor size >2 cm was considered an independent risk factor compared to the ≤2 cm group in the Cox regression analysis: 2–4 cm (HR =1.25, 1.12–1.39; P<0.001), 4–5 cm (HR =1.51, 1.32–1.39; P<0.001), the validation cohort showed the same trend. The concordance index of the training set was 0.634 (0.622–0.646), while that of the validation set was 0.716 (0.686–0.746). The calibration plot showed optimal consistency between the nomogram predicted survival and observed survival.ConclusionsTumors with different sizes showed significant postoperative survival differences among patients with IIIA-N2 NSCLC. Tumor size should be considered when making surgery decisions in such patients, with tumor size ≤2 cm showing considerably better prognosis.