Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

Function of NEK2 in clear cell renal cell carcinoma and its effect on the tumor microenvironment

BACKGROUND

Previous studies have revealed the critical functions of NEK2 in controlling the cell cycle which is linked to poor prognosis in multiple tumor types, but less research has been devoted to clear cell renal cell carcinoma (ccRCC).

METHODS

We downloaded clinical data from the gene expression omnibus (GEO) and TCGA databases together with transcriptional and mutational datasets. Strongly coexpressed genes with NEK2 were extracted from TCGA-KIRC cohort, and were submitted to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional analyses. According to NEK2 levels, the survival status, mutational characteristics, response to immunotherapy and sensitivity to drugs of the patients were studied. The potential correlations between NEK2 levels and immune cell state as well as immune cell infiltration were examined using the GEPIA, TIMER and TISIDB databases. Double immunofluorescence (IF) was performed to identify the NEK2 overexpression and relationship with CD8 in ccRCC.

RESULTS

The NEK2 gene was overexpressed and would enhance the nuclear division and cell cycle activities in ccRCC. ccRCC patients with high NEK2 expression had worse clinical outcomes, higher mutation burden and better therapeutic response. Moreover, NEK2 gene overexpression was positively related to various immune cell marker sets, which was also proved by validation cohort, and more infiltration of various immune cells.

CONCLUSION

ccRCC patients with NEK2 high expression have a poorer prognosis than those with NEK2 low expression, resulting from its function of promoting proliferation, accompanied by increased infiltration of CD8 + T cells and Tregs and T-cell exhaustion and will respond better to proper treatments.

TuBG1 promotes hepatocellular carcinoma via ATR/P53-apoptosis and cycling pathways

BACKGROUND

As reported, γ-tubulin (TuBG1) is related to the occurrence and development of various types of malignant tumors. However, its role in hepatocellular cancer (HCC) is not clear. The present study was to investigate the relationship between TuBG1 and clinical parameters and survival in HCC patients.

METHODS

The correlation between TuBG1 and clinical parameters and survival in HCC patients was explored by bioinformatics analysis. Immunohistochemistry was used for the verification. The molecular function of TuBG1 was measured using colony formation, scratch assay, trans-well assay and flow cytometry. Gene set enrichment analysis (GSEA) was used to pick up the enriched pathways, followed by investigating the target pathways using Western blotting. The tumor-immune system interactions and drug bank database (TISIDB) was used to evaluate TuBG1 and immunity. Based on the TuBG1-related immune genes, a prognostic model was constructed and was further validated internally and externally.

RESULTS

The bioinformatic analysis found high expressed TuBG1 in HCC tissue, which was confirmed using immunohistochemistry and Western blotting. After silencing the TuBG1 in HCC cell lines, more G1 arrested cells were found, cell proliferation and invasion were inhibited, and apoptosis was promoted. Furthermore, the silence of TuBG1 increased the expressions of Ataxia-Telangiectasia and Rad-3 (ATR), phospho-P38 mitogen-activated protein kinase (P-P38MAPK), phospho-P53 (P-P53), B-cell lymphoma-2 associated X protein (Bax), cleaved caspase 3 and P21; decreased the expressions of B-cell lymphoma-2 (Bcl-2), cyclin D1, cyclin E2, cyclin-dependent kinase 2 (CDK2) and CDK4. The correlation analysis of immunohistochemistry and clinical parameters and survival data revealed that TuBG1 was negatively correlated with the overall survival. The constructed immune prognosis model could effectively evaluate the prognosis.

CONCLUSIONS

The increased expression of TuBG1 in HCC is associated with poor prognosis, which might be involved in the occurrence and development of HCC.

The role of Cyclin Dependent Kinase Inhibitor 3 (CDKN3) in promoting human tumors: Literature review and pan-cancer analysis

Background

Although many experiments and clinical studies have proved the link between the expression of CDKN3 and human tumors, we have not been able to identify any bioinformatics study in which the extensive tumor-promoting effect of CDKN3 was systematically analyzed.

Objective

Explore the extensive tumor-promoting effects of CDKN3 and review the research progress of CDKN3 in cancer.

Methods

We systematically reviewed the literature on CDKN3 and tumors. We explored the potential tumor-promoting effects of CDKN3 on different tumors in the TCGA database and the GTEx database using multiple platforms and websites. We studied the expression level of CDKN3, survival, prognosis, diagnosis, genetic variation, immune infiltration, and enrichment analysis using databases such as TIMER 2.0, GEPIA2, cBioPortal, and STRING.

Results

We found that CDKN3 is highly expressed in most tumors. The expression of CDKN3 is closely related to the prognosis of some tumors. And CDKN3 may have diagnostic value. The conclusion of our literature review is roughly the same, but there are differences, which are worthy of further study. Moreover, CDKN3 may be related to immune cell infiltration in tumor tissues. The genetic alteration of LUAD, STAD, SARC, PCPG, and ESCA with "Amplification" as the main type. In addition, through enrichment analysis, we found that CDKN3 affects tumors mainly through the control of the cell cycle and mitosis.

Conclusion

CDKN3 is highly expressed in most tumor tissues and has a statistical correlation with survival prognosis. It has extensive tumor-promoting effects that may be related to mechanisms such as immune infiltration.

MiR-96-5p Suppresses Progression of Arsenite-Induced Human Keratinocyte Proliferation and Malignant Transformation by Targeting Denticleless E3 Ubiquitin Protein Ligase Homolog

Long-term exposure to arsenic has been linked to a variety of cancers, among which skin cancer is the most prevalent form. However, the mechanism underlying arsenic carcinogenesis is unclear, and there is still limited information on the role of miRNAs in arsenic-induced skin cancer. This study aims to explore the role of miR-96-5p in the arsenite-induced proliferation and malignant transformation of human HaCaT keratinocytes. The GEO database (accession numbers GSE97303, GSE97305, and GSE97306) was used to extract mRNA and miRNA expression profiles of HaCaT cells treated with or without 0.1 μmol/L sodium arsenite for 3 and 7 weeks. In this paper, according to the CCK8 assay result, HaCaT cells exposed to 0.1 μmol/L sodium arsenite for 48 h were finalized. CCK8, MTT, EdU incorporation, and colony formation assays were used to determine the viability and proliferation of HaCaT cells and transformed HaCaT (T-HaCaT) cells. The subcellular localization and relative expression levels of DTL, as well as miR-96-5p in HaCaT cells induced by arsenite, were determined via immunofluorescence, RT-qPCR, and Western blot. Dual-luciferase reporter assay was performed to identify miR-96-5p bound directly to DTL. Transfection of miR-96-5p mimics or DTL siRNA was conducted to verify the arsenite-induced viability of HaCaT cells and T-HaCaT cells. T-HaCaT cells and nude mice were used to construct arsenite-induced malignant transformation and an in vivo xenograft model to demonstrate the over-expressed effect of miR-96-5p. The results showed that DTL was the target gene of miR-96-5p. Meanwhile, we also found that 0.1 μmol/L sodium arsenite upregulated DTL by decreasing the miR-96-5p level, leading to the proliferation and malignant transformation of HaCaT cells. MiR-96-5p agomir treatment slowed the growth of transplanted HaCaT cells transformed by arsenite in a manner associated with DTL downregulation in the nude mice xenograft model. Taken together, we confirmed that miR-96-5p, as a potent regulator of DTL, suppressed arsenite-induced HaCaT cell proliferation and malignant transformation, which might provide a novel therapeutic target for the treatment of arsenic-induced skin cancer.

Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma

Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities.

Artificial intelligence-based prediction of molecular and genetic markers for hepatitis C-related hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the main cause of mortality from cancer globally. This paper intends to classify public gene expression data of patients with Hepatitis C virus-related HCC (HCV+HCC) and chronic HCV without HCC (HCV alone) through the XGboost approach and to identify key genes that may be responsible for HCC.

Methods

The current research is a retrospective case-control study. Public data from 17 patients with HCV+HCC and 35 patients with HCV-alone samples were used in this study. An XGboost model was established for the classification by 10-fold cross-validation. Accuracy (AC), balanced accuracy (BAC), sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were utilized for performance assessment.

Results

AC, BAC, sensitivity, specificity, positive predictive value, negative predictive value, and F1 scores from the XGboost model were 98.1, 97.1, 100, 94.1, 97.2, 100, and 98.6%, respectively. According to the variable importance values from the XGboost, the HAO2, TOMM20, GPC3, and PSMB4 genes can be considered potential biomarkers for HCV-related HCC.

Conclusion

A machine learning-based prediction method discovered genes that potentially serve as biomarkers for HCV-related HCC. After clinical confirmation of the acquired genes in the following medical study, their therapeutic use can be established. Additionally, more detailed clinical works are needed to substantiate the significant conclusions in the current study.

UBE2C expression is elevated in hepatoblastoma and correlates with inferior patient survival

Hepatoblastoma (HB) is the most common malignant liver tumor among children. To gain insight into the pathobiology of HB, we performed RNA sequence analysis on 5 patient-derived xenograft lines (HB-243, HB-279, HB-282, HB-284, HB-295) and 1 immortalized cell line (HUH6). Using cultured hepatocytes as a control, we found 2,868 genes that were differentially expressed in all of the HB lines on mRNA level. The most upregulated genes were ODAM, TRIM71, and IGDCC3, and the most downregulated were SAA1, SAA2, and NNMT. Protein-protein interaction analysis identified ubiquitination as a key pathway dysregulated in HB. UBE2C, encoding an E2 ubiquitin ligase often overexpressed in cancer cells, was markedly upregulated in 5 of the 6 HB cell lines. Validation studies confirmed UBE2C immunostaining in 20 of 25 HB tumor specimens versus 1 of 6 normal liver samples. The silencing of UBE2C in two HB cell models resulted in decreased cell viability. RNA sequencing analysis showed alterations in cell cycle regulation after UBE2C knockdown. UBE2C expression in HB correlated with inferior patient survival. We conclude that UBE2C may hold prognostic utility in HB and that the ubiquitin pathway is a potential therapeutic target in this tumor.

GID2 Interacts With CDKN3 and Regulates Pancreatic Cancer Growth and Apoptosis

Dysregulation of deubiquitinase or ubiquitinase-mediated protein expression contributes to various diseases, including cancer. In the present study, we identified GID2, a subunit of the glucose-induced degradation-deficient (GID) complex that functions as an E3 ubiquitin ligase, as a potential key candidate gene in pancreatic cancer (PC) progression. The functional role and potential mechanism of GID2 in PC progression were investigated. Integrated bioinformatics analysis was performed to identify differentially expressed genes in PC based on the Gene Expression Profiling Interactive Analysis data sets. We found that GID2 was upregulated in PC tissues and that a high level of GID2 expression in clinical PC samples was positively associated with tumor stage and poor survival. Functional assays elucidated that GID2 expression promoted cell growth in vitro and accelerated tumor growth in vivo. GID2 knockdown effectively attenuated the malignant behaviors of PC cells and tumor formation. Furthermore, the protein network that interacted with the GID2 protein was constructed based on the GeneMANIA website. Cyclin-dependent kinase inhibitor 3 (CDKN3), a cell cycle regulator, was identified as a potential target of the GID2 protein. We revealed that GID2 positively regulated CDKN3 expression and inhibited CDKN3 ubiquitination. Furthermore, CDKN3 downregulation reversed the promoting effects of GID2 on PC progression. Therefore, the present study demonstrated that GID2 might regulate PC progression by maintaining the stability of the CDKN3 protein. These findings highlight the potential roles of the GID2/CDKN3 axis as a potential therapeutic target in PC.

Hsa-circ-0006091 modulates the proliferation of hepatocellular carcinoma via the miR-622/CCNB1 axis

Background/aim

Hepatocellular carcinoma (HCC) is a common type of cancer. We hypothesize that circular RNA-0006091 (circ-0006091) affects the progression of HCC. The study aims to investigate the effect of circ-0006091 in HCC cells.

Materials and methods

The levels of circ-0006091, microRNA-622 (miR-622), and cyclin B1 (CCNB1) were assayed using qRT-PCR and western blotting. The metastasis of the HCC cells was measured with wound healing and transwell assays. The protein expression levels of MMP-2 and MMP-9 were assayed with western blotting. Dual-luciferase reporter and RNA-pulldown assays were used to determine the link between miR-622 and circ-0006091 or CCNB1. Mice-based tests were used to determine the effect of circ-0006091 on the proliferation of HCC cells.

Results

The levels of circ-0006091 and CCNB1 were increased in the HCC cells, but miR-622 was down-regulated. Deficiency of circ-0006091 reduced the metastasis of the HCC cells, and silencing of circ-0006091 decreased the activities of MMP-2 and MMP-9 in the same cells. Circ-0006091 modulated the CCNB1 level in the HCC cells via miR-622. Silencing of circ-0006091 suppressed the proliferation of the HCC cells in vivo.

Conclusion

Circ-0006091 regulated HCC cell metastasis via the miR-622/CCNB1 axis, a possible therapeutic target in managing HCC.

Non-thermal plasma-treated melatonin inhibits the biological activity of HCC cells by increasing intracellular ROS levels and reducing RRM2 expression

Non-thermal plasma (NTP) is thought to have a cytotoxic effect on tumor cells. Although its application in cancer therapy has shown considerable promise, the current understanding of its mechanism of action and cellular responses remains incomplete. Furthermore, the use of melatonin (MEL) as an adjuvant anticancer drug remains unexplored. In this study, we found that NTP assists MEL in promoting apoptosis, delaying cell cycle progression, and inhibiting cell invasion and migration in hepatocellular carcinoma (HCC) cells. This mechanism may be associated with the regulation of intracellular reactive oxygen species levels and ribonucleotide reductase regulatory subunit M2 expression. Our findings confirm the pharmacological role of MEL and the adjuvant value of NTP, emphasizing their potential in combination therapy for HCC. Our study may have important implications for the development of new approaches for HCC treatment.

Characterization of molecular subtypes based on chromatin regulators and identification of the role of NPAS2 in lung adenocarcinoma

BACKGROUND

Chromatin regulators (CRs) are critical epigenetic modifiers and have been reported to play critical roles during the progression of various tumors, but their role in lung adenocarcinoma (LUAD) has not been comprehensively studied.

METHODS

Differential expression and univariate Cox regression analyses were conducted to identify the prognostic CRs. Consensus clustering was applied to classify the subtypes of LUAD based on prognostic CRs. LASSO-multivariate Cox regression method was used for construction of a prognostic signature and development of chromatin regulator-related gene index (CRGI). The capacity of CRGI to distinguish survival was evaluated via Kaplan-Meier method in multiple datasets. Relationship between CRGI and tumor microenvironment (TME) was evaluated. Additionally, clinical variables and CRGI were incorporated to create a nomogram. The role of the prognostic gene NPAS2 in LUAD was elucidated via clinical samples validation and a series of in vitro and in vivo experiments.

RESULTS

Two subtypes of LUAD were classified based on 46 prognostic CRs via consensus clustering which had significantly different survival and TME. A prognostic signature consisting of six CRs (MOCS, PBK, CBX3, A1CF, NPAS2, and CTCFL) was developed and proved to be an effective survival predictor in multiple independent datasets. The prognostic signature was also demonstrated to be an indicator of TME and sensitivity to immunotherapy and chemotherapy. The nomogram was suggested to be a simple tool that can predict survival accurately. Clinical samples show that NPAS2 is highly expressed in LUAD tissues, and in vitro and in vivo experiments demonstrated that inhibition of NPAS2 impeded malignant progression of LUAD cells.

CONCLUSIONS

Our study comprehensively unveiled the functions of CRs in LUAD, developed a classifier to predict survival and response to treatments, and suggested that NPAS2 promoted LUAD progression for the first time.

Low expression of SLC34A1 is associated with poor prognosis in clear cell renal cell carcinoma

OBJECTIVE

Clear cell renal cell carcinoma (ccRCC) is a malignant renal tumor that is highly prone to metastasis and recurrence. The exact pathogenesis of this cancer is still not well understood. This study aimed to identify novel hub genes in renal clear cell carcinoma and determine their diagnostic and prognostic value.

METHODS

Intersection genes were obtained from multiple databases, and protein-protein interaction analysis and functional enrichment analysis were performed to identify key pathways related to the intersection genes. Hub genes were identified using the cytoHubba plugin in Cytoscape. GEPIA and UALCAN were utilized to observe differences in mRNA and protein expression of hub genes between KIRC and adjacent normal tissues. The Wilcoxon rank sum test was used to analyze hub gene levels between paired KIRC and matched non-cancer samples. IHC results were obtained from the HPA online database, and according to the median gene expression level, they were divided into a high-expression group and a low-expression group. The correlation of these groups with the prognosis of KIRC patients was analyzed. Logistic regression and the Wilcoxon rank sum test were used to test the relationship between SLC34A1 level and clinicopathological features. The diagnostic value of SLC34A1 was evaluated by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Cox regression analysis was used to analyze the relationship between clinicopathological features, SLC34A1 expression, and KIRC survival rate. LinkedOmics was used to obtain the genes most related to SLC34A1 and their functional enrichment. Genetic mutations and methylation levels of SLC34A1 in KIRC were obtained from the cBioPortal website and the MethSurv website, respectively.

RESULTS

Fifty-eight ccRCC differential genes were identified from six datasets, and they were mainly enriched in 10 functional items and 4 pathways. A total of 5 hub genes were identified. According to the GEPIA database analysis, low expression of SLC34A1, CASR, and ALDOB in tumors led to poor prognosis. Low expression of SLC34A1 mRNA was found to be related to clinicopathological features of patients. SLC34A1 expression in normal tissues could accurately identify tumors (AUC 0.776). SLC34A1 was also found to be an independent predictor of ccRCC in univariate and multivariate Cox analyses. The mutation rate of the SLC34A1 gene was 13%. Eight of the 10 DNA methylated CpG sites were associated with the prognosis of ccRCC. SLC34A1 expression in ccRCC was positively correlated with B cells, eosinophils, neutrophils, T cells, TFH, and Th17 cells, and negatively correlated with Tem, Tgd, and Th2 cells.

CONCLUSION

The expression level of SLC34A1 in KIRC samples was found to be decreased, which predicted a decreased survival rate of KIRC. SLC34A1 may serve as a molecular prognostic marker and therapeutic target for KIRC patients.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

A Network Pharmacology Method Combined with Molecular Docking Verification to Explore the Therapeutic Mechanisms Underlying Simiao Pill Herbal Medicine against Hyperuricemia

Objective

Hyperuricemia (HUA) is a common metabolic disease caused by disordered purine metabolism. We aim to reveal the mechanisms underlying the anti-HUA function of Simiao pill and provide therapeutic targets.

Methods

Simiao pill-related targets were obtained using Herbal Ingredients' Targets (HIT), Traditional Chinese Medicine Systems Pharmacology (TCMSP), and Traditional Chinese Medicine Integrated Database (TCMID). HUA-associated targets were retrieved from GeneCards, DisGeNET, and Therapeutic Targets Database (TTD). Protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, ggraph and igraph R packages. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using ClusterProfiler. The top 10 core targets were identified through cytoHubba. Molecular docking was conducted using PyMOL and AutoDock high-performance liquid chromatograph (HPLC) analysis was performed to identify effective compounds of Simiao pill.

Results

Simiao pill-HUA target network contained 80 targets. The key targets were mainly involved in inflammatory responses. Insulin (INS), tumor necrosis factor (TNF), interleukin-6 (IL6), interleukin 1 beta (IL1B), vascular endothelial growth factor A (VEGFA), leptin (LEP), signal transducer and activator of transcription 3 (STAT3), C-C motif chemokine ligand 2 (CCL2), interleukin-10 (IL10), and toll like receptor 4 (TLR4) were the top 10 targets in the PPI network. GO analysis demonstrated the main implication of the targets in molecular responses, production, and metabolism. KEGG analysis revealed that Simiao pill might mitigate HUA through advanced glycation end-product- (AGE-) receptor for AGE- (RAGE-) and hypoxia-inducible factor-1- (HIF-1-) associated pathways. IL1B, IL6, IL10, TLR4, and TNF were finally determined as the promising targets of Simiao pill treating HUA. Through molecular docking and HPLC analysis, luteolin, quercetin, rutaecarpine, baicalin, and atractylenolide I were the main active compounds.

Conclusions

Simiao pill can mitigate HUA by restraining inflammation, mediating AGE-RAGE- and HIF-1-related pathways, and targeting IL1B, IL6, IL10, TLR4, and TNF.

A dataset resource for clinically associated phosphosites in hepatocellular carcinoma

Phosphorylation is one of the most common post-translational modifications (PTMs) and is closely related to protein activity and function, playing a critical role during cancer development. Quantitative phosphoproteomic strategies have been widely used to study the underlying mechanisms of cancer progression or drug resistance. In this report, we analyzed the association of phosphosite levels originated from our previously reported proteogenomic study in hepatocellular carcinoma (HCC) with clinical parameters, including prognosis, recurrence, and Tumor-Node-Metastasis (TNM) stages. By using both the log-rank test and univariate Cox proportional hazards regression analysis, we found that the abundance levels of 1712 phosphosites were associated with prognosis and those of 393 phosphosites associated with recurrence. Besides, 692 phosphosites had different abundance levels among TNM stages (I, II, III+IV) by Analysis of Variance (ANOVA) test. Gene ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using proteins with these statistically significant phosphosites. In conclusion, we provided a dataset resource for clinically associated phosphosites in HCC, which may be beneficial to liver cancer related basic research.

Clinical implication and immunological landscape analyses of ANLN in pan-cancer: A new target for cancer research

BACKGROUND

Anillin is a F-actin binding protein (ANLN) mainly involved in the process of cytokinesis and known to be dysregulated in diverse cancers. However, the role of ANLN in pan-cancer prognosis and tumor immunity remains unclear.

METHODS

Gene expression profiles of 31 solid tumors were downloaded from The Cancer Genome Atlas (TCGA) database. ANLN mRNA and protein expression were quantified using quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC). Protein expression of ANLN was further confirmed in Human Protein Atlas (HPA) database. Cox regression and Kaplan-Meier analysis were utilized to assess the prognostic value of ANLN in pan-cancer. The correlation between ANLN and different immune gene markers and infiltration cells was analyzed via ESTIMATE and CIBERSORT. A BLCA immunotherapy cohort: IMvigor (210) was used to confirm the role of ANLN in immune response.

RESULTS

ANLN upregulation was detected in 21 types of cancers and was associated with poor overall survival (OS), disease-free interval (DFI), and progression-free interval (PFI) in most cancers except in THYM (Thymoma). Additionally, correlation analysis revealed a significantly positive association between ANLN expression and tumor mutation burden (TMB), microsatellite instability (MSI), immune cells infiltration. and immune checkpoint genes in various cancers. The BLCA immunotherapy cohort confirmed that patients with higher ANLN level had better immune responses and longer OS.

CONCLUSION

ANLN may serve as a prognostic biomarker for pan-cancer. ANLN upregulation is associated with higher TMB, MSI, and immune cell infiltration in multiple types of tumors, shedding new light for cancer treatment.

Bioinformatics Analysis Identifies ASCL1 as the Key Transcription Factor in Hepatocellular Carcinoma Progression

Methods

Differentially transcription factors (DETFs) were identified from differentially expressed genes (DEGs) in GSE62232 and transcription factors. Then, they were analyzed by regulatory networks, prognostic risk model, and overall survival analyses to identify the key DETF. Combined with the regulatory networks and binding site analysis, the target mRNA of key DETF was determined, and its prognostic value in HCC was evaluated by survival, clinical characteristics analyses, and experiments. Finally, the expressions and functions of the key DETF on the DEmRNAs were investigated in HCC cells.

Results

Through multiple bioinformatics analyses, ASCL1 was identified as the key DETF, and SLC6A13 was predicted to be its target mRNA with the common binding site of CCAGCAACTGGCC, both downregulated in HCC. In survival analysis, high SLC6A13 was related to better HCC prognosis, and SLC6A13 was differentially expressed in HCC patients with clinical characteristics. Furthermore, cell experiments showed the mRNA expressions of ASCL1 and SLC6A13 were both reduced in HCC, and their overexpressions suppressed the growth, invasion, and migration of HCC cells. Besides, over-ASCL1 could upregulate SLC6A13 expression in HCC cells.

Conclusion

This study identifies two suppressor genes in HCC progression, ASCL1 and SLC6A13, and the key transcription factor ASCL1 suppresses HCC progression by targeting SLC6A13 mRNA. They are both potential treatment targets and prognostic biomarkers for HCC patients, which provides new clues for HCC research.

Construction and Analysis of Hepatocellular Carcinoma Prognostic Model Based on Random Forest

Methods

Transcriptome data and clinical data of HCC were downloaded from the TCGA database. Screen important genes based on the random forest method, combined with differential expression genes (DEGs) to screen out important DEGs. The Kaplan‒Meier curve was used to evaluate its prognostic significance. Cox regression analysis was used to construct a survival prognosis prediction model, and the ROC curve was used to verify it. Finally, the mechanism of action was explored through GO and KEGG pathway enrichment and GeneMANIA coexpression analyses.

Results

Seven important DEGs were identified, three were highly expressed and four were lowly expressed. Among them, GPRIN1, MYBL2, and GSTM5 were closely related to prognosis (P < 0.05). After the survival prognosis prediction model was established, the survival analysis showed that the survival time of the high-risk group was significantly shortened (P < 0.001), but the ROC analysis indicated that the model was not superior to staging. Twenty coexpressed genes were screened, and enrichment analysis indicated that glutathione metabolism was an important mechanism for these genes to regulate HCC progression.

Conclusion

This study revealed the important DEGs affecting HCC progression and provided references for clinical assessment of patient prognosis and exploration of HCC progression mechanisms through the construction of predictive models and gene enrichment analysis.

HMMR potential as a diagnostic and prognostic biomarker of cancer-speculation based on a pan-cancer analysis

Background

Although the status of universal upregulation for the Hyaluronan-Mediated Motility Receptor (HMMR) in pan-cancer is still unknown, HMMR is upregulated and associated with poor prognosis for some tumors.

Methods

Exploring HMMR expression in different tumor types using The Cancer Genome Atlas (TCGA) or other public databases for a pan-cancer analysis, exploring the relationship between HMMR and tumor prognosis, and exploring the role of HMMR in tumor immunity.

Results

No matter the pairing or unpairing of data, HMMR expression generally increased compared to corresponding normal tissue. Based on a CCLE study, our results indicated that HMMR is widely expressed in various tumor cells. For most tumor types, high HMMR expression was associated with reduced Overall Survival (OS), Return to Functional Status (RFS), and Platinum Free Interval (PFI). ROC curves indicated that HMMR displays high prediction potential for most tumor types. In pan-cancer, HMMR is correlated with some clinical staging, immune cells, and immune checkpoints for some tumors. The GO/KEGG enrichment analysis results for proteins most closely related to HMMR indicate that the most highly enriched pathways are all related to tumor development.

Conclusions

Our pan-cancer analysis of HMMR suggests that HMMR can be used as a potential diagnostic and prognostic indicator of pan-cancer and that HMMR may be involved in tumor development.

Combined Transcriptomic and Proteomic Analyses Reveal the Different Responses to UVA and UVB Radiation in Human Keratinocytes

Ultraviolet (UV) radiation from sunlight is a major risk factor for many cutaneous pathologies including skin aging and cancers. Despite decades of research, the different responses to UVA and UVB in human keratinocytes have not been systemically investigated. Here, we performed multi-omics to characterize the common and different changes in gene transcription and protein expression after exposure to UVB and UVA, respectively. Keratinocyte cells, treated with or without UV, were analyzed by TMT-labeled MS/MS spectra and RNA-sequencing. A common set of genes/proteins was found to be impacted by both UVA and UVB and the other differential genes/proteins showed wavelength specificity. The common set of genes/proteins were mainly involved in keratinization, lipid metabolic processes and stimulus response. The UVB specifically responsive genes/proteins were mainly related to RNA processing, gene silencing regulation and cytoskeleton organization. The UVA specifically responsive genes/proteins were mainly involved in vesicle-mediated transport and oxygen-containing compound response. Meanwhile, the hub differential genes/proteins in each set were identified by protein-protein interaction networks and cluster analysis. This work provided a global view of the similar and differential molecular mechanisms of UVB- and UVA-induced cell damage in keratinocytes, which would be beneficial for further studies in the prevention or treatment of UV-related pathologies.

Clinical value and potential mechanisms of BUB1B up-regulation in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) has insidious onset, late clinical diagnosis and high recurrence rate, which leads to poor quality of patient life. Therefore, it is necessary to further explore the pathogenesis and therapy targets of NPC. BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) was found to be up-regulated in a variety of cancers, but only two previous study showed that BUB1B was overexpressed in NPC and the sample size was small. The clinical role of BUB1B expression and its underlying mechanism in NPC require more in-depth research. Immunohistochemical samples and public RNA-seq data indicated that BUB1B protein and mRNA expression levels were up-regulated in NPC, and summary receiver operating characteristic curve indicated that BUB1B expression level had a strong ability to distinguish NPC tissues from non-NPC tissues. Gene ontology and Kyoto Encyclopedia of genes and genomes were performed and revealed that BUB1B and its related genes were mainly involved in cell cycle and DNA replication. Protein- Protein Interaction were built to interpret the BUB1B molecular mechanism. Histone deacetylase 2 (HDAC2) could be the upstream regulation factor of BUB1B, which was verified by Chromatin Immunoprecipitation Sequencing samples. In summary, BUB1B was highly expressed in NPC, and HDAC2 may affect cell cycle by regulating BUB1B to promote cancer progression.

A novel lnc-LAMC2-1:1 SNP promotes colon adenocarcinoma progression by targeting miR-216a-3p/HMGB3

Single nucleotide polymorphisms (SNPs) was associated with altering the secondary structure of long non-coding RNA (lncRNA). Increasing reports showed that lnc-LAMC2-1:1 SNP played an important role in cancer development and invasion. This study is to elucidate the molecular function of lnc-LAMC2-1:1 SNP rs2147578 promoting tumor progression in colon adenocarcinoma (COAD). In this study, we found that the lnc-LAMC2-1:1 SNP rs2147578 was upregulated in COAD cell lines. Furthermore, lnc-LAMC2-1:1 SNP rs2147578 promoted colon cancer migration, invasion, and proliferation. Interestingly, lnc-LAMC2-1:1 SNP rs2147578 positively regulated HMGB3 expression via miR-216a-3p in colon cancer cells. Functional enrichment analysis showed that targeting genes of miR-216a-3p were enriched in regulating the pluripotency of stem cells, MAPK signaling pathway, TNF signaling pathway, neurotrophin signaling pathway, relaxin signaling pathway, and FoxO signaling pathway. Tumor Immune Estimation Resource (TIMER) database revealed that there was a significantly positive correlation between HMGB3 expression and the infiltration of CD8⁺ T cells, B cells, neutrophils, macrophages, and CD4⁺ T cells. Finally, HMGB3 overexpression was validated in external data. In conclusions, lnc-LAMC2-1:1 SNP rs2147578 was involved in promoting COAD progression by targeting miR-216a-3p/HMGB3, and this study will provide a novel molecular target for COAD.

The Novel Action of miR-193b-3p/CDK1 Signaling in HCC Proliferation and Migration: A Study Based on Bioinformatic Analysis and Experimental Investigation

Hepatocellular carcinoma (HCC) is a common human malignancy with high mortality and dismal prognosis. A growing number of novel targets underlying HCC pathophysiology have been detected using microarray high throughput screening platforms. This study carried out bioinformatics analysis to explore underlying biomarkers in HCC and assessed the potential action of the miR-193b-3p/CDK1 signaling pathway in HCC progression. A total of 241 common differentially expressed genes (DEGs) were screened from GSE33294, GSE104310, and GSE144269. Functional analysis results implicated that DEGs are significantly associated with "cell cycle," "cell division," and "proliferation." The protein-protein interaction network analysis extracted ten hub genes from common DEGs. Ten hub genes were significantly overexpression in HCC tissues. Kaplan-Meier survival analysis revealed that 10 hub genes were linked with a poorer prognosis in HCC patients. Functional assays showed that CDK1 knockdown repressed HCC cell proliferation and migration. Luciferase reporter assay showed that miR-193b-3p could target CDK1 3' untranslated region, and miR-193b-3p negatively modulated CDK1. Enforced CDK1 expression attenuated miR-193b-3p-modulated suppressive actions on HCC cell proliferation and migration. To summarize, we performed a comprehensive bioinformatics analysis and identified 10 hub genes linked to the prognosis in HCC patients. Functional analysis revealed that CDK1, negatively regulated by miR-193b-3p, may act as an oncogene to promote HCC cell proliferation and migration and may predict poor prognosis of HCC patients. However, the role of CDK1/miR-193b-3p may still require further investigation.

The Epitope Basis of Embryonic Stem Cell-Induced Antitumor Immunity against Bladder Cancer

Embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) share many cellular and molecular features with cancer cells. Taking advantage of these similarities, stem cells are effective vaccines against cancers in animal models. However, the molecular basis is not well understood, which hinders the development of effective cancer vaccines. Here, prophylactic and therapeutic bladder cancer vaccines composed of allogeneic ESCs and CpG with or without granulocyte macrophage colony stimulating factor are tested. The ESC-based cancer vaccines are able to induce specific antitumor immunity including stimulating cytotoxic CD8⁺ T cells and memory CD4⁺ T cells, reducing myeloid-derived suppressor cells, and preventing bladder cancer growth in mouse models. Furthermore, several genes that are overexpressed in both ESCs and tumors are identified. An epitope-based vaccine designed with shared overexpressed proteins induces specific antitumor immunity and reduces bladder cancer growth. Functional epitopes underlying the action of stem cell-based vaccines against bladder cancer are identified and it is confirmed that ESC-based anticancer vaccines have great potential. A systematic approach is provided here to developing novel effective epitope-based cancer vaccines in the future.

Screening for Biomarkers for Progression from Oral Leukoplakia to Oral Squamous Cell Carcinoma and Evaluation of Diagnostic Efficacy by Multiple Machine Learning Algorithms

The aim of the study is to identify key genes during the progression from oral leukoplakia (OL) to oral squamous cell carcinoma (OSCC) and predict effective diagnoses. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to identify seven genes associated with the progression from OL to OSCC. Twelve machine learning algorithms including k-nearest neighbor (KNN), neural network (NNet), and extreme gradient boosting (XGBoost) were used to construct multi-gene models, which revealed that each model had good diagnostic efficacy. The functional mechanism or the pathways associated with these genes were evaluated using enrichment analysis, subtype clustering, and immune infiltration analysis. The enrichment analysis revealed that the genes enriched were associated with the cell cycle, cell division, and intracellular energy metabolism. The immunoassay results revealed that the genes primarily affected the infiltration of proliferating T cells and macrophage polarization. Finally, a nomogram and Kaplan-Meier survival analysis were used to predict the prognostic efficacy of key genes in OSCC patients. The results showed that genes could predict the prognosis of the patients, and patients in the high-risk group had a poor prognosis. Our study identified that the seven key genes, including DHX9, BCL2L12, RAD51, MELK, CDC6, ANLN, and KIF4A, were associated with the progression from OL to OSCC. These genes had good diagnostic efficacy and could be used as potential biomarkers for the prognosis of OSCC patients.

A review on the role of cyclin dependent kinases in cancers

The Cyclin-dependent kinase (CDK) class of serine/threonine kinases has crucial roles in the regulation of cell cycle transition and is mainly involved in the pathogenesis of cancers. The expression of CDKs is controlled by a complex regulatory network comprised of genetic and epigenetic mechanisms, which are dysregulated during the progression of cancer. The abnormal activation of CDKs results in uncontrolled cancer cell proliferation and the induction of cancer stem cell characteristics. The levels of CDKs can be utilized to predict the prognosis and treatment response of cancer patients, and further understanding of the function and underlying mechanisms of CDKs in human tumors would pave the way for future cancer therapies that effectively target CDKs. Defects in the regulation of cell cycle and mutations in the genes coding cell-cycle regulatory proteins lead to unrestrained proliferation of cells leading to formation of tumors. A number of treatment modalities have been designed to combat dysregulation of cell cycle through affecting expression or activity of CDKs. However, effective application of these methods in the clinical settings requires recognition of the role of CDKs in the progression of each type of cancer, their partners, their interactions with signaling pathways and the effects of suppression of these kinases on malignant features. Thus, we designed this literature search to summarize these findings at cellular level, as well as in vivo and clinical levels.

ASPM promotes ATR-CHK1 activation and stabilizes stalled replication forks in response to replication stress

ASPM (encoded by MCPH5) is a frequently mutated protein, and such mutations occur in >40% of cases of primary microcephaly (MCPH). Here, we characterize a function of ASPM in DNA replication and the replication stress response. ASPM serves as a scaffold to load stimulators required for ATR-CHK1 checkpoint signaling upon replication stress, which protects stalled replication forks from degradation. ASPM deficiency leads to genomic instability and the sensitization of cancer cells to replication stressors.

ASPM is a protein encoded by primary microcephaly 5 (MCPH5) and is responsible for ensuring spindle position during mitosis and the symmetrical division of neural stem cells. We recently reported that ASPM promotes homologous recombination (HR) repair of DNA double strand breaks. However, its potential role in DNA replication and replication stress response remains elusive. Interestingly, we found that ASPM is dispensable for DNA replication under unperturbed conditions. However, ASPM is enriched at stalled replication forks in a RAD17-dependent manner in response to replication stress and promotes RAD9 and TopBP1 loading onto chromatin, facilitating ATR-CHK1 activation. ASPM depletion results in failed fork restart and nuclease MRE11-mediated nascent DNA degradation at the stalled replication fork. The overall consequence is chromosome instability and the sensitization of cancer cells to replication stressors. These data support a role for ASPM in loading RAD17-RAD9/TopBP1 onto chromatin to activate the ATR-CHK1 checkpoint and ultimately ensure genome stability.

Comprehensive bioinformatics analysis reveals the prognostic value, predictive value, and immunological roles of ANLN in human cancers

Anillin (ANLN) is a unique scaffolding, actin-binding protein, which is essential for the integrity and ingression of the cleavage furrow. It is mainly involved in the cytokinesis process, while its role in various tumors has not been fully addressed and remains largely elusive. To provide a thorough perspective of ANLN’s roles among diverse malignancies, we conducted a comprehensive, pan-cancer analysis about ANLN, including but not limited to gene expression levels, prognostic value, biological functions, interacting proteins, immune-related analysis, and predictive value. As a result, when compared to normal tissues, ANLN expression is elevated in most cancers, and its expression also differs in different immune subtypes and molecular subtypes in diverse cancers. In addition, in 17 types of cancer, ANLN expression is increased in early tumor stages, and higher ANLN expression predicts worse survival outcomes in more than ten cancers. Furthermore, ANLN shows close correlations with the infiltration levels of most immune cells, and enrichment analysis using ANLN co-expressed genes reveals that ANLN plays essential roles in cell cycle, mitosis, cellular senescence, and p53 signaling pathways. In the final, ANLN exhibits high accuracy in predicting many cancers, and subsequent multivariate analysis suggests ANLN could be an independent prognostic factor in specific cancer types. Taken together, ANLN is proved to be a novel and promising biomarker for its excellent predictive utility, promising prognostic value, and potential immunological roles in pan-cancer. Targeting ANLN might be an attractive approach to tumor treatment.

A cancer stem cell associated gene signature for predicting overall survival of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent type of primary liver cancer characterized by high mortality and morbidity rate. The lack of effective treatments and the high frequency of recurrence lead to poor prognosis of patients with HCC. Therefore, it is important to develop robust prediction tools for predicting the prognosis of HCC. Recent studies have shown that cancer stem cells (CSC) participate in HCC progression. The aim of this study was to explore the prognostic value of CSC-related genes and establish a prediction model based on data from The Cancer Genome Atlas (TCGA) database. In this study, 475 CSC-related genes were obtained from the Molecular Signature Database and 160 differentially expressed CSC-related genes in HCC patients were identified using the limma R package in the TCGA database. A total of 79 CSC-related genes were found to be associated with overall survival (OS). Using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regressions, a 3-gene signature (RAB10, TCOF1, and PSMD14) was constructed. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were constructed to test the prediction performance of the signature. Performance of the signature was validated using the International Cancer Genome Consortium (ICGC) dataset. In addition, immune feature and functional enrichment analyses were carried out to explore the underlying mechanisms. Moreover, a co-expression network was constructed using the weighted gene correlation network analysis (WGCNA) method to select genes significantly associated with risk scores in HCC in the TCGA dataset. The SGO2 gene was found to be significantly associated with risk scores of HCC. In vitro experiments revealed that it can promote HCC cell proliferation. Therefore, SGO2 may be a potential therapeutic target for HCC treatment. The constructed nomogram can help clinicians make decisions about HCC treatment.

Comprehensive analysis of key genes and pathways for biological and clinical implications in thyroid-associated ophthalmopathy

Background

Thyroid-associated ophthalmopathy (TAO) is a common and organ-specific autoimmune disease. Early diagnosis and novel treatments are essential to improve the prognosis of TAO patients. Therefore, the current work was performed to identify the key genes and pathways for the biological and clinical implications of TAO through comprehensive bioinformatics analysis and a series of clinical validations.

Methods

GSE105149 and GSE185952 were obtained from the Gene Expression Omnibus (GEO) database for analysis. The data were normalized to identify the common differentially expressed genes (DEGs) between the two datasets, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to assess key pathways in TAO. Protein–protein interaction (PPI) networks and hub genes among the common DEGs were identified. Furthermore, we collected the general information and blood samples from 50 TAO patients and 20 healthy controls (HCs), and the expression levels of the proteins encoded by hub genes in serum were detected by enzyme-linked immunosorbent assay (ELISA). Then we further assessed the relationship between the ELISA data and the TAO development.

Results

Several common pathways, including neuroactive ligand-receptor interaction, the IL-17 signaling pathway, and the TNF signaling pathway, were identified in both datasets. In parallel, 52 common DEGs were identified. The KEGG analysis showed that these common DEGs are mainly enriched in long-term depression, the VEGF signaling pathway, the IL-17 signaling pathway, the TNF signaling pathway, and cytokine-cytokine receptor interactions. The key hub genes PRKCG, OSM, DPP4, LRRTM1, CXCL6, and CSF3R were screened out through the PPI network. As confirmation, the ELISA results indicated that protein expression levels of PRKCG, OSM, CSF3R, and DPP4 were significantly upregulated in TAO patients compared with HCs. In addition, PRKCG and DPP4 were verified to show value in diagnosing TAO, and CSF3R was found to be a valuable diagnostic marker in distinguishing active TAO from inactive TAO.

Conclusions

Inflammation- and neuromodulation-related pathways might be closely associated with TAO. Based on the clinical verification, OSM, CSF3R, CXCL6, DPP4, and PRKCG may serve as inflammation- or neuromodulation-related biomarkers for TAO, providing novel insights for the diagnosis and treatment of TAO.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08854-5.

Comparative Proteomic Analysis of Liver Tissues and Serum in db/db Mice

Background and Aims: Non-alcoholic fatty liver disease (NAFLD) affects one-quarter of individuals worldwide. Liver biopsy, as the current reliable method for NAFLD evaluation, causes low patient acceptance because of the nature of invasive sampling. Therefore, sensitive non-invasive serum biomarkers are urgently needed. Results: The serum gene ontology (GO) classification and Kyoto encyclopedia of genes and genomes (KEGG) analysis revealed the DEPs enriched in pathways including JAK-STAT and FoxO. GO analysis indicated that serum DEPs were mainly involved in the cellular process, metabolic process, response to stimulus, and biological regulation. Hepatic proteomic KEGG analysis revealed the DEPs were mainly enriched in the PPAR signaling pathway, retinol metabolism, glycine, serine, and threonine metabolism, fatty acid elongation, biosynthesis of unsaturated fatty acids, glutathione metabolism, and steroid hormone biosynthesis. GO analysis revealed that DEPs predominantly participated in cellular, biological regulation, multicellular organismal, localization, signaling, multi-organism, and immune system processes. Protein-protein interaction (PPI) implied diverse clusters of the DEPs. Besides, the paralleled changes of the common upregulated and downregulated DEPs existed in both the liver and serum were validated in the mRNA expression of NRP1, MUP3, SERPINA1E, ALPL, and ALDOB as observed in our proteomic screening. Methods: We conducted hepatic and serum proteomic analysis based on the leptin-receptor-deficient mouse (db/db), a well-established diabetic mouse model with overt obesity and NAFLD. The results show differentially expressed proteins (DEPs) in hepatic and serum proteomic analysis. A parallel reaction monitor (PRM) confirmed the authenticity of the selected DEPs. Conclusion: These results are supposed to offer sensitive non-invasive serum biomarkers for diabetes and NAFLD.

Up-Regulation of RACGAP1 Promotes Progressions of Hepatocellular Carcinoma Regulated by GABPA via PI3K/AKT Pathway

Hepatocellular carcinoma (HCC) is one of the dominating tumors causing death due to lack of timely discovery and valid treatment. Abnormal increase of Rac GTPase activating protein 1 (RACGAP1) has been verified to be an oncogene in plenty tumors. The profound mechanism of RACGAP1 was rarely reported in HCC. In this study, we explored the function and mechanism of RACGAP1 in HCC through multiple analysis and experiments. RACGAP1 expression was up-regulated in HCC samples and the high expression of RACGAP1 was an independent prognostic risk factor for HCC patients. Meanwhile, RACGAP1 promoted developments of HCC both in vitro and in vivo. We verified that RACGAP1 promoted proliferation of HCC via PI3K/AKT/CDK2 and PI3K/AKT/GSK3β/Cyclin D1 signaling pathway. RACGAP1 accelerated the invasion and metastasis of HCC via phosphorylation of GSK3β and nuclear translocation of β-catenin. Furthermore, by luciferase reporter assay and Chromatin immunoprecipitation (ChIP) assay, we confirmed Recombinant GA Binding Protein Transcription Factor Alpha (GABPA) regulated the transcription of RACGAP1. All these findings revealed that RACGAP1 promotes the progression of HCC through a novel mechanism, which might be a new therapeutic target for HCC patients.

Role of ferroptosis-related genes in coronary atherosclerosis and identification of key genes: integration of bioinformatics analysis and experimental validation

Background

Coronary atherosclerosis (CA) is the most common type of atherosclerosis. However, the inherent pathogenesis and mechanisms of CA are unclear, and the relationship with ferroptosis-related genes (FRGs) has not been reported. The purpose of this study was to use bioinformatics techniques to evaluate potential therapeutic targets for CA.Please provide the given name for author “Dingshun”.Please provide the given name for author “Dingshun”.

Methods

First, the GSE132651 dataset was acquired from the Gene Expression Omnibus database. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and Protein–Protein interaction network were successively conducted. Next, overlapping genes between hub genes and CA genes were found. FRGs were found when comparing the CA group with the normal group. The correlation between overlapping genes and FRGs was further analyzed. At last, we performed Elisa to validate the expression of these genes in human blood specimens. Mice aortic tissues were used for western blot to detect the expression of proteins.

Results

Based on the GSE132651 dataset, 102 differentially expressed genes were identified. Five overlapping genes between hub genes and CA genes were found (CCNA2, RRM2, PBK, PCNA, CDK1). TFRC and GPX4 were found to be FRGs. TFRC was positively correlated with CCNA2, PBK, PCNA, CDK1, RRM2, with CDK1 being the strongest correlation. GPX4 was negatively correlated with these genes, among which CCNA2 was the strongest correlation. The ELISA results showed that CCNA2, CDK1, and TFRC expression were markedly increased in serum of the CA samples compared with controls, while GPX4 expression was markedly decreased in the CA samples. The western blot results show that GPX4 expression was lower in the model group, TFRC, CDK1, and CCNA2 protein expression were high in the model group.

Conclusions

Ferroptosis-related genes GPX4 and TFRC were closely correlated with the identified overlapping genes CCNA2 and CDK1, which may serve as targeted therapies for the treatment of CA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02747-x.

Ferroptosis-Related Hub Genes in Hepatocellular Carcinoma: Prognostic Signature, Immune-Related, and Drug Resistance Analysis

Background: Hepatocellular carcinoma (HCC) is the most prevalent type of primary liver cancer with a high fatality rate and dismal prognosis because of frequent recurrence and lack of efficient therapies. Ferroptosis is a recently recognized iron-dependent cell death distinct from necroptosis and apoptosis. The relationship between ferroptosis-related hub gene expression and prognosis in HCC remains to be further elucidated.

Methods: Ferroptosis-related genes from the FerrDb database and the mRNA sequencing data and clinical information of HCC patients were obtained from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to identify a prognostic signature consisting of five ferroptosis-related hub genes in the TCGA cohort. The International Cancer Genome Consortium (ICGC) database was utilized to validate the reliability of the signature. Functional enrichment and immune-related analysis, including single-sample gene set enrichment analysis (ssGSEA), immune checkpoints, TIP-related genes, tumor stemness, and m6A-related genes, were performed to analyze the underlying mechanism. Additionally, the correlations between ferroptosis and drug resistance were evaluated using the NCI-60 database.

Results: A 5–hub-gene signature associated with ferroptosis was constructed by multivariate Cox regression analysis to stratify patients into two risk groups. Patients with high risk had worse prognosis than those with low risk. Multivariate Cox regression analysis uncovered that the risk score was an independent prognostic indicator. We also proved the signature’s predictive capacity using the Kaplan–Meier method and receiver operating characteristic (ROC) curve analysis. Functional analysis showed that nuclear division and the cell cycle were enriched. Immune-related analysis revealed that the signature was enriched in immune-related pathways. Moreover, the risk signature was significantly associated with immune cell infiltration, immune checkpoints, TIP-related genes, tumor stem cells, as well as m6A-related genes. Furthermore, these genes were crucial regulators of drug resistance.

Conclusion: We identified and validated a novel hub gene signature that is closely associated with ferroptosis as a new and efficient biomarker with favorable potential for predicting the prognosis of HCC patients. In addition, it also offers new insights into the molecular mechanisms of HCC and provides an effective approach for the treatment of HCC. Further studies are necessary to validate the results of our study.

Esophageal squamous cell carcinoma: Integrated bioinformatics analysis for differential gene expression with identification of hub genes and lncRNA

Background

Esophageal squamous cell carcinoma (ESCC) is a typical Gastro-Intestinal (GI) tract neoplasm. This study was conducted to know the Differential Expressed Genes (DEGs) profile of ESCC along with hub gene screening, lncRNA identification, and drug-genes interactions.

Methods

GSE161533, GSE20347, GSE45670 microarray datasets were retrieved from the NCBI Gene Expression Omnibus (GEO) database. GEO2R was used for the DEGs identification, whereas GO (Gene Ontology) and KEGG enrichment analysis were performed in DAVID. PPI network constructed using STRING and visualized with Cytoscape app with the help of MCODE. The top ten connectivity genes were selected as hub genes—further survival analysis was performed in the Kaplan-Meier plotter. Moreover, Boxplot, pathological stage plots were constructed using GEPIA (Gene Expression Profiling Interactive Analysis). The methylation heatmap assembled in the DiseaseMeth version 2.0. lncRNA (Long non-coding RNA) was identified comparing the list of genes in HUGO, and Gene-drug interactions were accumulated from the DgiDB platform.

Results

This experiment showed 16 upregulated, and 59 downregulated DEGs shared among the three datasets. Biological process analysis showed significant terms such as extracellular matrix disassembly and collagen catabolism. The extracellular region was detected as the most crucial cellular compartment. Notably, metalloen dopeptidease and serine-type endopeptidase activity showed significant molecular functions term. In contrast, transcriptional misregulation was a highly substantial KEGG pathway. Kaplan-Meier plotter showed higher expression of CXCL8, SPP1, MMP13, CXCL1, and TOP2A have a significant impact on the overall survival of the patients. Nine out of ten hub genes have significantly different expression levels than normal and cancer tissues. HYMAI was the only lncRNA commonly expressed upregulated among the three datasets. Drug-gene interaction showed multiple genes have no drug options exist till now.

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GSE161533, GSE20347, and GSE45670 microarray datasets were analyzed.

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16 upregulated and 59 downregulated DEGs shared among the three datasets.

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CXCL8, SPP1, MMP13, CXCL1, and TOP2A have a significant impact on survival.

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HYMAI was the only lncRNA commonly expressed.

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Multiple genes have no drug options that exist.

N7-Methylguanosine Genes Related Prognostic Biomarker in Hepatocellular Carcinoma

Background: About 90% of liver cancer-related deaths are caused by hepatocellular carcinoma (HCC). N7-methylguanosine (m7G) modification is associated with the biological process and regulation of various diseases. To the best of our knowledge, its role in the pathogenesis and prognosis of HCC has not been thoroughly investigated.

Aim: To identify N7-methylguanosine (m7G) related prognostic biomarkers in HCC. Furthermore, we also studied the association of m7G–related prognostic gene signature with immune infiltration in HCC.

Methods: The TCGA datasets were used as a training and GEO dataset “GSE76427” for validation of the results. Statistical analyses were performed using the R statistical software version 4.1.2.

Results: Functional enrichment analysis identified some pathogenesis related to HCC. We identified 3 m7G-related genes (CDK1, ANO1, and PDGFRA) as prognostic biomarkers for HCC. A risk score was calculated from these 3 prognostic m7G-related genes which showed the high-risk group had a significantly poorer prognosis than the low-risk group in both training and validation datasets. The 3- and 5-years overall survival was predicted better with the risk score than the ideal model in the entire cohort in the predictive nomogram. Furthermore, immune checkpoint genes like CTLA4, HAVCR2, LAG3, and TIGT were expressed significantly higher in the high-risk group and the chemotherapy sensitivity analysis showed that the high-risk groups were responsive to sorafenib treatment.

Conclusion: These 3 m7G genes related signature model can be used as prognostic biomarkers in HCC and a guide for immunotherapy and chemotherapy response. Future clinical study on this biomarker model is required to verify its clinical implications.

A Risk Model Developed Based on Necroptosis Predicts Overall Survival for Hepatocellular Carcinoma and Identification of Possible Therapeutic Drugs

Background

Necroptosis is a form of regulatory cell death (RCD) that attracts and activates immune cells, resulting in pro-tumor or anti-tumor effects. The purpose of this study was to investigate genes associated with necroptosis, to construct a risk score for predicting overall survival in patients with hepatocellular carcinoma, and to find potentially effective drugs.

Methods

The three algorithms ssGSEA, EPIC, and ESTIMATE were used to quantify the immune cell infiltration of the samples, differentially expressed genes (DEGs) analysis, and weighted gene co-expression network analysis were used to screen necroptosis related genes. Variables were screened according to random survival forest analysis, and combinations with significant p-values and a low number of genes were defined as prognostic signatures by using log-rank test after gene combination. Based on the sensitivity data of PRISM and CTRP2.0 datasets, we predicted the potential therapeutic agents for high-NRS patients.

Results

Seven genes such as TOP2A were used to define necroptosis-related risk score (NRS). The prognostic value of risk score was further validated, where high NRS was identified as a poor prognostic factor and tended to have higher grades of histologic grade, pathologic stage, T stage, BCLC, CLIP, and higher AFP. Higher NRS was also negatively correlated with the abundance of DCs, Neutrophils, Th17 cells, Macrophages, Endothelial, and positively correlated with Th2 cells. Necroptosis is often accompanied by the release of multiple cytokines, and we found that some cytokines were significantly correlated with both NRS and immune cells, suggesting that necroptosis may affect the infiltration of immune cells through cytokines. In addition, we found that TP53 mutations were more common in samples with high NRS, and these mutations may be associated with changes in NRS. Patients with high NRS may be more sensitive to gemcitabine, and gemcitabine may be an effective drug to improve the prognosis of patients with high NRS, which may play a role by inhibiting the expression of TOP2A.

Conclusions

We constructed a necroptosis-related scoring model to predict OS in HCC patients, and NRS was associated with immune response, TP53 mutation, and poor clinical classification in HCC patients. In addition, gemcitabine may be an effective drug for high-NRS patients.

Proteogenomics Integrating Reveal a Complex Network, Alternative Splicing, Hub Genes Regulating Heart Maturation

Heart maturation is an essentially biological process for neonatal heart transition to adult heart, thus illustrating the mechanism of heart maturation may be helpful to explore postnatal heart development and cardiac cardiomyopathy. This study combined proteomic analysis based on isobaric tags for relative and absolute quantitation (iTRAQ) and transcriptome analysis based on RNA sequencing to detect the proteins and genes associated with heart maturation in mice. The proteogenomics integrating analysis identified 254 genes/proteins as commonly differentially expressed between neonatal and adult hearts. Functional and pathway analysis demonstrated that these identified genes/proteins contribute to heart maturation mainly by regulating mRNA processing and energy metabolism. Genome-wide alternative splicing (AS) analysis showed that some important sarcomere and energy-associated genes undergo different AS events. Through the Cytoscape plug-in CytoHubba, a total of 23 hub genes were found and further confirmed by RT-qPCR. Next, we verified that the most up-regulated hub gene, Ogdhl, plays an essential role in heart maturation by detecting energy metabolism phenotype changes in the Ogdhl-interfering cardiomyocytes. Together, we revealed a complex gene network, AS genes and patterns, and candidate hub genes controlling heart maturation by proteome and transcriptome combination analysis.

High expression of PDZ-binding kinase is correlated with poor prognosis and immune infiltrates in hepatocellular carcinoma

Background

PDZ-binding kinase (PBK) encodes a serine/threonine protein kinase related to the dual specific mitogen-activated protein kinase kinase (MAPKK) family. There is evidence that overexpression of this gene is associated with tumorigenesis. However, the role of PBK in hepatocellular carcinoma (HCC) remains unclear. Therefore, we evaluated the prognostic role of PBK and its correlation with immune infiltrates in hepatocellular carcinoma.

Methods

The expression of PBK in pan-cancers was studied by Onconmine and TIMER. The expression of PBK in HCC patients and its relationship with clinicopathological characteristics were analyzed using The Gene Expression Profiling Interactive Analysis (GEPIA), The human protein atlas database (HPA), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) databases. Receiver operating characteristic (ROC) curve was used to determine the diagnostic value of PBK in HCC patients. The relationship between PBK and prognosis of HCC was performed by GEPIA and Kaplan Meier plotter web tool. The correlations between the clinical characteristics and overall survival were analyzed by Univariate Cox regression and Multivariate Cox hazards regression to identify possible prognostic factors for HCC patients. LinkedOmics was applied to investigate co-expression associated with PBK and to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The network map of PBK and related genes is constructed by GeneMANIA. Finally, TIMER and TISIDB were used to analyze the correlations between PBK and tumor-infiltrating immune cells.

Results

Multiple database analysis shows that PBK was highly expressed in many types of tumors, including hepatocellular carcinoma, and was significantly related to tumor stage (P=0.0089), age (P=0.0131), and race (P=0.0024) of HCC patients. The receiver operating characteristic (ROC) curve analysis showed that PBK had high diagnostic potential to HCC in GSE76427 (AUC=0.8799), GSE121248 (AUC=0.9224), GSE62232 (AUC=0.9975), and GSE84402 (AUC=0.9541). Multivariate Cox hazards regression showed that high expression of PBK may be an independent risk factor for overall survival in HCC patients (HR = 1.566, 95% CI=1.062–2.311, P= 0.024). The Protein–protein interaction network showed that PBK significantly interacted with LRRC47, ARAF, LGALS9B, TTK, DLG1, and other essential genes. Furthermore, enrichment analysis showed that PBK and co-expressed genes participated in many biological processes, cell composition, molecular functions, and pathways in HCC. Finally, the immune infiltration analysis by TIMER and TISIDB indicated that a significant tightly correlation between PBK and macrophages, neutrophils, as well as chemokines and receptors.

Conclusions

High expression of PBK is significantly correlated with poor survival and immune infiltrates in hepatocellular carcinoma. Our study suggests that PBK can be used as a biomarker of poor prognosis and potential immune therapy target in hepatocellular carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02479-w.

DTL Is a Prognostic Biomarker and Promotes Bladder Cancer Progression through Regulating the AKT/mTOR axis

Background

Denticleless E3 ubiquitin protein ligase homolog (DTL) has been reported to be an important regulator for tumorigenesis and progression. Nonetheless, the biological functions and molecular mechanisms of DTL in BCa remain elusive.

Methods

We implemented integrative bioinformatics analysis to explore the diagnostic and prognostic values of DTL based on The Cancer Genome Atlas (TCGA), ArrayExpress, and Gene Expression Omnibus (GEO) databases. Then, we utilized qRT-PCR and immunohistochemistry to verify the clinical significance of DTL expression according to clinical specimens and tissue microarray (TMA). Moreover, the biological functions and underlying mechanisms of DTL in BCa were investigated through in vitro and in vivo experiments.

Results

Integrative bioinformatics analysis revealed that DTL was a key gene associated with BCa progression, and increased DTL expression was correlated with malignant biological behavior and poor prognosis. Experiments on clinical specimens and tissue microarray (TMA) further confirmed our findings. Bioinformatics analysis demonstrated that DTL could be associated with cell cycle- and DNA replication-associated pathways in BCa. The suppression of DTL inhibited BCa cell proliferation, migration, and invasion in vivo and in vitro. Mechanistically, DTL may promote BCa progression through the AKT/mTOR pathway.

Conclusions

Increased DTL expression was correlated with malignant biological behavior and poor prognosis of BCa patients, and it may promote BCa progression through the AKT/mTOR pathway. Our research provided a potential predictor and therapeutic target for BCa.

Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis

Osteosarcoma, which arises from bone tissue, is considered to be one of the most common types of cancer in children and teenagers. As the etiology of osteosarcoma has not been fully elucidated, the overall prognosis for patients is generally poor. In recent years, the development of bioinformatical technology has allowed researchers to identify numerous molecular biological characteristics associated with the prognosis of osteosarcoma using online databases. In the present study, Gene Expression Omnibus (GEO) database was used and three microarray datasets were obtained. The GEO2R web tool was utilized and differentially expressed genes (DEGs) in osteosarcoma tissue were identified. Venn analysis was performed to determine the intersection of the DEG profiles. DEGs were analyzed by Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein-protein interactions (PPIs) between these DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes database, and the PPI network was then visualized using Cytoscape software. The top ten genes were identified based on measurement of degree, density of maximum neighborhood component, maximal clique centrality and mononuclear cell counts in the PPI network, and five overlapping genes [origin recognition complex subunit 6 (ORC6), IGF-binding protein 5 (IGFBP5), minichromosome maintenance 10 replication initiation factor (MCM10), MET proto-oncogene, receptor tyrosine kinase (MET) and centromere protein F (CENPF)] were identified. Additionally, three module networks were analyzed by Molecular Complex Detection (MCODE), and six key genes [ORC6, MCM10, DEP domain containing 1 (DEPDC1), CENPF, TIMELESS interacting protein (TIPIN) and shugoshin 1 (SGOL1)] were screened. Combined with the results from Cytoscape and MCODE, eight hub genes (ORC6, MCM10, DEPDC1, CENPF, TIPIN, SGOL1, MET and IGFBP5) were obtained. Furthermore, Kaplan-Meier plotter survival analysis was used to evaluate the prognostic value of these eight hub genes in patients with osteosarcoma. Oncomine and GEPIA databases were applied to further confirm the expression levels of hub genes in tissue. Finally, the functional roles of the core gene CENPF were investigated using Cell Counting Kit-8, wound healing and Transwell assays, which indicated that CENPF knockdown inhibited the proliferation, migration and invasion of osteosarcoma cells. These results provided potential prognostic markers, as well as a basis for further investigation of the mechanism underlying osteosarcoma.

Clinical significance and effect of lncRNA BBOX1-AS1 on the proliferation and migration of lung squamous cell carcinoma

Long non-coding RNAs (lncRNAs) have a role in the occurrence and development of lung squamous cell carcinoma (LUSC). lncRNA γ-butyrobetaine hydroxylase 1 (BBOX1)-antisense 1 (AS1) may contribute to disease development. However, there are no studies on the role of BBOX1-AS1 in LUSC to date. In the present study, an in-house gene microarray analysis was performed to detect the differentially expressed lncRNAs and mRNAs between three pairs of LUSC and normal lung tissues. Only one lncRNA, BBOX1-AS1, was differentially expressed in the in-house microarray and The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and ArrayExpress databases. Reverse transcription-quantitative PCR (RT-qPCR) was then performed and the original RNA-sequencing data from the TCGA, GEO and ArrayExpress datasets were used to determine the expression and clinical value of BBOX1-AS1 in LUSC. In addition, a Cell Counting Kit-8 assay, cell cycle analysis and scratch assay were performed to explore whether BBOX1-AS1 expression affected the proliferation and migration of LUSC cells in vitro. The results of the RT-qPCR analysis and data obtained from the TCGA database, GEO datasets, in-house gene microarray and standard mean deviation analysis all supported the upregulated expression level of BBOX1-AS1 in LUSC. Furthermore, silencing of BBOX1-AS1 inhibited the proliferation and migration of LUSC cells according to in vitro assays. In addition, the cells were arrested in S-phase after knockdown of BBOX1-AS1. In conclusion, the expression level of BBOX1-AS1 was upregulated in LUSC tissues. BBOX1-AS1 may exert an oncogenic effect on LUSC by regulating various biological functions. However, additional functional experiments should be performed to verify the exact mechanism.

Bioinformatic Evidence Reveals that Cell Cycle Correlated Genes Drive the Communication between Tumor Cells and the Tumor Microenvironment and Impact the Outcomes of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is an aggressive cancer type with poor prognosis; thus, there is especially necessary and urgent to screen potential prognostic biomarkers for early diagnosis and novel therapeutic targets. In this study, we downloaded target data sets from the GEO database, and obtained codifferentially expressed genes using the limma R package and identified key genes through the protein–protein interaction network and molecular modules, and performed GO and KEGG pathway analyses for key genes via the clusterProfiler package and further determined their correlations with clinicopathological features using the Oncomine database. Survival analysis was completed in the GEPIA and the Kaplan–Meier plotter database. Finally, correlations between key genes, cell types infiltrated in the tumor microenvironment (TME), and hypoxic signatures were explored based on the TIMER database. From the results, 11 key genes related to the cell cycle were determined, and high levels of these key genes' expression were focused on advanced and higher grade status HCC patients, as well as in samples of TP53 mutation and vascular invasion. Besides, the 11 key genes were significantly associated with poor prognosis of HCC and also were positively related to the infiltration level of MDSCs in the TME and the HIF1A and VEGFA of hypoxic signatures, but a negative correlation was found with endothelial cells (ECs) and hematopoietic stem cells. The result determined that 11 key genes (RRM2, NDC80, ECT2, CCNB1, ASPM, CDK1, PRC1, KIF20A, DTL, TOP2A, and PBK) could play a vital role in the pathogenesis of HCC, drive the communication between tumor cells and the TME, and act as probably promising diagnostic, therapeutic, and prognostic biomarkers in HCC patients.

CCNB1 promotes the development of hepatocellular carcinoma by mediating DNA replication in the cell cycle

In our studies, cyclin B1 (CCNB1) mRNA and protein were overexpressed in hepatocellular carcinoma (HCC) tissues compared with non-HCC tissues. Moreover, CCNB1 was overexpressed in the serum of HCC patients. The expression of CCNB1 was associated with several crucial clinicopathologic characteristics, and the HCC patients with overexpressed CCNB1 had worse overall survival outcomes. In the screening of interactional genes, a total of 266 upregulated co-expression genes, which were positively associated with CCNB1, were selected from the datasets, and 67 downregulated co-expression genes, which were negatively associated with CCNB1, were identified. The key genes might be functionally enriched in DNA replication and the cell cycle pathways. CDC20, CCNA2, PLK1, and FTCD were selected for further research because they were highly connected in the protein-protein interaction networks. Upregulated CDC20, CCNA2, and PLK1 and downregulated FTCD might result in undesirable overall survival outcomes for HCC patients. The univariate Cox analysis results showed that CDC20 and PLK1 might be two independent risk factors, while FTCD might be protective in HCC. Therefore, CCNB1 may participate in the cell cycle of HCC by regulating DNA replication, and CCNB1 may provide a direction for the diagnosis of early-stage HCC and targeted HCC therapy.

A Five-Gene-Based Prognostic Signature for Hepatocellular Carcinoma

Objective: This study intends to identify potential prognostic marker genes associated with the prognosis of patients suffering from hepatocellular carcinoma (HCC) based on TCGA and GEO analysis.

Methods: TCGA-LIHC cohort was downloaded and the data related to HCC were extracted from The Cancer Genome Atlas (TCGA) database and subjected to differential analysis. HCC-related gene expression datasets were retrieved from the GEO database, followed by differential analysis. After intersection of the results of TCGA and GEO databases, gene interaction analysis was performed to obtain the core genes. To identify the genes related to the prognosis of HCC patients, we conducted univariate and multivariate Cox analyses.

Results: Based on differential analysis of TCGA database, 854 genes were differentially expressed in HCC, any of which might link to the occurrence and progression of HCC. Meanwhile, joint analysis of HCC-related gene expression datasets in the GEO database screened 214 genes. Five core genes CDC20, TOP2A, RRM2, UBE2C and AOX1 were significantly associated with the prognosis of HCC patients and the risk model based on these five genes effectively predicted the prognosis of HCC patients.

Conclusion: Collectively, our data suggest that CDC20, TOP2A, RRM2, UBE2C and AOX1 may be the key genes affecting the prognosis of patients with HCC. The five-gene signature could accurately predict the prognosis of HCC patients.

ASPM combined with KIF11 promotes the malignant progression of hepatocellular carcinoma via the Wnt/β-catenin signaling pathway

To investigate the molecular mechanism of assembly factor for spindle microtubules (ASPM) in the regulation of the malignant progression of hepatocellular carcinoma (HCC), bioinformatics analysis was utilized to analyze the role of ASPM in the malignant progression of HCC and its potential interaction with the kinesin family member 11 (KIF11) gene. The expression levels of ASPM and KIF11 were detected by reverse transcription-quantitative PCR and western blotting. Following knockdown of ASPM expression, Cell Counting Kit-8/colony formation assays were performed to detect cell viability and proliferation. Wound healing and Transwell assays were employed to detect cell migration and invasion. Additionally, a co-immunoprecipitation (CO-IP) assay was used to detect whether there was an interaction between ASPM and KIF11. KIF11 overexpression was performed to verify if ASPM exerted its effects via KIF11. ASPM was highly expressed in HCC tissues and cells, and was closely associated with a poor prognosis of patients with HCC. Interference with ASPM expression markedly inhibited the viability, proliferation, invasion and migration of HCC cells. Using a CO-IP assay, it was revealed that there was an interaction between ASPM and KIF11. Rescue experiments subsequently revealed the regulatory effects of ASPM on the activity, proliferation, invasion and migration of HCC cells via KIF11. Finally, western blot analysis demonstrated that ASPM in combination with KIF11 promoted the malignant progression of HCC by regulating the activity of the Wnt/β-catenin signaling pathway. Therefore, the present study demonstrated that ASPM may interact with KIF11 in HCC cells to promote the malignant progression of HCC via the Wnt/β-catenin signaling pathway.

Investigation of Potential Molecular Biomarkers for Diagnosis and Prognosis of AFP-Negative HCC

Background

Alpha-fetoprotein (AFP) is the most important diagnostic and prognostic index of hepatocellular carcinoma (HCC). AFP-positive HCC can be easily diagnosed based on the serum AFP level and typical imaging features, but a number of HCC patients are negative (AFP < 20 ng/mL) for AFP. Therefore, it is necessary to develop novel diagnostic and prognostic biomarkers for AFP-negative HCC.

Methods

RNA data from TCGA and differential expression of lncRNAs, miRNAs, and mRNAs were downloaded to analyze the differential RNA expression patterns between AFP-negative HCC tissues and normal tissues. A lncRNA-miRNA-mRNA ceRNA regulatory network was constructed to elucidate the interaction mechanism of RNAs. Functional enrichment analysis of these DEmRNAs was performed to indirectly reveal the mechanism of action of lncRNAs. A PPI network was built using STRING, and the hub genes were identified with Cytoscape. The diagnostic value of hub genes was assessed with receiver operating characteristic (ROC) analysis. And the prognostic value of RNAs in the ceRNA was estimated with Kaplan-Meier curve analysis.

Results

A total of 131 lncRNAs, 185 miRNA, and 1309 mRNAs were found to be differentially expressed in AFP-negative HCC. A ceRNA network consisting of 12 lncRNA, 23 miRNA, and 74 mRNA was constructed. The top ten hub genes including EZH2, CCNB1, E2F1, PBK, CHAF1A, ESR1, RRM2, CCNE1, MCM4, and ATAD2 showed good diagnostic power under the ROC curve; and 2 lncRNAs (LINC00261, LINC00482), 3 miRNAs (hsa-miR-93, hsa-miR-221, hsa-miR-222), and 2 mRNAs (EGR2, LPCAT1) were found to be associated with the overall survival of AFP-negative patients.

Conclusion

This study could provide a novel insight into the molecular pathogenesis of AFP-negative HCC and reveal some candidate diagnostic and prognostic biomarkers for AFP-negative HCC.

Prognostic Value of a Novel Signature With Nine Hepatitis C Virus-Induced Genes in Hepatic Cancer by Mining GEO and TCGA Databases

Background

Hepatitis C virus-induced genes (HCVIGs) play a critical role in regulating tumor development in hepatic cancer. The role of HCVIGs in hepatic cancer remains unknown. This study aimed to construct a prognostic signature and assess the value of the risk model for predicting the prognosis of hepatic cancer.

Methods

Differentially expressed HCVIGs were identified in hepatic cancer data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases using the library (“limma”) package of R software. The protein–protein interaction (PPI) network was constructed using the Cytoscape software. Functional enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate and multivariate Cox proportional hazard regression analyses were applied to screen for prognostic HCVIGs. The signature of HCVIGs was constructed. Gene Set Enrichment Analysis (GSEA) compared the low-risk and high-risk groups. Finally, the International Cancer Genome Consortium (ICGC) database was used to validate this prognostic signature. Polymerase chain reaction (PCR) was performed to validate the expression of nine HCVIGs in the hepatic cancer cell lines.

Results

A total of 143 differentially expressed HCVIGs were identified in TCGA hepatic cancer dataset. Functional enrichment analysis showed that DNA replication was associated with the development of hepatic cancer. The risk score signature was constructed based on the expression of ZIC2, SLC7A11, PSRC1, TMEM106C, TRAIP, DTYMK, FAM72D, TRIP13, and CENPM. In this study, the risk score was an independent prognostic factor in the multivariate Cox regression analysis [hazard ratio (HR) = 1.433, 95% CI = 1.280–1.605, P < 0.001]. The overall survival curve revealed that the high-risk group had a poor prognosis. The Kaplan–Meier Plotter online database showed that the survival time of hepatic cancer patients with overexpression of HCVIGs in this signature was significantly shorter. The prognostic signature-associated GO and KEGG pathways were significantly enriched in the risk group. This prognostic signature was validated using external data from the ICGC databases. The expression of nine prognostic genes was validated in HepG2 and LO-2.

Conclusion

This study evaluates a potential prognostic signature and provides a way to explore the mechanism of HCVIGs in hepatic cancer.