Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis

KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker

KIF2C/MCAK (KIF2C) is the most well-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule (MT) dynamics during mitosis, as well as interphase. This systematic review briefly describes the important structural elements of KIF2C, its regulation by multiple molecular mechanisms, and its broad cellular functions. Furthermore, it systematically summarizes its oncogenic potential in malignant progression and performs a meta-analysis of its prognostic value in cancer patients. KIF2C was shown to be involved in multiple crucial cellular processes including cell migration and invasion, DNA repair, senescence induction and immune modulation, which are all known to be critical during the development of malignant tumors. Indeed, an increasing number of publications indicate that KIF2C is aberrantly expressed in multiple cancer entities. Consequently, we have highlighted its involvement in at least five hallmarks of cancer, namely: genome instability, resisting cell death, activating invasion and metastasis, avoiding immune destruction and cellular senescence. This was followed by a systematic search of KIF2C/MCAK's expression in various malignant tumor entities and its correlation with clinicopathologic features. Available data were pooled into multiple weighted meta-analyses for the correlation between KIF2Chigh protein or gene expression and the overall survival in breast cancer, non-small cell lung cancer and hepatocellular carcinoma patients. Furthermore, high expression of KIF2C was correlated to disease-free survival of hepatocellular carcinoma. All meta-analyses showed poor prognosis for cancer patients with KIF2Chigh expression, associated with a decreased overall survival and reduced disease-free survival, indicating KIF2C's oncogenic potential in malignant progression and as a prognostic marker. This work delineated the promising research perspective of KIF2C with modern in vivo and in vitro technologies to further decipher the function of KIF2C in malignant tumor development and progression. This might help to establish KIF2C as a biomarker for the diagnosis or evaluation of at least three cancer entities.

scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

Identifying possible hub genes and biological mechanisms shared between bladder cancer and inflammatory bowel disease using machine learning and integrated bioinformatics

BACKGROUND

Recent studies have shown that inflammatory bowel disease (IBD) is associated with bladder cancer (BC) incidence. But there is still a lack of understanding regarding its pathogenesis. Thus, this study aimed to identify potential hub genes and their important pathways and pathological mechanisms of interactions between IBD and BC using bioinformatics methods.

METHODS

The data from Gene Expression Omnibus (GEO) and the cancer genome atlas (TCGA) were analyzed to screen common differentially expressed genes (DEGs) between IBD and BC. The "clusterProfiler" package was used to analyze GO term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment in DEGs. After that, we conducted a weighted gene co-expression network analysis (WGCNA) on these DEGs to determine the vital modules and genes significantly related to BC. Protein-protein interaction (PPI) networks was used to identify hub genes. Further, the hub genes were used to develop a prognostic signature by Cox analysis. The validity of the ten hub DEGs was tested using three classification algorithms. Finally, we analyzed the microRNAs (miRNA)-mRNA, transcription factors (TFs)-mRNA regulatory network.

RESULTS

Positive regulation of organelle fission, chromosomal region, tubulin binding, and cell cycle signaling pathway were the major enriched pathways for the common DEGs. PPI networks identified three hub proteins (AURKB, CDK1, and CCNA2) with high connectivity. Three machine-learning classification algorithms based on ten hub genes performed well for IBD and BC (accuracy > 0.80). The robust predictive model based on the ten hub genes could accurately classify BC cases with various clinical outcomes. Based on the gene-TFs and gene-miRNAs network construction, 9 TFs and 6 miRNAs were identified as potential critical TFs and miRNAs. There are 13 drugs that interact with the hub gene based on gene-drug interaction analysis.

CONCLUSIONS

This study explored common gene signatures and the potential pathogenesis of IBD and BC. We revealed that an unbalanced immune response, cell cycle pathway, and neutrophil infiltration might be the common pathogenesis of IBD and BC. Molecular mechanisms for the treatment of IBD and CC still require further investigation.

Identification of hub genes in digestive system of mandarin fish (Siniperca chuatsi) fed with artificial diet by weighted gene co-expression network analysis

Mandarin fish (Siniperca chuatsi) is a carnivorous freshwater fish and an economically important species. The digestive system (liver, stomach, intestine, pyloric caecum, esophagus, and gallbladder) is an important site for studying fish domestication. In our previous study, we found that mandarin fish undergoes adaptive changes in histological morphology and gene expression levels of the digestive system when subjected to artificial diet domestication. However, we are not clear which hub genes are highly associated with domestication. In this study, we performed WGCNA on the transcriptomes of 17 tissues and 9 developmental stages and combined differentially expressed genes analysis in the digestive system to identify the hub genes that may play important functions in the adaptation of mandarin fish to bait conversion. A total of 31,657 genes in 26 samples were classified into 23 color modules via WGCNA. The modules midnightblue, darkred, lightyellow, and darkgreen highly associated with the liver, stomach, esophagus, and gallbladder were extracted, respectively. Tan module was highly related to both intestine and pyloric caecum. The hub genes in liver were cp, vtgc, c1in, c9, lect2, and klkb1. The hub genes in stomach were ghrl, atp4a, gjb3, muc5ac, duox2, and chia2. The hub genes in esophagus were mybpc1, myl2, and tpm3. The hub genes in gallbladder were dyst, npy2r, slc13a1, and slc39a4. The hub genes in the intestine and pyloric caecum were slc15a1, cdhr5, btn3a1, anpep, slc34a2, cdhr2, and ace2. Through pathway analysis, modules highly related to the digestive system were mainly enriched in digestion and absorption, metabolism, and immune-related pathways. After domestication, the hub genes vtgc and lect2 were significantly upregulated in the liver. Chia2 was significantly downregulated in the stomach. Slc15a1, anpep, and slc34a2 were significantly upregulated in the intestine. This study identified the hub genes that may play an important role in the adaptation of the digestive system to artificial diet, which provided novel evidence and ideas for further research on the domestication of mandarin fish from molecular level.

Targeting IGF2BP3 in Cancer

RNA-binding proteins (RBPs) can regulate multiple pathways by binding to RNAs, playing a variety of functions, such as localization, stability, and immunity. In recent years, with the development of technology, researchers have discovered that RBPs play a key role in the N6-methyladenosine (m6A) modification process. M6A methylation is the most abundant form of RNA modification in eukaryotes, which is defined as methylation on the sixth N atom of adenine in RNA. Insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) is one of the components of m6A binding proteins, which plays an important role in decoding m6A marks and performing various biological functions. IGF2BP3 is abnormally expressed in many human cancers, often associated with poor prognosis. Here, we summarize the physiological role of IGF2BP3 in organisms and describe its role and mechanism in tumors. These data suggest that IGF2BP3 may be a valuable therapeutic target and prognostic marker in the future.

Comprehensive analysis of FOXM1 immune infiltrates, m6a, glycolysis and ceRNA network in human hepatocellular carcinoma

Background

Forkhead box M1 (FOXM1) is a member of the Forkhead box (Fox) transcription factor family. It regulates cell mitosis, cell proliferation, and genome stability. However, the relationship between the expression of FOXM1 and the levels of m6a modification, immune infiltration, glycolysis, and ketone body metabolism in HCC has yet to be fully elucidated.

Methods

Transcriptome and somatic mutation profiles of HCC were downloaded from the TCGA database. Somatic mutations were analyzed by maftools R package and visualized in oncoplots. GO, KEGG and GSEA function enrichment was performed on FOXM1 co-expression using R. We used Cox regression and machine learning algorithms (CIBERSORT, LASSO, random forest, and SVM-RFE) to study the prognostic value of FOXM1 and immune infiltrating characteristic immune cells in HCC. The relationship between FOXM1 and m6A modification, glycolysis, and ketone body metabolism were analyzed by RNA-seq and CHIP-seq. The competing endogenous RNA (ceRNA) network construction relies on the multiMiR R package, ENCORI, and miRNET platforms.

Results

FOXM1 is highly expressed in HCC and is associated with a poorer prognosis. At the same time, the expression level of FOXM1 is significantly related to the T, N, and stage. Subsequently, based on the machine learning strategies, we found that the infiltration level of T follicular helper cells (Tfh) was a risk factor affecting the prognosis of HCC patients. The high infiltration of Tfh was significantly related to the poor overall survival rate of HCC. Besides, the CHIP-seq demonstrated that FOXM1 regulates m6a modification by binding to the promoter of IGF2BP3 and affects the glycolytic process by initiating the transcription of HK2 and PKM in HCC. A ceRNA network was successfully obtained, including FOXM1 - has-miR-125-5p - DANCR/MIR4435-2HG ceRNA network related to the prognosis of HCC.

Conclusion

Our study implicates that the aberrant infiltration of Tfh associated with FOXM1 is a crucial prognostic factor for HCC patients. FOXM1 regulates genes related to m6a modification and glycolysis at the transcriptional level. Furthermore, the specific ceRNA network can be used as a potential therapeutic target for HCC.

Blocking the CXCL1-CXCR2 axis enhances the effects of doxorubicin in HCC by remodelling the tumour microenvironment via the NF-κB/IL-1β/CXCL1 signalling pathway

Inflammation is a core mechanism for oncogenesis. Chemokines act as important mediators of chronic inflammation and the tumour inflammatory response. However, there is limited information on chemokines in hepatocellular carcinoma (HCC), a disease for which almost all cases are derived from chronic liver inflammation. Here, we explored the protumor effects of CXCL1, a commonly elevated inflammatory chemokine in cirrhosis, in HCC. The protumor role was confirmed in clinical samples from HCC patients. CXCL1 enhanced tumorigenesis in the hepatic inflammatory microenvironment directly by acting on tumour cells and indirectly through promoting the recruitment of macrophages. The increase in the number of macrophages in the tumour microenvironment (TME) promoted tumour cell epithelial-mesenchymal transition (EMT) and significantly increased CXCL1 levels in the TME partly through NF-κB/IL-1β activation. To investigate the potential therapeutic value of CXCL1 in HCC with an inflammatory background, an antibody blocking CXCL1 was used alone or combined with the chemotherapy agent doxorubicin (DOX), with the goal of reshaping the TME. It has been shown that blocking CXCL1-CXCR2 inhibits tumour progression and reduces macrophage recruitment in the TME. The combination regimen has been shown to synergistically reduce the number of pro-tumour macrophages in the TME and suppress tumour progression. This provides insight into therapeutic strategies for treating HCC patients with high CXCL1 expression.

Up‐regulation of PRKDC was associated with poor renal dysfunction after renal transplantation: A multi‐centre analysis

Renal transplantation is the only efficacious treatment for end-stage kidney disease. However, some people have developed renal insufficiency after transplantation, the mechanisms of which have not been well clarified. Previous studies have focused on patient factors, while the effect of gene expression in the donor kidney on post-transplant renal function has been less studied. Donor kidney clinical data and mRNA expression status were extracted from the GEO database (GSE147451). Weight gene co-expression network analysis (WGCNA) and differential gene enrichment analysis were performed. For external validation, we collected data from 122 patients who accepted renal transplantation at several hospitals and measured the level of target genes by qPCR. This study included 192 patients from the GEO data set, and 13 co-expressed genes were confirmed by WGCNA and differential gene enrichment analysis. Then, the PPI network contained 17 edges as well as 12 nodes, and four central genes (PRKDC, RFC5, RFC3 and RBM14) were identified. We found by collecting data from 122 patients who underwent renal transplantation in several hospitals and by multivariate logistic regression that acute graft-versus-host disease postoperative infection, PRKDC [Hazard Ratio (HR) = 4.44; 95% CI = [1.60, 13.68]; p = 0.006] mRNA level correlated with the renal function after transplantation. The prediction model constructed had good predictive accuracy (C-index = 0.886). Elevated levels of donor kidney PRKDC are associated with renal dysfunction after transplantation. The prediction model of renal function status for post-transplant recipients based on PRKDC has good predictive accuracy and clinical application.

Harshening stem cell research and precision medicine: The states of human pluripotent cells stem cell repository diversity, and racial and sex differences in transcriptomes

In vitro investigation on human development, disease modeling, and drug discovery has been empowered by human induced pluripotent stem cell (hiPSC) technologies that form the foundation of precision medicine. Race and sex genetic backgrounds have become a major focus of many diseases modeling and drug response evaluation in the pharmaceutical industry. Here, we gathered data from major stem cell repositories to analyze the diversity with respect to ethnicity, sex, and disease types; and we also analyzed public datasets to unravel transcriptomics differences between samples of different ethnicities and sexes. We found a lack of diversity despite the large sample size of human induced pluripotent stem cells. In the ethnic comparison, the White group made up the majority of the banked hiPSCs. Similarly, for the organ/disease type and sex comparisons, the neural and male hiPSCs accounted for the majority of currently available hiPSCs. Bulk RNA-seq and single-cell transcriptomic analysis coupled with Machine Learning and Network Analysis revealed panels of gene features differently expressed in healthy hiPSCs and human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) of different races and sexes. The data highlights the current ethnic and sex inequality in stem cell research and demonstrates the molecular biological diversity of hiPSCs and cardiomyocytes from different races and genders. We postulate that future efforts in stem cell biology, regenerative and precision medicine should be guided towards an inclusive, diverse repository reflecting the prevalence of diseases across racial and ethnic groups and the sexes, important for both common and rare disease modeling, drug screening, and cell therapeutics.

Identification and validation of core genes as promising diagnostic signature in hepatocellular carcinoma based on integrated bioinformatics approach

The primary objective of this investigation was to determine the hub genes of hepatocellular carcinoma (HCC) through an in silico approach. In the current context of the increased incidence of liver cancers, this approach could be a useful prognostic biomarker and HCC prevention target. This study aimed to examine hub genes for immune cell infiltration and their good prognostic characteristics for HCC research. Human genes selected from databases (Gene Cards and DisGeNET) were used to identify the HCC markers. Further, classification of the hub genes from communicating genes was performed using data derived from the targets' protein-protein interaction (PPI) platform. The expression as well as survival studies of all these selected genes were validated by utilizing databases such as GEPIA2, HPA, and immune cell infiltration. Based on the studies, five hub genes (TP53, ESR1, AKT1, CASP3, and JUN) were identified, which have been linked to HCC. They may be an important prognostic biomarker and preventative target of HCC. In silico analysis revealed that out of five hub genes, the TP53 and ESR1 hub genes potentially act as key targets for HCC prevention and treatment.

Pan-cancer analysis of prognostic and immunological role of DTYMK in human tumors

Background: Deoxythymidylate kinase (DTYMK) has been reported to correlate with the progression of hepatocellular carcinoma. However, the role of DTYMK in human cancers is not studied. In this study, we studied the prognostic value, functional states, and correlations with immune infiltration of DTYMK in human cancers.

Methods: The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), UALCAN, Clinical Proteomic Tumor Analysis Consortium (CPTAC), the search tool for the retrieval of interacting genes (STRING), GeneMANIA, cBioPortal, Cancer Single-cell State Atlas (CancerSEA), and Tumor IMmune Estimation Resource (TIMER) databases were utilized to analyze DTYMK in cancers.

Results: In general, DTYMK is abnormally expressed between most human cancer and normal tissues from a pan-cancer perspective. DTYMK can be used as a diagnostic biomarker to differentiate tumor tissues from normal tissues in most tumors. Upregulation of DTYMK predicted poor survival status in most cancer types in TCGA. Moreover, DTYMK expression was correlated with the T stage in ACC, BRCA, KIRC, LIHC, and LUAD, with the N stage in BLCA, HNSC, KICH, KIRC, LUAD, LUSC, and THCA, with the M stage in ACC, KIRC, KIRP, and LUAD, with TNM stage in ACC, KIRC, LIHC, LUAD, and LUSC. In addition, based on single-cell sequencing data, we concluded that the expression of DTYMK was correlated with the functional status of the cell cycle, DNA damage, DNA repair, invasion, EMT, and proliferation. Finally, DTYMK expression was correlated with six infiltrating immune cells, including B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells by investigating TIMER.

Conclusion: Our findings suggested that abnormally expressed DTYMK was correlated with poor survival, malignant functional status, and immune infiltrates. DTYMK might be served as a potential biomarker for diagnosis and poor prognosis in various cancer types. DTYMK might act as a potential target for immune therapy.

BIRC5 Modulates PD-L1 Expression and Immune Infiltration in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the most prevalent thoracic cancer with the highest incidence and mortality worldwide. Baculoviral IAP Repeat Containing 5 (BIRC5) is well studied in many malignancies, its prognosis value and correlation with the tumor microenvironment (TME) in LAUD remains largely elusive.

Methods: The Wilcoxon signed-rank test and logistic regression were used to evaluate the relationship between clinical features and BIRC5 expression in LUAD. To assess the impact of BIRC5 on prognosis, the Kaplan-Meier plotter analysis and Cox regression were used, as well as a receiver operating characteristic (ROC) curve and nomogram. Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were recruited to predict the association between BIRC5 and immune cell infiltrations. Furthermore, qRT-PCR and western bolt were utilized to confirm gene expression on mRNA and protein levels. The proliferation of A549 and H1299 cells was evaluated using CCK8 and EdU assay. Cell mobility was tested by transwell assay and wound healing assay. Detection of PD-L1 and infiltrated CD8 T cells in xenograft tumors was done by flow cytometry.

Results: BIRC5 expression was found to be substantially greater in LUAD patients. According to KM-plotter analysis, patients with high levels of BIRC5 had shorter survival rates. Multivariate Cox analysis revealed that elevated BIRC5 expression was an independent risk factor for OS and PFS in LUAD patients. High BIRC5 expression was predicted to be associated with chemokine activity and immune cell chemotaxis, whereas ssGSEA suggested that BIRC5 is highly associated with CD8 T cell infiltration and PD-L1 levels. In vitro experiments suggested overexpression of BIRC5 promoted the proliferation, mobility, and PD-L1 level of A549 cells, and vice versa in H1299 cells. Furthermore, in vivo study suggested elevated tumor weight and PD-L1 levels in xenograft tumors generated from LLC cells with overexpressed BIRC5.

Conclusion: BIRC5 promotes lung adenocarcinoma progression by modulating PD-L1 expression and inducing tumor immune evasion.

Identifying a Novel Endoplasmic Reticulum-Related Prognostic Model for Hepatocellular Carcinomas

From the standpoint of the ER (endoplasmic reticulum), we were interested in identifying hub genes that impact clinical prognosis for HCC (hepatocellular carcinoma) patients and developing an ER-related prognostic model. Using TCGA-LIHC (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) and GSE14520 datasets, we conducted a series of analyses, which included differential gene screening, clinical prognostic analysis, Lasso regression, nomogram prediction, tumour clustering, gene functional enrichment, and tumour infiltration of immune cells. Following our screening for ER-related genes ( $n=1975$ ), we conducted a Lasso regression model to obtain five hub genes, KPNA2, FMO3, SPP1, KIF2C, and LPCAT1, using TCGA-LIHC as a training set. According to risk scores, HCC samples within either the TCGG-LIHC or GSE14520 cohort were categorized into high- and low-risk groups. Compared to the high-risk group of HCC patients, patients in the low-risk group had a better prognosis of OS (overall survival) or RFS (relapse-free survival). For TCGA-LIHC training set, with the factors of risk score, stage, age, and sex, we plotted a nomogram for 1-, 3-, and 5-year survival predictions. Our model demonstrated better clinical validity in both TCGA-LIHC and GSE14520 cohorts. Additionally, events related to biological enzyme activity, biological metabolic processes, or the cell cycle were associated with the prognostic risk of ER. Furthermore, two HCC prognosis-associated tumour clusters were identified by ER hub gene-based consensus clustering. Our findings indicated a link between ER prognostic signature-related high/low risk and tumour infiltration levels of several immune cells, such as “macrophages M2/M0” and “regulatory T cells (Tregs).” Overall, we developed a novel ER-related clinical prognostic model for HCC patients.

Up-Regulation of TRIM32 Associated With the Poor Prognosis of Acute Myeloid Leukemia by Integrated Bioinformatics Analysis With External Validation

Background

Acute myeloid leukemia (AML) is a malignant and molecularly heterogeneous disease. It is essential to clarify the molecular mechanisms of AML and develop targeted treatment strategies to improve patient prognosis.

Methods

AML mRNA expression data and survival status were extracted from TCGA and GEO databases (GSE37642, GSE76009, GSE16432, GSE12417, GSE71014). Weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis were performed. Functional enrichment analysis and protein-protein interaction (PPI) network were used to screen out hub genes. In addition, we validated the expression levels of hub genes as well as the prognostic value and externally validated TRIM32 with clinical data from our center. AML cell lines transfected with TRIM32 shRNA were also established to detect the proliferation in vitro.

Results

A total of 2192 AML patients from TCGA and GEO datasets were included in this study and 20 differentially co-expressed genes were screened by WGCNA and differential gene expression analysis methods. These genes were mainly enriched in phospholipid metabolic processes (biological processes, BP), secretory granule membranes (cellular components, CC), and protein serine/threonine kinase activity (molecular functions, MF). In addition, the protein-protein interaction (PPI) network contains 15 nodes and 15 edges and 10 hub genes (TLE1, GLI2, HDAC9, MICALL2, DOCK1, PDPN, RAB27B, SIX3, TRIM32 and TBX1) were identified. The expression of 10 central genes, except TLE1, was associated with survival status in AML patients (p<0.05). High expression of TRIM32 was tightly associated with poor relapse-free survival (RFS) and overall survival (OS) in AML patients, which was verified in the bone marrow samples from our center. In vitro, knockdown of TRIM32 can inhibit the proliferation of AML cell lines.

Conclusion

TRIM32 was associated with the progression and prognosis of AML patients and could be a potential therapeutic target and biomarker for AML in the future.

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Background

Dermatomyositis is an autoimmune disease characterized by damage to the skin and muscles. CD4⁺ T cells are of crucial importance in the occurrence and development of dermatomyositis (DM). However, there are few bioinformatics studies on potential pathogenic genes and immune cell infiltration of DM. Therefore, this study intended to explore CD4⁺ T-cell infiltration–associated key genes in DM and construct a new model to predict the level of CD4⁺ T-cell infiltration in DM.

Methods

GSE46239, GSE142807, GSE1551, and GSE193276 datasets were downloaded. The WGCNA and CIBERSORT algorithms were performed to identify the most correlated gene module with CD4⁺ T cells. Matascape was used for GO enrichment and KEGG pathway analysis of the key gene module. LASSO regression analysis was used to identify the key genes and construct the prediction model. The correlation between the key genes and CD4⁺ T-cell infiltration was investigated. GSEA was performed to research the underlying signaling pathways of the key genes. The key gene-correlated transcription factors were identified through the RcisTarget and Gene-motif rankings databases. The miRcode and DIANA-LncBase databases were used to build the lncRNA-miRNA-mRNA network.

Results

In the brown module, 5 key genes (chromosome 1 open reading frame 106 (C1orf106), component of oligomeric Golgi complex 8 (COG8), envoplakin (EVPL), GTPases of immunity-associated protein family member 6 (GIMAP6), and interferon-alpha inducible protein 6 (IFI6)) highly associated with CD4⁺ T-cell infiltration were identified. The prediction model was constructed and showed better predictive performance in the training set, and this satisfactory model performance was validated in another skin biopsy dataset and a muscle biopsy dataset. The expression levels of the key genes promoted the CD4⁺ T-cell infiltration. GSEA results revealed that the key genes were remarkably enriched in many immunity-associated pathways, such as JAK/STAT signaling pathway. The cisbp_M2205, transcription factor-binding site, was enriched in C1orf106, EVPL, and IF16. Finally, 3,835 lncRNAs and 52 miRNAs significantly correlated with key genes were used to build a ceRNA network.

Conclusion

The C1orf106, COG8, EVPL, GIMAP6, and IFI6 genes are associated with CD4⁺ T-cell infiltration. The prediction model constructed based on the 5 key genes may better predict the level of CD4⁺ T-cell infiltration in damaged muscle and lesional skin of DM. These key genes could be recognized as potential biomarkers and immunotherapeutic targets of DM.

RNA Therapeutic Options to Manage Aberrant Signaling Pathways in Hepatocellular Carcinoma: Dream or Reality?

Despite the early promise of RNA therapeutics as a magic bullet to modulate aberrant signaling in cancer, this field remains a work-in-progress. Nevertheless, RNA therapeutics is now a reality for the treatment of viral diseases (COVID-19) and offers great promise for cancer. This review paper specifically investigates RNAi as a therapeutic option for HCC and discusses a range of RNAi technology including anti-sense oligonucleotides (ASOs), Aptamers, small interfering RNA (siRNA), ribozymes, riboswitches and CRISPR/Cas9 technology. The use of these RNAi based interventions is specifically outlined in three primary strategies, namely, repressing angiogenesis, the suppression of cell proliferation and the promotion of apoptosis. We also discuss some of the inherent chemical and delivery problems, as well as targeting issues and immunogenic reaction to RNAi interventions.

A Prognostic Gene Signature for Hepatocellular Carcinoma

Hepatocellular carcinoma is the third most common cause of cancer-related deaths in China and immune-based therapy can improve patient outcomes. In this study, we investigated the relationship between immunity-associated genes and hepatocellular carcinoma from the prognostic perspective. The data downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) and the Gene Expression Omnibus (GEO) was screened for gene mutation frequency using the maftools package. Immunity-associated eight-gene signature with strong prognostic ability was constructed and proved as an independent predictor of the patient outcome in LIHC. Seven genes in the immune-related eight-gene signature were strongly associated with the infiltration of M0 macrophages, resting mast cells, and regulatory T cells. Our research may provide clinicians with a quantitative method to predict the prognosis of patients with liver cancer, which can assist in the selection of the optimal treatment plan.

Immune-related biomarkers shared by inflammatory bowel disease and liver cancer

It has been indicated that there is an association between inflammatory bowel disease (IBD) and hepatocellular carcinoma (HCC). However, the molecular mechanism underlying the risk of developing HCC among patients with IBD is not well understood. The current study aimed to identify shared genes and potential pathways and regulators between IBD and HCC using a system biology approach. By performing the different gene expression analyses, we identified 871 common differentially expressed genes (DEGs) between IBD and HCC. Of these, 112 genes overlapped with immune genes were subjected to subsequent bioinformatics analyses. The results revealed four hub genes (CXCL2, MMP9, SPP1 and SRC) and several other key regulators including six transcription factors (FOXC1, FOXL1, GATA2, YY1, ZNF354C and TP53) and five microRNAs (miR-124-3p, miR-34a-5p, miR-1-3p, miR-7-5p and miR-99b-5p) for these disease networks. Protein-drug interaction analysis discovered the interaction of the hub genes with 46 SRC-related and 11 MMP9- related drugs that may have a therapeutic effect on IBD and HCC. In conclusion, this study sheds light on the potential connecting mechanisms of HCC and IBD.

RAGE is a potential biomarker implicated in immune infiltrates and cellular senescence in lung adenocarcinoma

Background

Receptor for Advanced Glycation End‐products (RAGE) is an oncogene abnormally expressed in various cancers. However, the clinical value of RAGE and the biological role of RAGE in lung cancer have not been fully investigated.

Methods

We compared the RAGE expression using several public databases. The relationship between RAGE expression and clinicopathological variables was assessed. The R software package was used to carry out enrichment analyses of RAGE co‐expression and gene set enrichment analysis (GSEA). Additionally, we used the TIMER database to assess the association between immune infiltration and RAGE expression. The correlation between RAGE expression and senescence biomarkers in lung adenocarcinoma was analyzed using the TCGA database.

Results

Our findings indicated that the expression of RAGE was downregulated in lung adenocarcinoma, and down‐regulation of RAGE was related to poor overall survival and disease‐free survival. Functional enrichment analysis indicated that RAGE co‐expression genes were mainly associated with neutrophil activation involved in immune response, neutrophil degranulation, and regulation of leukocyte‐mediated immunity. Correlation analysis revealed that RAGE expression was closely related to the purity of the tumor and immune infiltration. GSEA indicated that the RAGE‐related differential genes were mainly enriched in senescence‐related pathways. Besides, the RAGE expression was significantly associated with senescence‐related genes.

Conclusion

Down‐regulation of RAGE expression was associated with poor prognosis, as well as defective immune infiltration and cellular senescence in lung adenocarcinoma.

Identification of Alternative Splicing Events Associated with Paratuberculosis in Dairy Cattle Using Multi-Tissue RNA Sequencing Data

Paratuberculosis is a major endemic disease caused by Mycobacterium avium subspecies paratuberculosis (MAP) infection and leads to huge economic loss in the dairy sector worldwide. Alternative splicing (AS) events, playing indispensable regulatory roles in many protein functions and biological pathways, are shown to be associated with complex traits and diseases. In this study, by integrating the RNA sequencing (RNA-seq) data of 24 samples from three tissues (peripheral blood, jejunum and salivary gland) of Holstein cows, we obtained 2,706,541,696 uniquely mapped reads in total that represented 12,870 expressed genes, and detected 4285 differentially expressed genes (DEGs) between MAP-infected and healthy cows (p < 0.05). Of them, 92 differentially expressed splicing factors (DESFs) were included. Further, 119, 150 and 68 differential alternative splicing (DAS) events between MAP-infected and healthy cows were identified in peripheral blood, jejunum and salivary glands, respectively. Of note, six DAS events were highly and significantly correlated with the DESFs (R2 > 0.9; p < 0.01), and their corresponding genes (COPI coat complex subunit gamma 2gene (COPG2), kinesin family member 2C gene (KIF2C), exocyst complex component 7 (EXOC7), Rab9 effector protein with kelch motifs gene (RABEPK), deoxyribonuclease 1 gene (DNASE1) and early endosome antigen 1gene (EEA1)) were significantly enriched in immune response such as vesicle-mediated transport, regulation of acute inflammatory response and tuberculosis through gene ontology (GO) and KEGG analysis. KS test showed that the DAS events in the EXOC7 and KIF2C genes indeed displayed differences between MAP-infected cows and healthy cows. The DAS in EXOC7 might produce a new protein sequence with lack of 23 amino acids, and the DAS in KIF2C induced a stop codon of premature occurrence and resulted in a lack of functional domain. In summary, this study identified the DAS events and corresponding genes related to MAP-infection base on the RNA-seq data from multiple tissues of Holstein cows, providing novel insights into the regulatory mechanisms underpinning paratuberculosis in dairy cattle.

Evolutionary Dynamics and Molecular Mechanisms of HORMA Domain Protein Signaling

Controlled assembly and disassembly of multi-protein complexes is central to cellular signaling. Proteins of the widespread and functionally diverse HORMA family nucleate assembly of signaling complexes by binding short peptide motifs through a distinctive safety-belt mechanism. HORMA proteins are now understood as key signaling proteins across kingdoms, serving as infection sensors in a bacterial immune system and playing central roles in eukaryotic cell cycle, genome stability, sexual reproduction, and cellular homeostasis pathways. Here, we describe how HORMA proteins' unique ability to adopt multiple conformational states underlies their functions in these diverse contexts. We also outline how a dedicated AAA+ ATPase regulator, Pch2/TRIP13, manipulates HORMA proteins' conformational states to activate or inactivate signaling in different cellular contexts. The emergence of Pch2/TRIP13 as a lynchpin for HORMA protein action in multiple genome-maintenance pathways accounts for its frequent misregulation in human cancers and highlights TRIP13 as a novel therapeutic target. Expected final online publication date for the Annual Review of Biochemistry, Volume 91 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Liver cancer is one of the most common cancers and the top leading cause of cancer death globally. However, the molecular mechanisms of liver tumorigenesis and progression remain unclear. In the current study, we investigated the hub genes and the potential molecular pathways through which these genes contribute to liver cancer onset and development. The weighted gene co-expression network analysis (WCGNA) was performed on the main data attained from the GEO (Gene Expression Omnibus) database. The Cancer Genome Atlas (TCGA) dataset was used to evaluate the association between prognosis and these hub genes. The expression of genes from the black module was found to be significantly related to liver cancer. Based on the results of protein-protein interaction, gene co-expression network, and survival analyses, DNA topoisomerase II alpha (TOP2A), ribonucleotide reductase regulatory subunit M2 (RRM2), never in mitosis-related kinase 2 (NEK2), cyclin-dependent kinase 1 (CDK1), and cyclin B1 (CCNB1) were identified as the hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the differentially expressed genes (DEGs) were enriched in the immune-associated pathways. These hub genes were further screened and validated using statistical and functional analyses. Additionally, the TOP2A, RRM2, NEK2, CDK1, and CCNB1 proteins were overexpressed in tumor liver tissues as compared to normal liver tissues according to the Human Protein Atlas database and previous studies. Our results suggest the potential use of TOP2A, RRM2,&nbsp;NEK2, CDK1, and CCNB1 as prognostic biomarkers in liver cancer.

Identification of Three Key Genes Associated with Hepatocellular Carcinoma Progression Based on Co-expression Analysis

Hepatocellular carcinoma (HCC) is the fifth most common cancer and one of the leading causes of cancer-related death in the world. Due to the recurrence of HCC, its survival rate is still low. Therefore, it is vital to seek prognostic biomarkers for HCC. In this study, differential analysis was conducted on gene expression data in The Cancer Genome Atlas -LIHC, and 4482 differentially expressed genes in tumor tissue were selected. Then, weighted gene co-expression network analysis was used to analyze the co-expression of the gained differential genes. By module-trait correlation analysis, the turquoise gene module that was significantly related to tumor grade, pathologic_T stage, and clinical stage was identified. Thereafter, enrichment analysis of genes in this module uncovered that the genes were mainly enriched in the signaling pathways involved in spliceosome and cell cycle. After that, through correlation analysis, 18 hub genes highly correlated with tumor grade, clinical stage, pathologic_T stage, and the turquoise module were selected. Meanwhile, protein-protein interaction (PPI) network was constructed by using genes in the module. Finally, three key genes, heterogeneous nuclear ribonucleoprotein L, serrate RNA effector molecule, and cyclin B2, were identified by intersecting the top 30 genes with the highest connectivity in PPI network and the previously obtained 18 hub genes in the turquoise module. Further survival analysis revealed that high expression of the three key genes predicted poor prognosis of HCC. These results indicated the direction for further research on clinical diagnosis and prognostic biomarkers of HCC.

Dauricine inhibits human pancreatic carcinoma cell proliferation through regulating miRNAs

Pancreatic cancer is one of the most malignant digestive tract tumors with the worst prognosis. Dauricine (Dau) can inhibit the proliferation of the pancreatic cancer cell line, and has the potential to be used as an adjuvant drug against pancreatic cancer; however, the working mechanism of Dau has not been elucidated. To unravel the effects and mechanisms of Dau on proteins and metabolic pathways, we evaluated the mRNA and microRNA expression in BxPC3 cells treated with Dau. The differences in the gene expression were compared using principal component analysis using mRNA and miRNA data to detect and analyze the sample discrimination. 187 miRNA and 907 mRNA that were significantly differentially expressed were identified using Python programming. On comparing genes and miRNAs in the DISEASES database, 79 known miRNA and 47 mRNA were found to be affected by Dau. The up-regulated and down-regulated genes were annotated with GO biological processes to determine the functional effect. Interactions between mRNA and mRNA were analyzed using the STRING database and the miRBase database was queried to obtain experimentally verified interactions between miRNA and mRNA as edges of miRNA and mRNA in the network. Finally, 413 sites and 2125 sides of the network were obtained, including 1 up-regulated and 18 down-regulated miRNAs. The expression of 19 miRNAs was identified by qPCR. The analysis of the protein-protein interaction network, using the Molecular Complex Detection (MCODE) plug-in of cytoscape, helped in identifying 12 important sub-networks. Most subnets are indirectly or directly related to specific miRNAs. This study provides evidence for the anticancer effect of Dau as a potential anticancer compound.

DTYMK promote hepatocellular carcinoma proliferation by regulating cell cycle

Overexpression of DTYMK is related with tumorigenesis and progression in several human tumors. However, the role of upregulated DTYMK in hepatocellular carcinoma (HCC) patients still remains unclear. In this study, the DTYMK expression in HCC tumors was evaluated in three GEO series (GSE14520, GSE54236, GSE63898), TCGA-LIHC, and ICGC-IRLR-JP cohorts. Survival analysis of DTYMK based on TCGA-LIHC and ICGC-LIRI-JP cohorts was conducted. We found that DTYMK was dramatically upregulated in tumor tissue compared with that in adjacent liver tissue. Kaplan-Meier analysis revealed that high expression of DTYMK in HCC patients' tumor tissue was significantly corresponded to worse overall survival (OS) (P < 0.05). Further analysis showed that overexpressing DTYMK led to poor relapse free survival (RFS) and disease-specific survival (DSS) (all P < 0.05). In conclusion, DTYMK is upregulated in tumors and correlated with poor prognosis in HCC patients. In our report, DTYMK is higher expression in HCC cancer tissue and cell line than tumor adjacent tissue and normal liver cell line. Knocking down DTYMK can inhabit tumor cell proliferation by interfering cell cycle, whereas overexpression of DTYMK can promote tumor cell proliferation. These findings indicate that upregulation of DTYMK enhances tumor growth and proliferation by promoting cell cycle.

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.

Integrated Analysis of Nine Prognostic RNA-Binding Proteins in Soft Tissue Sarcoma

RNA-binding proteins (RBPs) have been shown to be dysregulated in cancer transcription and translation, but few studies have investigated their mechanism of action in soft tissue sarcoma (STS). Here, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to identify differentially expressed RBPs in STS and normal tissues. Through a series of biological information analyses, 329 differentially expressed RBPs were identified. Functional enrichment analysis showed that differentially expressed RBPs were mainly involved in RNA transport, RNA splicing, mRNA monitoring pathways, ribosome biogenesis and translation regulation. Through Cox regression analyses, 9 RBPs (BYSL, IGF2BP3, DNMT3B, TERT, CD3EAP, SRSF12, TLR7, TRIM21 and MEX3A) were all up-regulated in STS as prognosis-related genes, and a prognostic model was established. The model calculated a risk score based on the expression of 9 hub RBPs. The risk score could be used for risk stratification of patients and had a high prognostic value based on the receiver operating characteristic (ROC) curve. We also established a nomogram containing risk scores and 9 key RBPs to predict the 1-year, 3-year, and 5-year survival rates of patients in STS. Afterwards, methylation analysis showed significant changes in the methylation degree of BYSL, CD3EAP and MEX2A. Furthermore, the expression of 9 hub RBPs was closely related to immune infiltration rather than tumor purity. Based on the above studies, these findings may provide new insights into the pathogenesis of STS and will provide candidate biomarkers for the prognosis of STS.

A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear.

METHODS

Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset.

RESULTS

A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours.

CONCLUSION

Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

Prognostic biomarkers and therapeutic targets in oral squamous cell carcinoma: a study based on cross-database analysis

Background

Oral squamous cell carcinoma (OSCC) is a malignant cancer, the survival rate of patients is disappointing. Therefore, it is necessary to identify the driven-genes and prognostic biomarkers in OSCC.

Methods

Four Gene Expression Omnibus (GEO) datasets were integratedly analyzed using bioinformatics approaches, including identification of differentially expressed genes (DEGs), GO and KEGG analysis, construction of protein-protein interaction (PPI) network, selection of hub genes, analysis of prognostic information and genetic alterations of hub genes. ONCOMINE, The Cancer Genome Atlas (TCGA) and Human Protein Atlas databases were used to evaluate the expression and prognostic value of hub genes. Tumor immunity was assessed to investigate the functions of hub genes. Finally, Cox regression model was performed to construct a multiple-gene prognostic signature.

Results

Totally 261 genes were found to be dysregulated. 10 genes were considered to be the hub genes. The Kaplan-Meier analysis showed that upregulated SPP1, FN1, CXCL8, BIRC5, PLAUR, and AURKA were related to poor outcomes in OSCC patients. FOXM1 and TPX2 were considered as the potential immunotherapeutic targets with future clinical significance. Moreover, we constructed a nine-gene signature (TEX101, DSG2, SCG5, ADA, BOC, SCARA5, FST, SOCS1, and STC2), which can be utilized to predict prognosis of OSCC patients effectively.

Conclusion

These findings may provide new clues for exploring the molecular mechanisms and targeted therapy in OSCC. The hub genes and risk gene signature are helpful to the personalized treatment and prognostic judgement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00181-1.

Identification of Biomarkers Related to Immune Cell Infiltration in Hepatocellular Carcinoma Using Gene Co-Expression Network

Hepatocellular carcinoma (HCC) is a common cancer with poor prognosis. Due to the lack of effective biomarkers and its complex immune microenvironment, the effects of current HCC therapies are not ideal. In this study, we used the GSE57957 microarray data from Gene Expression Omnibus database to construct a co-expression network. The weighted gene co-expression network analysis and CIBERSORT algorithm, which quantifies cellular composition of immune cells, were used to identify modules related to immune cells. Four hub genes (EFTUD2, GAPDH, NOP56, PA2G4) were identified by co-expression network and protein-protein interactions network analysis. We examined these genes in TCGA database, and found that the four hub genes were highly expressed in tumor tissues in multiple HCC groups, and the expression levels were significantly correlated with patient survival time, pathological stage and tumor progression. On the other hand, methylation analysis showed that the up-regulation of EFTUD2, GAPDH, NOP56 might be due to the hypomethylation status of their promoters. Next, we investigated the correlations between the expression levels of four hub genes and tumor immune infiltration using Tumor Immune Estimation Resource (TIMER). Gene set variation analysis suggested that the four hub genes were associated with numerous pathways that affect tumor progression or immune microenvironment. Overall, our results showed that the four hub genes were closely related to tumor prognosis, and may serve as targets for treatment and diagnosis of HCC. In addition, the associations between these genes and immune infiltration enhanced our understanding of tumor immune environment and provided new directions for the development of drugs and the monitoring of tumor immune status.

A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma

To further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expression network (GCN) based on gene expression data firstly; Then the Newman algorithm was used to initially identify gene modules based on the topology of GCN, and the number of clusters and clustering centers were determined; Finally the number of clusters and clustering centers were input into the K-means algorithm framework, and the secondary clustering was performed based on the gene expression profile to obtain the final gene modules. The algorithm took into account the role of modularity in the clustering process, and could find the optimal membership module for each gene through multiple iterations. Experimental results showed that the algorithm proposed in this paper had the best performance in error rate, biological significance and CNN classification indicators (Precision, Recall and F-score). The gene module obtained by GCNA-Kpca was used for the task of key gene identification, and these key genes had the highest prognostic significance. Moreover, GCNA-Kpca algorithm was used to identify 10 key genes in hepatocellular carcinoma (HCC): CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4, NOP58, AURKA, PCNA, and FEN1. According to the validation, it was reasonable to speculate that these 10 key genes could be biomarkers for HCC. And NOP56 and NOP58 are key genes for HCC that we discovered for the first time.

Identification of an autophagy-related gene signature predicting overall survival for hepatocellular carcinoma

The poor prognosis of hepatocellular carcinoma (HCC) calls for the development of accurate prognostic models. The growing number of studies indicating a correlation between autophagy activity and HCC indicates there is a commitment to finding solutions for the prognosis of HCC from the perspective of autophagy. We used a cohort in The Cancer Genome Atlas (TCGA) to evaluate the expression of autophagy-related genes in 371 HCC samples using univariate Cox and lasso Cox regression analysis, and the prognostic features were identified. A prognostic model was established by combining the expression of selected genes with the multivariate Cox regression coefficient of each gene. Eight autophagy-related genes were selected as prognostic features of HCC. We established the HCC prognostic risk model in TCGA dataset using these identified prognostic genes. The model's stability was confirmed in two independent verification sets (GSE14520 and GSE36376). The model had a good predictive power for the overall survival (OS) of HCC (hazard ratio = 2.32, 95% confidence interval = 1.76-3.05, P<0.001). Moreover, the risk score computed by the model did not depend on other clinical parameters. Finally, the applicability of the model was demonstrated through a nomogram (C-index = 0.701). In the present study, we established an autophagy-related risk model having a high prediction accuracy for OS in HCC. Our findings will contribute to the definition of prognosis and establishment of personalized therapy for HCC patients.

An Integrative Analysis Reveals the Potential Mechanism between Herbal Medicine Yinchen and Immunoregulation in Hepatocellular Carcinoma

Aims. Abundant evidences in traditional Chinese medicine (TCM) supported the therapeutic value of herbal medicine Yinchen in hepatocellular carcinoma (HCC), but the underlying mechanism remains to be investigated. Main Methods. The intersection of immune gene set, module genes, HCC-associated genes, and target genes of Yinchen was employed for further analyses. The module genes were identified by weighted gene coexpression network analysis, and the other three gene sets were obtained from public databases. Subsequently, we further explored the clinical value and immunoregulation of the hub gene of intersection. The relevant pathways related to hub gene expression were investigated by gene set enrichment analysis. Finally, the interaction of active compounds and target genes was validated by molecular docking. Key Findings. Thirteen active compounds and 90 target genes of Yinchen were included. After constructing the network among Yinchen, target genes, and HCC, BIRC5 was identified as the hub gene. Significant difference was found between the high-expressed group and the low-expressed group in survival and stage. Different immune subtypes also presented significant difference in BIRC5 expression. Moreover, NK cell and T cell (CD4+ effector memory and CD4+ memory resting) were negatively correlated with BIRC5 expression, while CTLA4 and LAG3 were positively correlated. The results of molecular docking further validated a good binding activity of quercetin-BIRC5 interaction. Significance. In summary, our research identified for the first time a novel underlying association among herbal medicine Yinchen, BIRC5, immunotherapy, and HCC. We speculated that Yinchen may target the immune checkpoints (CTLA4 and LAG3) and activate the immune cells by suppressing BIRC5.

Identification of Hub Genes Associated With Immune Infiltration and Predict Prognosis in Hepatocellular Carcinoma via Bioinformatics Approaches

Aims

In the cancer-related research field, there is currently a major need for a greater number of valuable biomarkers to predict the prognosis of hepatocellular carcinoma (HCC). In this study, we aimed to screen hub genes related to immune cell infiltration and explore their prognostic value for HCC.

Methods

We analyzed five datasets (GSE46408, GSE57957, GSE74656, GSE76427, and GSE87630) from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes; then, the hub genes were identified. Functional enrichment of the genes was performed on the Metascape website. Next, the expression of these hub genes was validated in several databases, including Oncomine, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Human Protein Atlas. We explored the correlations between the hub genes and infiltrated immune cells in the TIMER2.0 database. The survival curves were generated in GEPIA2, and the univariate and multivariate Cox regression analyses were performed using TIMER2.0.

Results

The top ten hub genes [DNA topoisomerase II alpha (TOP2A), cyclin B2 (CCNB2), protein regulator of cytokinesis 1 (PRC1), Rac GTPase-activating protein 1 (RACGAP1), aurora kinase A (AURKA), cyclin-dependent kinase inhibitor 3 (CDKN3), nucleolar and spindle-associated protein 1 (NUSAP1), cell division cycle-associated 5 (CDCA5), abnormal spindle microtubule assembly (ASPM), and non-SMC condensin I complex subunit G (NCAPG)] were identified in subsequent analysis. These genes are most markedly enriched in cell division, suggesting their close association with tumorigenesis. Multi-database analyses validated that the hub genes were upregulated in HCC tissues. All hub genes positively correlated with several types of immune infiltration, including B cells, CD4⁺ T cells, macrophages, and dendritic cells. Furthermore, these hub genes served as independent prognostic factors, and the expression of these hub genes combing with the macrophage levels could help predict an unfavorable prognosis of HCC.

Conclusion

In sum, these hub genes (TOP2A, CCNB2, PRC1, RACGAP1, AURKA, CDKN3, NUSAP1, CDCA5, ASPM, and NCAPG) may be pivotal markers for prognostic prediction as well as potentially work as targets for immune-based intervention strategies in HCC.

BIRC3 and BIRC5: multi-faceted inhibitors in cancer

BACKGROUND

The evasion from apoptosis is a common strategy adopted by most tumors, and inhibitors of apoptosis proteins (IAPs) are among the most studied molecular and therapeutic targets. BIRC3 (cellular IAP2) and BIRC5 (survivin) are two of the eight members of the human IAPs family. This family is characterized by the presence of the baculoviral IAP repeat (BIR) domains, involved in protein-protein interactions. In addition to the BIR domains, IAPs also contain other important domains like the C-terminal ubiquitin-conjugating (UBC) domain, the caspase recruitment (CARD) domain and the C-terminal Ring zinc-finger (RING) domain.

MAIN BODY

BIRC3 and BIRC5 have been characterized in some solid and hematological tumors and are therapeutic targets for the family of drugs called "Smac mimetics". Many evidences point to the pro-survival and antiapoptotic role of BIRC3 in cancer cells, however, not all the data are consistent and the resulting picture is heterogeneous. For instance, BIRC3 genetic inactivation due to deletions or point mutations is consistently associated to shorter progression free survival and poor prognosis in chronic lymphocytic leukemia patients. BIRC3 inactivation has also been associated to chemoimmunotherapy resistance. On the contrary, the progression from low grade gliomas to high grade gliomas is accompanied by BIRC3 expression increase, which bears relevant prognostic consequences. Due to the relationship between BIRC3, MAP3K14 and the non-canonical NF-kB pathway, BIRC3 inactivation bears consequences also on the tumor cells relying on NF-kB pathway to survive. BIRC5, on the contrary, is commonly considered an anti-apoptotic molecule, promoting cell division and tumor progression and it is widely regarded as potential therapeutic target.

CONCLUSIONS

The present manuscript collects and reviews the most recent literature concerning the role played by BIRC3 and BIRC5 in cancer cells, providing useful information for the choice of the best therapeutic targets.