PRC1 and RACGAP1 are Diagnostic Biomarkers of Early HCC and PRC1 Drives Self-Renewal of Liver Cancer Stem Cells

Regulation of histone H3K27 methylation in inflammation and cancer

Inflammation is a multifaceted defense mechanism of the immune system against infection. Chronic inflammation is intricately linked to all stages of tumorigenesis and is therefore associated with an elevated risk of developing serious cancers. Epigenetic mechanisms have the capacity to trigger inflammation as well as facilitate tumor development and transformation within an inflammatory context. They achieve this by dynamically modulating the expression of both pro-inflammatory and anti-inflammatory cytokines, which in turn sustains chronic inflammation. The aberrant epigenetic landscape reconfigures the transcriptional programs of inflammatory and oncogenic genes. This reconfiguration is pivotal in dictating the biological functions of both tumor cells and immune cells. Aberrant histone H3 lysine 27 site (H3K27) methylation has been shown to be involved in biological behaviors such as inflammation development, tumor progression, and immune response. The establishment and maintenance of this repressive epigenetic mark is dependent on the involvement of the responsible histone modifying enzymes enhancer of zeste homologue 2 (EZH2), jumonji domain containing 3 (JMJD3) and ubiquitously transcribed tetratricopeptide repeat gene X (UTX) as well as multiple cofactors. In addition, specific pharmacological agents have been shown to modulate H3K27 methylation levels, thereby modulating inflammation and carcinogenesis. This review comprehensively summarises the current characteristics and clinical significance of epigenetic regulation of H3K27 methylation in the context of inflammatory response and tumor progression.

Strategies for discovering novel hepatocellular carcinoma biomarkers

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge due to its high mortality rate and late-stage diagnosis. The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes. This review provides a comprehensive overview of current and emerging biomarkers for HCC, including alpha-fetoprotein, des-gamma-carboxy prothrombin, glypican-3, Golgi protein 73, osteopontin, and microRNAs. Despite advancements, the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages. The review emphasizes the importance of integrating multi-omics approaches, combining genomics, proteomics, and metabolomics, to develop more robust biomarker panels. Such integrative methods have the potential to capture the complex molecular landscape of HCC, offering insights into disease mechanisms and identifying targets for personalized therapies. The significance of large-scale validation studies, collaboration between research institutions and clinical settings, and consideration of regulatory pathways for clinical implementation is also discussed. In conclusion, while substantial progress has been made in biomarker discovery, continued research and innovation are essential to address the remaining challenges. The successful translation of these discoveries into clinical practice will require rigorous validation, standardization of protocols, and cross-disciplinary collaboration. By advancing the development and application of novel biomarkers, we can improve the early detection and management of HCC, ultimately enhancing patient survival and quality of life.

Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common differentially expressed genes (DEGs) across platforms. Consequently, these studies may missed some important genes in the investigation of HCC. To solve these problems, we have taken datasets from multiple platforms and designed a statistical and machine learning-based system to determine platform-independent key genes (KGs) for HCC patients. DEGs were determined from each dataset using limma. Individual combined DEGs (icDEGs) were identified from each platform and then determined grand combined DEGs (gcDEGs) from icDEGs of all platforms. Differentially expressed discriminative genes (DEDGs) was determined based on the classification accuracy using Support vector machine. We constructed PPI network on DEDGs and identified hub genes using MCC. This study determined the optimal modules using the MCODE scores of the PPI network and selected their gene combinations. We combined all genes, obtained from previous studies to form metadata, known as meta-hub genes. Finally, six KGs (CDC20, TOP2A, CENPF, DLGAP5, UBE2C, and RACGAP1) were selected by intersecting the overlapping hub genes, meta-hub genes, and hub module genes. The discriminative power of six KGs and their prognostic potentiality were evaluated using AUC and survival analysis.

The Expression Regulation and Cancer-Promoting Roles of RACGAP1

RACGAP1 is a Rho-GTPase-activating protein originally discovered in male germ cells to inactivate Rac, RhoA and Cdc42 from the GTP-bound form to the GDP-bound form. GAP has traditionally been known as a tumor suppressor. However, studies increasingly suggest that overexpressed RACGAP1 activates Rac and RhoA in multiple cancers to mediate downstream oncogene overexpression by assisting in the nuclear translocation of signaling molecules and to promote cytokinesis by regulating the cytoskeleton or serving as a component of the central spindle. Contradictorily, it was also reported that RACGAP1 in gastric cancer could inactivate Rac and RhoA. In addition, studies have revealed that RACGAP1 can be a biomarker for prognosis, and its role in reducing doxorubicin sensitivity poses difficulties for treatment, while the current drug targets mainly focus on its downstream molecule. This article mainly reviews the expression regulation of RACGAP1 and its cancer-promoting functions through oncogene expression mediation and Rho-GTPase activation.

Shared and specific competing endogenous RNAs network mining in four digestive system tumors

Background

Digestive system malignancies, including esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), liver hepatocellular carcinoma (LIHC), and colon adenocarcinoma (COAD), pose significant global health challenges. Identifying shared and distinct regulatory mechanisms across these cancers can lead to improved therapies. This study aims to construct and compare competing endogenous RNA (ceRNA) networks across ESCA, STAD, LIHC, and COAD to identify RNA biomarkers that could serve as precision therapeutic targets to enhance clinical outcomes and advance personalized cancer care.

Methods

Clinical and transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed to predict differentially expressed RNAs using the edgeR package. The ceRNA networks were constructed using the miRcode and ENCORI databases. Functional enrichment analysis and prognostic RNA screening were performed with ConsensusPathDB and univariate Cox regression analysis.

Results

we identified 6, 88, 55, and 41 RNA biomarkers in ESCA, STAD, LIHC, and COAD, respectively. Network analysis revealed shared and specific elements, with shared nodes enriched in cell cycle and mitotic processes. Several biomarkers, including HMGB3 and RGS16 (ESCA), COL4A1 and COL6A3 (STAD), CDCA5 and CDCA8 (LIHC), and LIMK1 and OSBPL3 (COAD), were consistent with prior studies, while novel biomarkers, such as C3P1 (ESCA), P2RY6 (STAD), and N4BP2L1 and PPP1R3B (LIHC), were discovered. Based on RNA correlation analysis, 1, 23, and 2 potential ceRNA regulatory axes were identified in STAD (PVT1/miR-490-3p/HMGA2), LIHC (DLX6-AS1/miR-139-5p/TOP2A, etc.), and COAD (STRCP1 & LINC00488/miR-142-3p/GAB1), respectively.

Conclusions

This study advances the understanding of ceRNA networks in digestive cancers, highlighting RNA biomarkers with potential as therapeutic targets for personalized treatment strategies.

An Integrated Framework to Identify Prognostic Biomarkers and Novel Therapeutic Targets in Hepatocellular Carcinoma-Based Disabilities

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors globally, significantly affecting liver functions, thus necessitating the identification of biomarkers and effective therapeutics to improve HCC-based disabilities. This study aimed to identify prognostic biomarkers, signaling cascades, and candidate drugs for the treatment of HCC through integrated bioinformatics approaches such as functional enrichment analysis, survival analysis, molecular docking, and simulation. Differential expression and functional enrichment analyses revealed 176 common differentially expressed genes from two microarray datasets, GSE29721 and GSE49515, significantly involved in HCC development and progression. Topological analyses revealed 12 hub genes exhibiting elevated expression in patients with higher tumor stages and grades. Survival analyses indicated that 11 hub genes (CCNB1, AURKA, RACGAP1, CEP55, SMC4, RRM2, PRC1, CKAP2, SMC2, UHRF1, and FANCI) and three transcription factors (E2F1, CREB1, and NFYA) are strongly linked to poor patient survival. Finally, molecular docking and simulation identified seven candidate drugs with stable complexes to their target proteins: tozasertib (-9.8 kcal/mol), tamatinib (-9.6 kcal/mol), ilorasertib (-9.5 kcal/mol), hesperidin (-9.5 kcal/mol), PF-562271 (-9.3 kcal/mol), coumestrol (-8.4 kcal/mol), and clofarabine (-7.7 kcal/mol). These findings suggest that the identified hub genes and TFs could serve as valuable prognostic biomarkers and therapeutic targets for HCC-based disabilities.

Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters

BACKGROUND

Cuproptosis, a recently identified type of programmed cell death triggered by copper, has mechanisms in Wilms tumor (WT) that are not yet fully understood. This research focuses on examining the link between WT and Cuproptosis-related genes (CRGs), with the goal of developing a predictive model for WT.

METHODS

Four gene expression datasets related to WT were sourced from the GEO database. Subsequently, expression profiles of CRGs were extracted for differential analysis and immune infiltration studies. Utilizing 105 WT samples, clusters related to Cuproptosis were identified. This involved analyzing associated immune cell infiltration and conducting functional enrichment analysis. Disease-characteristic genes were pinpointed using weighted gene co-expression network analysis. Finally, the WT risk prediction model was constructed by four machine learning methods: random forest, support vector machine (SVM), generalized linear and extreme gradient strength model. The best-performing machine learning model was chosen, and a nomogram was created. The effectiveness of this predictive model was validated using methods such as the calibration curve, decision curve analysis, and by appiying it to the TARGET-GTEx dataset.

RESULTS

Thirteen differentially expressed Cuproptosis-related genes were identified. The infiltration level of CD8 + T cells in WT children was lower than that in Normal tissue (NT) children, and the level of M0 infiltration of macrophages and T follicular helper cells was higher than that in NT children. In addition, two clusters of cuproptosis-related WT were identified. Enrichment analysis results indicated that genes in cluster 2 were primarily involved in cell division, nuclear division regulation, DNA biosynthesis process, ubiquitin-mediated proteolysis. The SVM model was judged to be the optimal model using 5 genes. Its accuracy was confirmed through a calibration curve and decision curve analysis, demonstrating satisfactory performance on the TARGET-GTEx validation dataset. Additional analysis revealed that these five genes exhibited high expression in both the TARGET-GTEx validation dataset and sequencing data.

CONCLUSION

This research established a link between WT and Cuproptosis. It developed a predictive model for assessing the risk of WT and pinpointed five key genes associated with the disease.

Therapeutic vulnerabilities in triple negative breast cancer: Stem-like traits explored within molecular classification

Triple Negative Breast Cancer (TNBC) is the most aggressive type of breast cancer (BC). Despite advances in the clinical management of TNBC, recurrence-related mortality remains a challenge. The stem-like phenotype of TNBC plays a significant role in the persistence of minimal disease residue after therapy. Individuals exhibiting stem-like characteristics are particularly prone to inducing malignant relapse accompanied by strong resistance. Therefore, stem-like traits have been broadly proposed as therapeutic vulnerabilities to treat TNBC and reduce recurrence. However, heterogeneity within TNBC often generally restricts the stability of the therapeutic efficacy. To understand the heterogeneity and manage TNBC more precisely, multiple TNBC subtyping categories have been reported, providing the basis for profile-according therapeutic regimens. To provide more insight into targeting stem-like traits to ablate TNBC and reduce recurrence in the context of heterogeneity, this paper reviewed the molecular subtyping of TNBC, identified the consensus subtypes with distinct stem-like phenotypes, characterized the stemness hierarchy of TNBC, outlined the biological models for stem-like TNBC subtypes, summarized the therapeutic vulnerabilities in stem-like traits of the subtypes, and proposed potential therapeutic regimens targeting stem-like characteristics to improve TNBC prognosis.

Oncogenic and immunological roles of RACGAP1 in pan-cancer and its potential value in nasopharyngeal carcinoma

A particular GTPase-activating protein called RACGAP1 is involved in apoptosis, proliferation, invasion, metastasis, and drug resistance in a variety of malignancies. Nevertheless, the role of RACGAP1 in pan-cancer was less studied, and its value of the expression and prognostic of nasopharyngeal carcinoma (NPC) has not been explored. Hence, the goal of this study was to investigate the oncogenic and immunological roles of RACGAP1 in various cancers and its potential value in NPC. We comprehensively analyzed RACGAP1 expression, prognostic value, function, methylation levels, relationship with immune cells, immune infiltration, and immunotherapy response in pan-cancer utilizing multiple databases. The results discovered that RACGAP1 expression was elevated in most cancers and suggested poor prognosis, which could be related to the involvement of RACGAP1 in various cancer-related pathways such as the cell cycle and correlated with RACGAP1 methylation levels, immune cell infiltration and reaction to immunotherapy, and chemoresistance. RACGAP1 could inhibit anti-tumor immunity and immunotherapy responses by fostering immune cell infiltration and cytotoxic T lymphocyte dysfunction. Significantly, we validated that RACGAP1 mRNA and protein were highly expressed in NPC. The Gene Expression Omnibus database revealed that elevated RACGAP1 expression was associated with shorter PFS in patients with NPC, and RACGAP1 potentially influenced cell cycle progression, DNA replication, metabolism, and immune-related pathways, resulting in the recurrence and metastasis of NPC. This study indicated that RACGAP1 could be a potential biomarker in pan-cancer and NPC.

Fascin actin-bundling protein 1 regulates non-small cell lung cancer progression by influencing the transcription and splicing of tumorigenesis-related genes

Background

High mortality rates are prevalent among patients with non-small-cell lung cancer (NSCLC), and effective therapeutic targets are key prognostic factors. Fascin actin-bundling protein 1 (FSCN1) promotes NSCLC; however, its role as an RNA-binding protein in NSCLC remains unexplored. Therefore, we aimed to explore FSCN1 expression and function in A549 cells.

Method

We screened for alternative-splicing events and differentially expressed genes (DEGs) after FSCN1 silence via RNA-sequencing (RNA-seq). FSCN1 immunoprecipitation followed by RNA-seq were used to identify target genes whose mRNA expression and pre-mRNA alternative-splicing levels might be influenced by FSCN1.

Results

Silencing FSCN1 in A549 cells affected malignant phenotypes; it inhibited proliferation, migration, and invasion, and promoted apoptosis. RNA-seq analysis revealed 2,851 DEGs and 3,057 alternatively spliced genes. Gene ontology-based functional enrichment analysis showed that downregulated DEGs and alternatively splicing genes were enriched for the cell-cycle. FSCN1 promoted the alternative splicing of cell-cycle-related mRNAs involved in tumorigenesis (i.e., BCCIP, DLGAP5, PRC1, RECQL5, WTAP, and SGO1). Combined analysis of FSCN1 RNA-binding targets and RNA-seq data suggested that FSCN1 might affect ACTG1, KRT7, and PDE3A expression by modulating the pre-mRNA alternative-splicing levels of NME4, NCOR2, and EEF1D, that were bound to long non-coding RNA transcripts (RNASNHG20, NEAT1, NSD2, and FTH1), which were highly abundant. Overall, extensive transcriptome analysis of gene alternative splicing and expression levels was performed in cells transfected with FSCN1 short-interfering RNA. Our data provide global insights into the regulatory mechanisms associated with the roles of FSCN1 and its target genes in lung cancer.

HIF‑1α and RACGAP1 promote the progression of hepatocellular carcinoma in a mutually regulatory way

Hypoxia, a condition characterized by low oxygen levels, serves an important role in the progression of hepatocellular carcinoma (HCC). However, the precise molecular mechanisms underlying hypoxia‑induced HCC progression are yet to be fully elucidated. The present study assessed the involvement of two key factors, hypoxia‑inducible factor‑1α (HIF‑1α) and Rac GTPase activating protein 1 (RACGAP1), in HCC development under hypoxic conditions. HIF‑1α and RACGAP1 genes were overexpressed and knocked down in Hep3B and Huh7 cells using lentiviral transduction and the levels of HIF‑1α and RACGAP1 in the cells were assessed using quantitative PCR, western blotting and immunofluorescence. Co‑immunoprecipitation experiments were performed to evaluate the interaction between HIF‑1α and RACGAP1. Subsequently, the proliferation, apoptosis, migration and invasion of Hep3B and Huh7 cells were assessed using the Cell Counting Kit‑8 assay, flow cytometry, Transwell assay and migration experiments. The expression levels of HIF‑1α and RACGAP1 in normal and HCC tumor samples were analyzed utilizing the Gene Expression Profiling Interactive Analysis database. Furthermore, correlations between HIF‑1α/RACGAP1 gene expression levels and patient survival outcomes were evaluated using the Kaplan‑Meier plotter. Knockdown of HIF‑1α resulted in a significant decrease in RACGAP1 expression, whilst overexpression of HIF‑1α resulted in a significant increase in RACGAP1 expression. Moreover, overexpression and knockdown of RACGAP1 had the same effect on HIF‑1α expression. Additionally, it was demonstrated that HIF‑1α and RACGAP1 interacted directly within a complex. Overexpression of HIF‑1α or RACGAP1 significantly increased proliferation, invasion and migration, and significantly decreased the proportion of apoptotic Hep3B and Huh7 cells. Conversely, knockdown of HIF‑1α or RACGAP1 significantly decreased proliferation, invasion and migration, and significantly increased the proportion of apoptotic Hep3B and Huh7 cells. In addition, the combined knockdown or overexpression of HIF‑1α and RACGAP1 had a more pronounced effect on HCC cell migration compared with knockdown of HIF‑1α alone. Furthermore, there was a significant positive correlation between the expression levels of HIF‑1α and RACGAP1 in HCC tissues and patients with HCC and upregulation of both HIF‑1α and RACGAP1 demonstrated a lower overall survival probability. In conclusion, HIF‑1α and RACGAP1 may synergistically contribute to the development of HCC, highlighting their potential as valuable targets for HCC therapy.

MicroRNA-30c-5p arrests bladder cancer G2/M phase and suppresses its progression by targeting PRC1-mediated blocking of CDK1/Cyclin B1 axis

BACKGROUND

MicroRNAs (miRNAs) play a critical role in cancer development and progression, the dis-regulation of miR-30c-5p has been observed in various malignant tumors but no research was done in bladder cancer (BCa). This study aims to investigate the downregulation of miR-30c-5p in BCa, and examine its mechanism and prognostic significance.

METHODS

Bioinformatics analyses and clinical specimens were employed to investigate the relationship between miR-30c-5p and clinical information in BCa patients. The expression levels of miR-30c-5p and its target gene were assessed by real-time PCR and western blot. Cell viability was evaluated through clonogenic capacity, CCK-8, and EdU assays. Cell cycle distribution and cell apoptosis were determined by flow cytometry. The anti-tumor effect of miR-30c-5p was also validated in animal models.

RESULTS

The expression levels of miR-30c-5p were significantly decreased in both bladder tumor tissue and BCa cell lines. Low miR-30c-5p expression was found to be correlated with unfavorable TNM stages and poor prognosis. Over-expressing miR-30c-5p was observed to hinder BCa cell growth, migration, and invasion abilities and causing cell cycle arrest. Mechanistically, miR-30c-5p directly binds and suppresses PRC1, thereby blocking the CDK1/Cyclin B1 axis in BCa, thus impairing BCa cell viability and inducing cell cycle arrest at G2/M phase.

CONCLUSION

Down-regulated miR-30c-5p promotes BCa through its target gene PRC1, miR-30c-5p is a favorable biomarker for predicting clinical outcomes in BCa patients and has the potential to be a therapeutic target.

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

BACKGROUND

Hepatocellular carcinoma (HCC), the third most prevalent cause of cancer-related death, is a frequent primary liver cancer with a high rate of morbidity and mortality. T-cell depletion (TEX) is a progressive decline in T-cell function due to continuous stimulation of the TCR in the presence of sustained antigen exposure. Numerous studies have shown that TEX plays an essential role in the antitumor immune process and is significantly associated with patient prognosis. Hence, it is important to gain insight into the potential role of T cell depletion in the tumor microenvironment. The purpose of this study was to develop a trustworthy TEX-based signature using single-cell RNA-seq (scRNA-seq) and high-throughput RNA sequencing, opening up new avenues for evaluating the prognosis and immunotherapeutic response of HCC patients.

METHODS

The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases were used to download RNA-seq information for HCC patients. The 10x scRNA-seq. data of HCC were downloaded from GSE166635, and UMAP was used for clustering descending, and subgroup identification. TEX-related genes were identified by gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Afterward, we established a prognostic TEX signature using LASSO-Cox analysis. External validation was performed in the ICGC cohort. Immunotherapy response was assessed by the IMvigor210, GSE78220, GSE79671, and GSE91061cohorts. In addition, differences in mutational landscape and chemotherapy sensitivity between different risk groups were investigated. Finally, the differential expression of TEX genes was verified by qRT-PCR.

RESULT

11 TEX genes were thought to be highly predictive of the prognosis of HCC and substantially related to HCC prognosis. Patients in the low-risk group had a greater overall survival rate than those in the high-risk group, according to multivariate analysis, which also revealed that the model was an independent predictor of HCC. The predictive efficacy of columnar maps created from clinical features and risk scores was strong.

CONCLUSION

TEX signature and column line plots showed good predictive performance, providing a new perspective for assessing pre-immune efficacy, which will be useful for future precision immuno-oncology studies.