Identification of Hepatocellular Carcinoma-Related Potential Genes and Pathways Through Bioinformatic-Based Analyses

Recent updates of centromere proteins in hepatocellular carcinoma: a review

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide, with approximately 800,000 deaths worldwide each year. Owing to the atypical early symptoms and characteristics of HCC, over 80% of HCC patients cannot receive curative treatment. The treatment of HCC is facing a bottleneck, and new treatment methods are urgently needed. Since the pathogenesis of HCC is not yet clear, identifying the molecular mechanisms and therapeutic targets related to it is crucial. Centromeres are considered special deoxyribonucleic acid (DNA) sequences with highly repetitive sequences that are physically connected to the spindle during cell division, ensuring equal division of genetic material between daughter cells. The numerous proteins that aggregate on this sequence during cell division are called centromere proteins (CENPs). Currently, numerous studies have shown that CENPs are abnormally expressed in tumor cells and are associated with patient prognosis. The abnormal expression of CENPs is a key cause of chromosomal instability. Furthermore, chromosomal instability is a common characteristic of the majority of tumors. Chromosomal instability can lead to uncontrolled and sustained division and proliferation of malignant tumors. Therapeutic plans targeting CENPs play important roles in the treatment of HCC. For example, small ribonucleic acid (RNA) can silence CENP expression and prevent the occurrence and development of liver cancer. In recent years, studies of HCC-targeting CENPs have gradually increased but are still relatively novel, requiring further systematic elaboration. In this review, we provide a detailed introduction to the characteristics of CENPs and discuss their roles in HCC. In addition, we discuss their application prospects in future clinical practice.

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.

Identification of GMFG as a novel biomarker in IgA nephropathy based on comprehensive bioinformatics analysis

Background

IgA nephropathy (IgAN) stands as the most prevalent form of glomerulonephritis and ranks among the leading causes of end-stage renal disease worldwide. Regrettably, we continue to grapple with the absence of dependable diagnostic markers and specific therapeutic agents for IgAN. Therefore, this study endeavors to explore novel biomarkers and potential therapeutic targets in IgAN, while also considering their relevance in the context of tumors.

Methods

We gathered IgAN datasets from the Gene Expression Omnibus (GEO) database. Subsequently, leveraging these datasets, we conducted an array of analyses, encompassing differential gene expression, weighted gene co-expression network analysis (WGCNA), machine learning, receiver operator characteristic (ROC) curve analysis, gene expression validation, clinical correlations, and immune infiltration. Finally, we carried out pan-cancer analysis based on hub gene.

Results

We obtained 1391 differentially expressed genes (DEGs) in GSE93798 and 783 DGEs in GSE14795, respectively. identifying 69 common genes for further investigation. Subsequently, GMFG was identified the hub gene based on machine learning. In the verification set and the training set, the GMFG was higher in the IgAN group than in the healthy group and all of the GMFG area under the curve (AUC) was more 0.8. In addition, GMFG has a close relationship with the prognosis of malignancies and a range of immune cells.

Conclusions

Our study suggests that GMFG could serve as a promising novel biomarker and potential therapeutic target for both IgAN and certain types of tumors.

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.

Identification of BRIP1, NSMCE2, ANAPC7, RAD18 and TTL from chromosome segregation gene set associated with hepatocellular carcinoma

INTRODUCTION

Hepatocellular carcinoma is one of the most frequent cancers with high mortality rate worldwide.

METHODS

TCGA LIHC HTseq counts were analyzed. GSEA was performed with GO BP gene sets. GO analysis was performed with differentially expressed genes. The subset of genes contributing most of the enrichment result of GO_BP_CHROMOSOME_SEGREGATION of GSEA were identified. Five genes have been selected in this subset of genes for further analysis. A microarray data set, GSE112790, was analyzed as a validation data set. Survival analysis was performed.

RESULTS

According to GSEA and GO analysis several gene sets and processes related to chromosome segregation were enriched in LIHC. GO_BP_CHROMOSOME_SEGREGATION gene set from GSEA had the highest size of the genes contributing most of the enrichment. Five genes in this gene set; BRIP1, NSMCE2, ANAPC7, RAD18 and TTL, whose expressions and prognostic values have not been studied in hepatocellular carcinoma in detail, have been selected for further analyses. Expression of these five genes were identified as significantly upregulated in LIHC RNA-seq and HCC microarray data set. Survival analysis showed that high expression of the five genes was associated with poor overall survival in HCC patients.

CONCLUSION

Selected genes were upregulated and had prognostic value in HCC.

Long noncoding RNA-LINC00478 promotes the progression of clear cell renal cell carcinoma through PBX3

Long noncoding RNAs play an important regulatory role in the development and progression of tumors. Our study found that LINC00478 was upregulated in clear cell renal cell carcinoma (ccRCC), so we made an in-depth exploration into its mechanism. In Caki-2 cells, we established the oe-LINC00478 cell line overexpressing LINC00478, and established underexpressing sh-LINC00478 cell line by short hairpin RNA silencing. The abilities of oe-LINC00478 cell invasion and metastasis were significantly enhanced, and the cell proliferative potential was also improved. The cellular expressions of PBX3, CDCA8, and CDK2 were upregulated, while in the sh-LINC00478 cells, the proliferative potential and metastatic and invasive abilities were weakened. Similarly, we established the PBX3-overexpressing oe-PBX3 cell line and the PBX3-underexpressing sh-PBX3 cell line, finding that the PBX3 overexpression enhanced the metastatic and invasive abilities of Caki-2 cells. When we overexpressed LINC00478 in PBX3-knockout Caki-2-PBX3^{- / -} cells, no significant changes were noted in the metastatic or invasive ability. Through RNA pull-down and RNA-binding protein immunoprecipitation assays, we found that LINC00478 could facilitate the transcription-translation processes of PBX3 by binding to it, thus further promoting the expression of downstream cyclins to exert its action. In animal experimentation, the oe-LINC00478 and sh-LINC00478 Caki-2 cells were separately seeded, revealing that the tumor volume was significantly larger in the oe-LINC00478 group than in the sh-LINC00478 group. This study finds that by promoting the PBX3 transcription, LINC00478 can further regulate the expressions of downstream cyclins, thereby facilitating the metastasis and invasion of ccRCC.

Construction and validation of a novel ten miRNA-pair based signature for the prognosis of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most predominate pathological subtype of renal cell carcinoma, causing a recurrence or metastasis rate as high as 20% to 40% after operation, for which effective prognostic signature is urgently needed.

METHODS

The mRNA and miRNA profiles of ccRCC specimens were collected from the Cancer Genome Atlas. MiRNA-pair risk score (miPRS) for each miRNA pair was generated as a signature and validated by univariate and multivariate Cox proportional hazards regression analysis. Functional enrichment was performed, and immune cells infiltration, as well as tumor mutation burden (TMB), and immunophenoscore (IPS) were evaluated between high and low miPRS groups. Target gene-prediction and differentially expressed gene-analysis were performed based on databases of miRDB, miRTarBase, and TargetScan. Multivariate Cox proportional hazards regression analysis was adopted to establish the prognostic model and Kaplan-Meier survival analysis was performed.

FINDINGS

A novel 10 miRNA-pair based signature was established. Area under the time-dependent receiver operating curve proved the performance of the signature in the training, validation, and testing cohorts. Higher TMB, as well as the higher CTLA4-negative PD1-negative IPS, were discovered in high miPRS patients. A prognostic model was built based on miPRS (1 year-, 5 year-, 10 year- ROC-AUC=0.92, 0.84, 0.82, respectively).

INTERPRETATION

The model based on miPRS is a novel and valid tool for predicting the prognosis of ccRCC.

FUNDING

This study was supported by research grants from the China National Natural Scientific Foundation (81903972, 82002018, and 82170752) and Shanghai Sailing Program (19YF1406700 and 20YF1406000).

Prognostic comparative genes predict targets for sorafenib combination therapies in hepatocellular carcinoma

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Large-scale comparative transcriptomics analysis of hepatocellular carcinoma reveals 664 prognostic comparative HCC (pcHCC) genes.

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pcHCC genes included novel targets with potential utility in sorafenib combination therapies.

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Knockdown of the selective pcHCC genes NCAPG and CENPW downregulated the p38/STAT3 axis to enhance sorafenib combination treatments.

With the increasing incidence and mortality of human hepatocellular carcinoma (HCC) worldwide, revealing innovative targets to improve therapeutic strategies is crucial for prolonging the lives of patients. To identify innovative targets, we conducted a comprehensive comparative transcriptome analysis of 5,410 human HCCs and 974 mouse liver cancers to identify concordantly expressed genes associated with patient survival. Among the 664 identified prognostic comparative HCC (pcHCC) genes, upregulated pcHCC genes were associated with prognostic clinical features, including large tumor size, vascular invasion and late HCC stages. Interestingly, after validating HCC patient prognoses in multiple independent datasets, we matched the 664 aberrant pcHCC genes with the sorafenib-altered genes in TCGA_LIHC patients and found these 664 pcHCC genes were enriched in sorafenib-related functions, such as downregulated xenobiotic and lipid metabolism and upregulated cell proliferation. Therapeutic agents targeting aberrant pcHCC genes presented divergent molecular mechanisms, including suppression of sorafenib-unrelated oncogenic pathways, induction of sorafenib-unrelated ferroptosis, and modulation of sorafenib transportation and metabolism, to potentiate sorafenib therapeutic effects in HCC combination therapy. Moreover, the pcHCC genes NCAPG and CENPW, which have not been targeted in combination with sorafenib treatment, were knocked down and combined with sorafenib treatment, which reduced HCC cell viability based on disruption to the p38/STAT3 axis, thereby hypersensitizing HCC cells. Together, our results provide important resources and reveal that 664 pcHCC genes represent innovative targets suitable for developing therapeutic strategies in combination with sorafenib based on the divergent synergistic mechanisms for HCC tumor suppression.

Autophagy-Related Signatures as Prognostic Indicators for Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common and deadly type of liver cancer. Autophagy is the process of transporting damaged or aging cellular components into lysosomes for digestion and degradation. Accumulating evidence implies that autophagy is a key factor in tumor progression. The aim of this study was to determine a panel of novel autophagy-related prognostic markers for liver cancer.

Methods

We conducted a comprehensive analysis of autophagy-related gene (ARG) expression profiles and corresponding clinical information based on The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The univariate Cox proportional regression model was used to screen candidate autophagy-related prognostic genes. In addition, a multivariate Cox proportional regression model was used to identify five key prognostic autophagy-related genes (ATIC, BAX, BIRC5, CAPNS1, and FKBP1A), which were used to construct a prognostic signature. Real-time qPCR analysis was used to evaluate the expression levels of ARGs in 20 surgically resected HCC samples and matched tumor-adjacent normal tissue samples. In addition, the effect of FKBP1A on autophagy and tumor progression was determined by performing in vitro and in vivo experiments.

Results

Based on the prognostic signature, patients with liver cancer were significantly divided into high-risk and low-risk groups in terms of overall survival (OS). A subsequent multivariate Cox regression analysis indicated that the prognostic signature remained an independent prognostic factor for OS. The prognostic signature possessing a better area under the curve (AUC) displayed better performance in predicting the survival of patients with HCC than other clinical parameters. Furthermore, FKBP1A was overexpressed in HCC tissues, and knockdown of FKBP1A impaired cell proliferation, migration, and invasion through the PI3K/AKT/mTOR signaling pathway.

Conclusion

This study provides a prospective biomarker for monitoring outcomes of patients with HCC.

Upregulation of CENPF is linked to aggressive features of osteosarcoma

Centromere protein F (CENPF) plays a key role in the regulation of the cell cycle. The present study revealed that CENPF was overexpressed in a variety of tumors and associated with the poor prognosis of osteosarcoma. The mRNA expression levels of CENPF were analyzed using the Gene Expression Profiling Interactive Analysis database and the protein levels of CENPF were detected in the specimens from patients with osteosarcoma using immunohistochemistry. Cell proliferation, cell cycle and flow cytometry assays were performed after the transfection of control or CENPF plasmids into osteosarcoma cells. A xenografts assay was used to determine the effects of CENPF on tumor growth in vivo. The results showed that CENPF was upregulated in osteosarcoma tissues and associated with high-grade tumor stage (P=0.023) and intraglandular dissemination (P=0.046). The transfection-induced depletion of CENPF in human osteosarcoma MG-63 and U-2 OS cell lines inhibited cell proliferation, stimulated apoptosis and induced cell cycle arrest. Induced CENPF depletion in MG-63 cells inhibited tumor growth of osteosarcoma cells in mice. These findings suggested that elevated CENPF levels contributed to increased cell proliferation by mediating apoptosis and cell cycle in osteosarcoma. Therefore, CENPF might be a potential biomarker for poor prognosis of osteosarcoma.

Prognostic value of baculoviral IAP repeat containing 5 expression as a new biomarker in lung adenocarcinoma: a meta-analysis

BACKGROUND

BIRC5 is associated with the prognosis of a variety of tumors. This meta-analysis aimed to identify whether BIRC5 is associated with the prognosis of lung adenocarcinoma (LUAD).

RESEARCH DESIGN AND METHODS

We conducted an in-depth review of seven Chinese and English databases and two high-throughput sequencing databases according to inclusion and exclusion criteria to find relevant studies. The pooled standardized mean differences (SMDs) and 95% confidence intervals (CIs) for the associations between the BIRC5 expression level and clinicopathological characteristics were calculated, and the pooled hazard ratios (HRs) and 95% CIs were calculated to estimate associations between the BIRC5 expression level and survival outcomes.

RESULTS

In total, 17 studies involving 2887 LUAD patients whose BIRC5 expression level was known were included in this meta-analysis. The BIRC5 expression level was higher in younger patients, males, and smokers and correlated with advanced AJCC, T and N stages but not M stage. A high BIRC5 expression level also correlated with poor overall survival (OS) and progression-free survival (PFS). There was no publication bias in this study.

CONCLUSIONS

This meta-analysis indicates that BIRC5 is a significant biomarker for a poor prognosis and poor clinicopathological outcomes in patients with LUAD.

Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods

Background

Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.

Methods

Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.

Results

A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.

Conclusion

Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.

Highly deregulated lncRNA LOC is associated with overall worse prognosis in Hepatocellular Carcinoma patients

Although numerous long non-coding RNAs (lncRNAs) were reported to be deregulated in Hepatocellular Carcinoma (HCC), experimentally characterized, and/or associated with patient's clinical characteristics, there is, thus far, minimal concerted research strategy to identify deregulated lncRNAs that modulate prognosis of HCC patients. Here, we present a novel strategy where we identify lncRNAs, which are not only de-regulated in HCC patients, but are also associated with pertinent clinical characteristics, potentially contributing to the prognosis of HCC patients. LOC101926913 (LOC) was further characterized because it is the most highly differentially expressed amongst those that are associated with the most number of clinical features (tumor-stage, vascular and tumor invasion and poorer overall survival). Experimental gain- and loss-of-function manipulation of LOC in liver cell-lines highlight LOC as a potential onco-lncRNA promoting cell proliferation, anchorage independent growth and invasion. LOC expression in cells up-regulated genes involved in GTPase-activities and downregulated genes associated with cellular detoxification, oxygen- and drug-transport. Hence, LOC may represent a novel therapeutic target, modulating prognosis of HCC patients through up-regulating GTPase-activities and down-regulating detoxification, oxygen- and drug-transport. This strategy may thus be useful for the identification of clinically relevant lncRNAs as potential biomarkers/targets that modulate prognosis in other cancers as well.

Machine Learning for Building Immune Genetic Model in Hepatocellular Carcinoma Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is the leading liver cancer with special immune microenvironment, which played vital roles in tumor relapse and poor drug responses. In this study, we aimed to explore the prognostic immune signatures in HCC and tried to construct an immune-risk model for patient evaluation.

METHODS

RNA sequencing profiles of HCC patients were collected from the cancer genome Atlas (TCGA), international cancer genome consortium (ICGC), and gene expression omnibus (GEO) databases (GSE14520). Differentially expressed immune genes, derived from ImmPort database and MSigDB signaling pathway lists, between tumor and normal tissues were analyzed with Limma package in R environment. Univariate Cox regression was performed to find survival-related immune genes in TCGA dataset, and in further random forest algorithm analysis, significantly changed immune genes were used to generate a multivariate Cox model to calculate the corresponding immune-risk score. The model was examined in the other two datasets with recipient operation curve (ROC) and survival analysis. Risk effects of immune-risk score and clinical characteristics of patients were individually evaluated, and significant factors were then used to generate a nomogram.

RESULTS

There were 52 downregulated and 259 upregulated immune genes between tumor and relatively normal tissues, and the final immune-risk model (based on SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV and MAP4K2) can better differentiate patients into high and low immune-risk subpopulations, in which high score patients showed worse outcomes after resection (p < 0.05). The differentially enriched pathways between the two groups were mainly about cell proliferation and cytokine production, and calculated immune-risk score was also highly correlated with immune infiltration levels. The nomogram, constructed with immune-risk score and tumor stages, showed high accuracy and clinical benefits in prediction of 1-, 3- and 5-year overall survival, which is useful in clinical practice.

CONCLUSION

The immune-risk model, based on expression of SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV, and MAP4K2, can better differentiate patients into high and low immune-risk groups. Combined nomogram, using immune-risk score and tumor stages, could make accurate prediction of 1-, 3- and 5-year survival in HCC patients.

Identification of Hypoxia-Related Differentially Expressed Genes and Construction of the Clinical Prognostic Predictor in Hepatocellular Carcinoma by Bioinformatic Analysis

Background. Hypoxia closely relates to malignant progression and appears to be prognostic for outcome in hepatocellular carcinoma (HCC). Our research is aimed at mining the hypoxic-related genes (HRGs) and constructing a prognostic predictor (PP) model on clinical prognosis in HCC patients. Methods. RNA-sequencing data about HRGs and clinical data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database portal. Differentially expressed HRGs between HCC and para-carcinoma tissue samples were obtained by applying the Wilcox analysis in R statistical software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene functional enrichment analyses. Then, the patients who were asked to follow up for at least one month were enrolled in the following study. Cox proportional risk regression model was applied to obtain key HRGs which related to overall survival (OS) in HCC. PP was constructed and defined, and the accuracy of PP was validated by constructing the signature in a training set and validation set. Connectivity map (CMap) was used to find potential drugs, and gene set cancer analysis (GSCA) was also performed to explore the underlying molecular mechanisms. Results. Thirty-seven differentially expressed HRGs were obtained. It contained 28 upregulated and 9 downregulated genes. After the univariate Cox regression model analysis, we obtained 27 prognosis-related HRGs. Of these, 25 genes were risk factors for cancer, and 2 genes were protective factors. The PP was composed by 12 key genes (HDLBP, SAP30, PFKP, DPYSL4, SLC2A1, HMOX1, PGK1, ERO1A, LDHA, ENO2, SLC6A6, and TPI1). GSCA results showed the overall activity of these 12 key genes in 10 cancer-related pathways. Besides, CMap identified deferoxamine, crotamiton, talampicillin, and lycorine might have effects with HCC. Conclusions. This study firstly reported 12 prognostic HRGs and constructed the model of the PP. This comprehensive research of multiple databases helps us gain insight into the biological properties of HCC and provides deferoxamine, crotamiton, talampicillin, and lycorine as potential drugs to fight against HCC.

SPC25 overexpression promotes tumor proliferation and is prognostic of poor survival in hepatocellular carcinoma

Background: The nuclear division cycle 80 (NDC80) complex assures proper chromosome segregation during the cell cycle progression. SPC25 is a crucial component of NDC80, and its role in hepatocellular carcinoma (HCC) has been explored recently. This study characterized the differential expression of SPC25 in HCC patients of different races and HBV infection status.

Methods: Expression patterns of SPC25 were evaluated in TCGA and Chinese HCC patients. Kaplan-Meier analysis was applied to examine the predictive value of SPC25. In vitro and in vivo functional assays were conducted to explore the role of SPC25 in HCC. Bioinformatics methods were applied to investigate the regulatory mechanisms of SPC25.

Findings: The mRNA levels of SPC25 were up-regulated in HCC. SPC25 has a significantly higher transcriptional level in Asian patients than Caucasian patients. SPC25 promoted HCC cell proliferation in vitro and tumor growth in vivo by accelerating the cell cycle. We identified transcription factors, miRNAs, and immune cells that may interact with SPC25.

Interpretation: The findings suggest that increased expression of SPC25 is associated with poor prognosis of HCC and enhances the proliferative capacity of HCC cells. SPC25 could serve as a valuable prognostic marker and a novel treatment target for HCC.

NCAPG Induces Cell Proliferation in Cardia Adenocarcinoma via PI3K/AKT Signaling Pathway

Purpose

Previous studies have shown that non-SMC condensin I complex subunit G (NCAPG) overexpression is correlated to poor prognosis of multiple cancer types. Herein, we explored the underlying mechanism of NCAPG-mediated cardia adenocarcinoma (CA) proliferation and cell cycle regulation.

Methods

The protein profiling technology was used to analyze the gene expression in 20 CA and adjacent tissue samples. Differential genes were identified by bioinformatic analysis. Western blot and qRT-PCR-based analysis assessed the NCAPG expression levels in multiple CA cell lines. CA cell lines, SGC-7901 and AGS, were transfected with Lip 2000, and stably transfected cell lines were screened for NCAPG overexpression and downregulation. MTT and clone formation assays were employed to detect cell proliferation, and cell cycle phases were analyzed using flow cytometry. Western blot was performed to determine the NCAPG gene expression levels. Finally, we studied the tumorigenic effects of NCAPG in the mouse model and validated the cell experiment results using immunohistochemistry.

Results

A significant overexpression of NCAPG was found in CA tissues and CA cell lines. The outcomes of MTT and clone formation assays showed that NCAPG upregulation promoted cell proliferation. The outcomes of these analyses were further validated using nude mice as an in vivo tumor model. As per the outcome of Western blot and flow cytometry analysis, NCAPG regulated the G1 phase through the cyclins (CDK4, CDK6, and cyclin D1) overexpression and cell cycle inhibitors (P21 and P27) downregulation. Overexpressed NCAPG and silenced NCAPG, both in vitro and in vivo, resulted in abnormal activation of the PI3K/AKT signaling pathway in CA cells. We observed that NCAPG overexpression increased the levels of phosphorylated PI3K, AKT, and GSK3β; however, their total protein levels remained unchanged in CA cells.

Conclusion

As a CA oncogene, NCAPG promoted cell proliferation and regulated cell cycle through PI3K/AKT signaling pathway activation.