Upregulate KIF4A Enhances Proliferation, Invasion of Hepatocellular Carcinoma and Indicates poor prognosis Across Human Cancer Types

The role of kinesin family members in hepatobiliary carcinomas: from bench to bedside

As a major component of the digestive system malignancies, tumors originating from the hepatic and biliary ducts seriously endanger public health. The kinesins (KIFs) are molecular motors that enable the microtubule-dependent intracellular trafficking necessary for mitosis and meiosis. Normally, the stability of KIFs is essential to maintain cell proliferation and genetic homeostasis. However, aberrant KIFs activity may destroy this dynamic stability, leading to uncontrolled cell division and tumor initiation. In this work, we have made an integral summarization of the specific roles of KIFs in hepatocellular and biliary duct carcinogenesis, referring to aberrant signal transduction and the potential for prognostic evaluation. Additionally, current clinical applications of KIFs-targeted inhibitors have also been discussed, including their efficacy advantages, relationship with drug sensitivity or resistance, the feasibility of combination chemotherapy or other targeted agents, as well as the corresponding clinical trials. In conclusion, the abnormally activated KIFs participate in the regulation of tumor progression via a diverse range of mechanisms and are closely associated with tumor prognosis. Meanwhile, KIFs-aimed inhibitors also carry out a promising tumor-targeted therapeutic strategy that deserves to be further investigated in hepatobiliary carcinoma (HBC).

Inhibition of the MNK1/2-eIF4E Axis Augments Palbociclib-Mediated Antitumor Activity in Melanoma and Breast Cancer

Aberrant cell-cycle progression is characteristic of melanoma, and CDK4/6 inhibitors, such as palbociclib, are currently being tested for efficacy in this disease. Despite the promising nature of CDK4/6 inhibitors, their use as single agents in melanoma has shown limited clinical benefit. Herein, we discovered that treatment of tumor cells with palbociclib induces the phosphorylation of the mRNA translation initiation factor eIF4E. When phosphorylated, eIF4E specifically engenders the translation of mRNAs that code for proteins involved in cell survival. We hypothesized that cancer cells treated with palbociclib use upregulated phosphorylated eIF4E (phospho-eIF4E) to escape the antitumor benefits of this drug. Indeed, we found that pharmacologic or genetic disruption of MNK1/2 activity, the only known kinases for eIF4E, enhanced the ability of palbociclib to decrease clonogenic outgrowth. Moreover, a quantitative proteomics analysis of melanoma cells treated with combined MNK1/2 and CDK4/6 inhibitors showed downregulation of proteins with critical roles in cell-cycle progression and mitosis, including AURKB, TPX2, and survivin. We also observed that palbociclib-resistant breast cancer cells have higher basal levels of phospho-eIF4E, and that treatment with MNK1/2 inhibitors sensitized these palbociclib-resistant cells to CDK4/6 inhibition. In vivo we demonstrate that the combination of MNK1/2 and CDK4/6 inhibition significantly increases the overall survival of mice compared with either monotherapy. Overall, our data support MNK1/2 inhibitors as promising drugs to potentiate the antineoplastic effects of palbociclib and overcome therapy-resistant disease.

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

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

KIF4A drives gliomas growth by transcriptional repression of Rac1/Cdc42 to induce cytoskeletal remodeling in glioma cells

Glioma is one of the most prevalent cancers diseases in the worldwide. Kinesin superfamily protein 4 (KIF4), a KIF member classified in Kinesin 4 has been indicated as a mediator acted in tumorigenesis of human cancer. However, the mechanism of KIF4A on glioma is yet to be investigated. This study aimed to explore the potential function and mechanism of KIF4A in gliomas. We analyzed the KIF4A expression and the prognosis in gliomas patients using The Cancer Genome Atlas (TCGA) databases. KIF4A level in normal human astrocyte cell (NHA) and glioma cell lines were examined by Western blot. We studied the function of KIF4A on proliferation, migration, invasion, cell cycle in glioma cell lines using a series of in vitro and in vivo experiments. Chromatin Immunoprecipitation (ChIP) analysis was applied to searching potential KIF4A related downstream in glioma. We identified the significant up-regulated expression of KIF4A both in glioma tissues and cell. Glioma patients with elevated KIF4A expression have shorter survival. Down-regulation of KIF4A exerted inhibitory effect on cell proliferation, invasion and migration. We crucially identified that KIF4A drives gliomas growth by transcriptional repression of Rac1/Cdc42 to induce cytoskeletal remodeling in glioma cells. Knockdown of KIF4A decreased RohA, Rac1, Cdc42, Pak1 and Pak2 expression level. Our study provided a prospect that KIF4A functions as an oncogene in glioma.

A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection

Great advances in deep learning have provided effective solutions for prediction tasks in the biomedical field. However, accurate prognosis prediction using cancer genomics data remains challenging due to the severe overfitting problem caused by curse of dimensionality inherent to high-throughput sequencing data. Moreover, there are unique challenges to perform survival analysis, arising from the difficulty in utilizing censored samples whose events of interest are not observed. Convolutional neural network (CNN) models provide us the opportunity to extract meaningful hierarchical features to characterize cancer subtype and prognosis outcomes. On the other hand, feature selection can mitigate overfitting and reduce subsequent model training computation burden by screening out significant genes from redundant genes. To accomplish model simplification, we developed a concise and efficient survival analysis model, named CNN-Cox model, which combines a special CNN framework with prognosis-related feature selection cascaded Wx, with the advantage of less computation demand utilizing light training parameters. Experiment results show that CNN-Cox model achieved consistent higher C-index values and better survival prediction performance across seven cancer type datasets in The Cancer Genome Atlas cohort, including bladder carcinoma, head and neck squamous cell carcinoma, kidney renal cell carcinoma, brain low-grade glioma, lung adenocarcinoma (LUAD), lung squamous cell carcinoma, and skin cutaneous melanoma, compared with the existing state-of-the-art survival analysis methods. As an illustration of model interpretation, we examined potential prognostic gene signatures of LUAD dataset using the proposed CNN-Cox model. We conducted protein-protein interaction network analysis to identify potential prognostic genes and further analyzed the biological function of 13 hub genes, including ANLN, RACGAP1, KIF4A, KIF20A, KIF14, ASPM, CDK1, SPC25, NCAPG, MKI67, HJURP, EXO1, HMMR, whose high expression is significantly associated with poor survival of LUAD patients. These findings confirmed that CNN-Cox model is effective in extracting not only prognosis factors but also biologically meaningful gene features. The codes are available at the GitHub website: https://github.com/wangwangCCChen/CNN-Cox .

Gene coexpression network analysis identifies hubs in hepatitis B virus-associated hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is among the leading causes of cancer-related death worldwide. The molecular pathogenesis of HCC involves multiple signaling pathways. This study utilizes systems and bioinformatic approaches to investigate the pathogenesis of HCC.

METHODS

Gene expression microarray data were obtained from 50 patients with chronic hepatitis B and HCC. There were 1649 differentially expressed genes inferred from tumorous and nontumorous datasets. Weighted gene coexpression network analysis (WGCNA) was performed to construct clustered coexpressed gene modules. Statistical analysis was used to study the correlation between gene coexpression networks and demographic features of patients. Functional annotation and pathway inference were explored for each coexpression network. Network analysis identified hub genes of the prognostic gene coexpression network. The hub genes were further validated with a public database.

RESULT

Five distinct gene coexpression networks were identified by WGCNA. A distinct coexpressed gene network was significantly correlated with HCC prognosis. Pathway analysis of this network revealed extensive integration with cell cycle regulation. Ten hub genes of this gene network were inferred from protein-protein interaction network analysis and further validated in an external validation dataset. Survival analysis showed that lower expression of the 10-gene signature had better overall survival and recurrence-free survival.

CONCLUSION

This study identified a crucial gene coexpression network associated with the prognosis of hepatitis B virus-related HCC. The identified hub genes may provide insights for HCC pathogenesis and may be potential prognostic markers or therapeutic targets.

EZH2 suppresses insulinoma development by epigenetically reducing KIF4A expression via H3K27me3 modification

Kinesin family member 4A (KIF4A), located in the human chromosome band Xq13.1, is aberrantly overexpressed in various cancers. Our study intended to assess the expression of KIF4A in insulinoma and to gain new insights into the molecular mechanisms of this rare disease. First, KIF4A was significantly recruited in pancreatic endocrine cells relative to other cell types. A significant correlation existed between the overexpression of KIF4A and the poor survival of pancreatic adenocarcinoma patients. As revealed by CCK-8, TUNEL assay, flow cytometry, wound healing, Matrigel-transwell, senescence-associated β-galactosidase staining, ELISA, and subcutaneous tumor formation analysis in nude mice, knocking down KIF4A significantly inhibited the growth and metastasis of insulinoma cells in vivo and in vitro. Mechanistically, we observed that KIF4A promoter sequences had reduced H3K27me3 modifications, and decline in enhancer of zeste homolog-2 (EZH2) expression promoted KIF4A expression by reducing the modification, thus leading to insulinoma. Moreover, EZH2 knockdown-induced insulinoma cell proliferation was dependent on KIF4A overexpression since KIF4A knockdown eradicated shEZH2-induced proliferation of insulinoma cells. In summary, KIF4A was identified as a possible therapeutic target for insulinoma.

Damage-Net: A program for DNA repair meta-analysis identifies a network of novel repair genes that facilitate cancer evolution

The advent of genome-wide methods for identifying novel components in biological processes including CRISPR screens and proteomic studies, has transformed the research landscape within the biological sciences. However, each study normally investigates a single aspect of a process without integration of other published datasets. Here, we present Damage-Net, a program with a curated database of published results from a broad range of studies investigating DNA repair, that facilitates simple and quick meta-analysis. Users can incorporate their own datasets for analysis, and query genes of interest in the database. Importantly, this program also allows users to examine the correlation of genes of interest with pan-cancer patient survival and mutational burden effects. Interrogating these datasets revealed a network of genes that associated with cancer progression in adrenocortical carcinoma via facilitating mutational burden, ultimately contributing substantially to adrenocortical carcinoma's poor prognosis. Download at www.damage-net.co.uk.

YAP/TEAD4-induced KIF4A contributes to the progression and worse prognosis of esophageal squamous cell carcinoma

Aberrant expression of kinesin family member 4A (KIF4A), which is associated with tumor progression, has been reported in several types of cancer. However, its expression and the underlying molecular mechanisms regulating the transcription of KIF4A in esophageal squamous cell carcinoma (ESCC) remain largely unclear. Here, we found that high KIF4A expression was positively correlated with tumor stage and poor prognosis in ESCC patients. KIF4A silencing significantly inhibited the growth and migration of ESCC cells, arrested cell cycle, and induced apoptosis. Interestingly, KIF4A expression was positively related to the expression of YAP in human ESCC tissues. YAP knockdown or disrupting YAP/TEAD4 interaction by verteporfin repressed KIF4A expression. Also, KIF4A knockdown significantly inhibited the cell growth induced by YAP overexpression. Mechanistically, YAP activated KIF4A transcriptional expression by TEAD4-mediated direct binding to KIF4A promoter. Finally, KIF4A knockdown and verteporfin treatment synergistically inhibited tumor growth in xenograft models. Together, these results indicated that KIF4A, a novel target gene of YAP/TEAD4, may be a progression and prognostic biomarker of ESCC. Targeting drugs for KIF4A combined with YAP inhibitor may be a novel therapeutic strategy for ESCC.

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.

Identification of KIF4A as a pan-cancer diagnostic and prognostic biomarker via bioinformatics analysis and validation in osteosarcoma cell lines

Background

Cancer is a disease of abnormal cell proliferation caused by abnormal expression of cancer-related genes. However, it is still difficult to distinguish benign and malignant lesions in many cases. KIF4A has been reported to be associated with a variety of cancer lesions. We aimed to explore whether KIF4A could be used as a biomarker of pan-cancer diagnostic.

Methods

We identified twenty-eight cell cycle-related genes that were overexpressed in no less than ten types of cancer. We determined KIF4A mRNA and protein expression in osteosarcoma (OS) cells. Furthermore, to determine the effect of KIF4A in OS, we silenced KIF4A in OS cells and detected cell viability, colony formation, invasion, migration, apoptosis and cell cycle parameters.

Results

KIF4A exhibited upregulated expression in eleven types of cancer. Cell cycle-related genes are extensively overexpressed in various types of cancers. KIF4A overexpression can serve as a diagnostic and prognostic marker in various cancers. Silencing KIF4A inhibited the viability, colony formation, invasion and migration and induced apoptosis and cell cycle arrest of OS cells. Our findings revealed that high expression of KIF4A could serve as a diagnostic and prognostic marker in OS cancers.

Conclusion

KIF4A could serve as a pan-cancer diagnostic and prognostic marker. KIF4A could be used as a novel therapeutic target for OS.

KIF4A knockdown suppresses ovarian cancer cell proliferation and induces apoptosis by downregulating BUB1 expression

Ovarian cancer is one of the most common lethal gynecological malignancies worldwide. Abnormal kinesin family member 4A (KIF4A) expression has been implicated in ovarian cancer progression; however, the potential mechanism underlying KIF4A in ovarian cancer is not completely understood. The present study aimed to clarify the molecular basis of KIF4A in ovarian cancer. KIF4A and budding uninhibited by benzimidazoles 1 (BUB1) expression levels were detected via reverse transcription-quantitative PCR and western blotting. Cell Counting Kit-8, colony formation, wound healing, TUNEL and flow cytometry assays were performed to assess cell proliferation, migration, apoptosis and cell cycle distribution, respectively. Ki67 expression levels were detected by conducting immunofluorescence assays. The expression levels of migration- and apoptosis-related proteins were measured via western blotting. A co-immunoprecipitation assay was conducted to determine the association between KIF4A and BUB1. The results demonstrated that KIF4A was expressed at significantly higher levels in ovarian cancer cell lines compared with IOSE-80 cells. Compared with the short hairpin RNA-negative control group, KIF4A knockdown significantly inhibited cell viability, colony formation and migration, and markedly induced cell apoptosis. The results indicated that KIF4A could bind to BUB1 and regulate BUB1 expression. BUB1 overexpression weakened KIF4A knockdown-mediated effects on cell viability, colony formation, migration and apoptosis. Overall, the present study demonstrated that KIF4A knockdown suppressed ovarian cancer progression by regulating BUB1, and suggested the potential value of KIF4A and BUB1 as therapeutic targets for ovarian cancer.

KIF4A promotes the development of bladder cancer by transcriptionally activating the expression of CDCA3

Bladder cancer (BC) is among the most common urinary system tumors with a high morbidity and mortality worldwide. Despite advancements being made in the diagnosis and treatment of bladder cancer, targeted therapy remains the most promising treatment, and novel therapeutic targets are urgently required in to improve the outcomes of patients with BC. Kinesin family member 4A (KIF4A) is a plus-end directed motor protein involved in the regulation of multiple cellular processes, such as mitosis and axon growth. Notably, KIF4A plays important roles in tumor growth and progression, and its expression is associated with the prognosis of several types of cancer. However, the potential role and molecular mechanisms of KIF4A in bladder cancer development remain unclear. The present study demonstrated that KIF4A was highly expressed in human BC tissues, and its expression was associated with patient clinicopathological characteristics, such as tumor stage (P=0.012) and with the prognosis of patients with BC. It was further found that KIF4A promoted the cell proliferation of bladder cancer both in vitro and in vivo. On the whole, the data presented herein provide evidence that KIF4A promotes the development of BC through the transcriptional activation of the expression of CDCA3. The present study indicates the involvement of KIF4A in the progression of BC and suggests that KIF4A may be a promising therapeutic target for the treatment of BC.

Integrative analysis of transcriptional profile reveals LINC00052 as a suppressor of breast cancer cell migration

BACKGROUND

Long-non-coding RNAs, a class of transcripts with lengths > 200 nt, play key roles in tumour progression. Previous reports revealed that LINC00052 (long intergenic non-coding RNA 00052) was strongly downregulated during breast cancer multicellular spheroids formation and suggested a role in cell migration and oxidative metabolism.

OBJECTIVE

To examine the function of LINC00052 in MCF-7 breast cancer cells.

METHODS

Loss-of-function studies were performed to evaluate LINC00052 role on MCF-7 breast cancer cells. Microarray expression assays were performed to determine genes and cellular functions modified after LINC00052 knockdown. Next, the impact of LINC00052 depletion on MCF-7 cell respiration and migration was evaluated.

RESULTS

1,081 genes were differentially expressed upon LINC00052 inhibition. Gene set enrichment analysis, Gene Ontology and Key Pathway Advisor analysis showed that signalling networks related to cell migration and oxidative phosphorylation were enriched. However, whereas LINC00052 knockdown in MCF-7 cells revealed marginal difference in oxygen consumption rates when compared with control cells, LINC00052 inhibition enhanced cell migration in vitro and in vivo, as observed using a Zebrafish embryo xenotransplant model.

CONCLUSION

Our data show that LINC00052 modulates MCF-7 cell migration. Genome-wide microarray experiments suggest that cancer cell migration is affected by LINC00052 through cytoskeleton modulation and Notch/β-catenin/NF-κB signalling pathways.

High expression of TREM2 promotes EMT via the PI3K/AKT pathway in gastric cancer: bioinformatics analysis and experimental verification

Background: To date, the pathogenesis of gastric cancer (GC) remains unclear. We combined public database resources and bioinformatics analysis methods, explored some novel genes and verified the experiments to further understand the pathogenesis of GC and to provide a promising target for anti-tumor therapy. Methods: We downloaded the chip data related to GC from the Gene Expression Omnibus (GEO) database, extracted differentially expressed genes (DEGs), and then determined the key genes in the development of GC via PPI networks and model analysis. Functional annotation via GO and KEGG enrichment of DEGs was used to understand the latent roles of DEGs. The expression of the triggering receptor expressed on myeloid cells 2 (TREM2) gene in GC cell lines was verified via RT-PCR and western blotting. Moreover, the CCK-8, wound healing assay, and transwell migration and invasion assays were used to understand the changes in the proliferation, migration, and invasion abilities of GC cells after silencing TREM2. Western blotting verified the interaction between TREM2 and PI3K predict of the string website, as well as the effect of TREM2 on EMT. Finally, a lung metastasis model was used to explore the relationship between TREM2 and metastasis. Results: Our study identified 16 key genes, namely BGN, COL1A1, COL4A1, COL5A2, NOX4, SPARC, HEYL, SPP1, TIMP1, CTHRC1, TREM2, SFRP4, FBXO32, GPX3, KIF4A, and MMP9 genes associated with GC. The EMT-related pathway was the most significantly altered pathway. TREM2 expression was higher in GC cell lines and was remarkably associated with tumor invasion depth, TNM stage, histological grade, histological type, anatomic subdivision, and Helicobacter pylori state. Knockdown of TREM2 expression inhibited the proliferation, migration, and invasion of GC cells as well as the progression of EMT by PI3K/AKT signaling in vitro. In addition, lung metastasis were decreased in vivo. Conclusions: We identified some important genes associated with the progression of GC via public database analysis, explored and verified the effects of proto-oncogene TREM2 on EMT via the PI3K/AKT pathway. TREM2 may be a novel target in the GC therapy.

Transcriptome analysis identifies putative multi-gene signature distinguishing benign and malignant pancreatic head mass

Background

Most often, the patients with pancreatic diseases are presented with a mass in pancreatic head region and existing methods of diagnosis fail to confirm whether the head mass is malignant or benign. As subsequent management of the disease hugely depends on the correct diagnosis, we wanted to explore possible biomarkers which could distinguish benign and malignant pancreatic head masses.

Methods

In order to address that gap, we performed a case–control study to identify genome-wide differentially expressed coding and noncoding genes between pancreatic tissues collected from benign and malignant head masses. These genes were next shortlisted using stringent criteria followed by selection of top malignancy specific genes. They subsequently got validated by quantitative RT-PCR and also in other patient cohorts. Survival analysis and ROC analysis were also performed.

Results

We identified 55 coding and 13 noncoding genes specific for malignant pancreatic head masses. Further shortlisting and validation, however, resulted in 5 coding genes as part of malignancy specific multi-gene signature, which was validated in three independent patient cohorts of 145 normal and 153 PDAC patients. We also found that overexpression of these genes resulted in survival disadvantage in the patients and ROC analysis identified that combination of 5 coding genes had the AUROC of 0.94, making them potential biomarker.

Conclusions

Our study identified a multi-gene signature comprising of 5 coding genes (CDCA7, DLGAP5, FOXM1, TPX2 and OSBPL3) to distinguish malignant head masses from benign ones.

KIF4A is a promising prognostic marker and correlates with immune infiltration in clear cell renal cell carcinoma

Background

Kinesin family member 4A (KIF4A) belongs to the kinesin family. It has been found to promote the proliferation and invasion of tumor cells and correlates with the poor prognosis of different types of human cancers. However, the expression and prognostic value of KIF4A in clear cell renal cell carcinoma (ccRCC) and its correlation with tumor-infiltrating immune cells are currently unclear.

Methods

Here, we analyzed the expression data of KIF4A in different types of tumors on the TIMER database. The Kaplan-Meier curve was utilized to reveal the correlation between KIF4A expression and the clinical prognosis of ccRCC patients. UALCAN database was utilized to evaluate the relationship between KIF4A expression and the clinicopathological features of ccRCC patients. In addition, the correlation between KIF4A expression and the abundance of immune infiltrates, as well as gene markers of tumor-infiltrating immune cells was determined on the TIMER database.

Results

Various types of malignant tumors, including kidney renal clear cell carcinoma (KIRC), show high levels of KIF4A expression. The high expression of KIF4A was correlated with the worse prognosis, advanced clinical stage, poorer differentiation, and higher levels of immune infiltration in KIRC.

Conclusions

In summary, KIF4A is a promising prognostic marker and correlates with immune infiltration in clear cell renal cell carcinoma. However, further molecular and cellular experimental evidence is required to validate these conclusions.

Comprehensive analysis of transcriptome data stemness indices identifies key genes for controlling cancer stem cell characteristics in gastric cancer

BACKGROUND

Cancer stem cells (CSCs) are the tumor cell of origin with self-renewing ability and multi-differentiation potency. CSCs can play vital roles in gastric cancer (GC) metastasis and relapse. However, the genes that regulate the stemness maintenance of CSCs in GC patients remain largely unknown. In the present study, we sought to determine the key genes associated with stemness in GC patients.

METHODS

mRNA expression-based stemness index (mRNA SI) was analyzed with regard to the differential expression levels between normal and GC tissues, as well as clinical features and survival outcomes. Weighted gene co-expression network analysis (WGCNA) was performed to identify modules of interest and key genes. The differences in mRNA expression of key genes between normal and GC tissues were calculated by "ggpubr" package in R. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were carried out to annotate the function of key genes. Protein-protein interaction (PPI) and gene co-expression analyses were conducted using STRING and "corrplot" package in R, respectively.

RESULTS

mRNA SI score was markedly increased in GC tumor compared to normal tissues. High mRNA SI score was remarkably associated with more advanced tumor stage and higher pathologic grade, but longer survival times. Based on the results of WGCNA, 19 key genes (i.e., BUB1, BUB1B, KIF14, NCAPH, RACGAP1, KIF15, CENPF, TPX2, RAD54L, KIF18B, TTX, KIF4A, SGO2, PLK4, ARHGAP11A, XRCC2, C1orf112, NCAPG, ORC6) were identified. GO and KEGG functional analyses revealed that these 19 key genes were mainly related to cell proliferation. From PPI and gene co-expression analyses, these 19 key genes were discovered to be intensively associated with each other at both protein and transcription levels.

CONCLUSIONS

our study identified 19 key genes that play vital roles in the stemness maintenance of CSCs in GC patients. Targeting these key genes may help to control CSC characteristics in GC.

Identification by TCGA database search of five genes that are aberrantly expressed and involved in hepatocellular carcinoma potentially via DNA methylation changes

BACKGROUND

Various treatments for hepatocellular carcinoma (HCC) are utilized in clinical practice; however, the prognosis is still poor on account of high recurrence rates. DNA methylation levels of CpG islands around promoters (promoter CpGis) inversely regulate gene expression and closely involved in carcinogenesis. As a new strategy, several chemicals globally inhibiting DNA methylation have been developed aiming at reducing DNA methylation levels and maintaining the expression of tumor suppressor genes. On the other hand, since these drugs nonspecifically modify DNA methylation, they can cause serious adverse effects. In order to ameliorate the methods by targeting specific CpGs, information of cancer-related genes that are regulated by DNA methylation is required.

METHODS

We searched candidate genes whose expressions were regulated by DNA methylation of promoter CpGi and which are involved in HCC cases. To do so, we first identified genes whose expression were changed in HCC by comparing gene expressions of 371 HCC tissues and 41 non-tumor tissues using the Cancer Genome Atlas (TCGA) database. The genes were further selected for poor prognosis by log-rank test of Kaplan-Meier plot and for cancer relevance by Pubmed search. Expression profiles of upregulated genes in HCC tissues were assessed by Gene Ontology (GO) analysis. Finally, using DNA methylation data of TCGA database, we selected genes whose promoter DNA methylation levels were inversely correlated with gene expression.

RESULTS

We found 115 genes which were significantly up- or downregulated in HCC tissues and were associated with poor prognosis and cancer relevance. The upregulated genes were significantly enriched in cell division, cell cycle, and cell proliferation. Among the upregulated genes in HCC, we identified hypomethylation of CpGis around promoters of FANCB, KIF15, KIF4A, ERCC6L, and UBE2C. In addition, TCGA data showed that the tumor suppressor gene P16 is unexpectedly overexpressed in many types of cancers.

CONCLUSIONS

We identified five candidate genes whose expressions were regulated by DNA methylation of promoter CpGi and associate with cancer cases and poor prognosis in HCC. Modification of site-specific DNA methylation of these genes enables a different approach for HCC treatment with higher selectivity and lower adverse effects.

STAT1-induced upregulation of lncRNA LINC01123 predicts poor prognosis and promotes the progression of endometrial cancer through miR-516b/KIF4A

Long non-coding RNAs (lncRNAs) have been proposed as suppressors or promoters in many tumor processes. LncRNA LINC01123 (LINC01123) was a newly identified lncRNA which was firstly functionally analyzed in lung cancer. However, its expression and function in other tumor types were rarely reported. In this study, we firstly confirmed that LINC01123 was highly expressed in both endometrial cancer (EC) tissues and cell lines using bioinformatics analysis and RT-CPR. Then, we preliminarily analyzed the mechanisms involved in overexpression of LINC01123 in EC, finding that STAT1 could bind directly to the LINC01123 promoter region and activate its transcription. Clinical research with 106 patients indicated that high expression of LINC01123 was associated with advanced clinical progression and poor clinical outcome of EC patients. Functionally, knockdown of LINC01123 suppressed the proliferation, migration and invasion of EC cells, and promoted apoptosis. Mechanistically, we observed that LINC01123 may act as an endogenous sponge by competing for miR-516b, thereby regulating KIF4A. Overall, our study revealed a novel LINC01123/miR-516b/KIF4A pathway regulatory axis in EC pathogenesis. LINC01123 may be a novel prognostic biomarker and therapeutic target in EC.Abbreviations: EC: Endometrial cancer; LncRNA: Long non-coding RNA; EMT: epithelial-mesenchymal transition; miRNA: microRNA; qRT-PCR: Quantitative real-time polymerase chain reaction; SPSS: Statistical Package for Social Sciences; Chip: chromatin-immunoprecipitation, TCGA: The Cancer Genome Atlas; CCK-8: Cell Counting Kit-8; KIF4A: Chromosome-associated kinesin KIF4A.

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

The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC.

Identification of potential hub genes related to the progression and prognosis of hepatocellular carcinoma through integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related deaths among cancer patients. Genes correlated with the progression and prognosis of HCC are critically needed to be identified. In the present study, 3 Gene Expression Omnibus (GEO) datasets (GSE46408, GSE65372 and GSE84402) were used to analyze the differentially expressed genes (DEGs) between HCC and non-tumor liver tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to clarify the functional roles of DEGs. A protein-protein interaction network was established to screen the hub genes associated with HCC. The prognostic values of hub genes in HCC patients were analyzed using The Cancer Genome Atlas (TCGA) database. The expression levels of hub genes were validated based on ONCOMINE, TCGA and Human Protein Atlas (HPA) databases. Notably, 56 upregulated and 33 downregulated DEGs were markedly enriched under various GO terms and four KEGG terms. Among these DEGs, 10 hub genes with high connectivity degree were identified, including cyclin B1, cyclin A2, cyclin B2, condensin complex subunit 3, PDZ binding kinase, nucleolar and spindle-associated protein 1, aurora kinase A, ZW10 interacting kinetochore protein, protein regulator of cytokinesis 1 and kinesin family member 4A. The upregulated expression levels of these hub genes in HCC tissues were further confirmed by ONCOMINE, TCGA, and HPA databases. Additionally, the increased mRNA expression of each hub gene was related to the unfavorable disease-free survival and overall survival of HCC patients. The present study identified ten genes associated with HCC, which may help to provide candidate targets for the diagnosis and treatment of HCC.

Overexpression of CDCA5, KIF4A, TPX2, and FOXM1 Coregulated Cell Cycle and Promoted Hepatocellular Carcinoma Development

This study aimed to identify key functional modules and genes in functional module involved in hepatocellular carcinoma (HCC) development. The microarray data set GSE54236 was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between HCC, and normal samples were identified by Limma. DAVID was used to identify the gene ontology terms these genes enriched. The co-expression network was constructed based on Pearson correlation coefficient between gene expression values, and the functional modules these DEGs obviously enriched in were recognized through GraphWeb. Then, based on the genes related to the development of HCC, the DEGs interacting with HCC-associated genes were spotted. Finally, survival analysis and real-time quantitative polymerase chain reaction were performed. Totally, 427 upregulated (e.g., cell division cycle associated 5 [CDCA5], kinesin family member 4A [KIF4A], TPX2 microtubule nucleation factor [TPX2]) and 313 downregulated (e.g., metallothionein 1E [MT1E]) DEGs were identified in HCC. Besides, CDCA5, KIF4A, and TPX2 had interacting relationship and played important roles in HCC development by interrelating with HCC-related gene, forkhead box M1 (FOXM1). Furthermore, CDCA5, KIF4A, TPX2, and FOXM1 obviously enriched in cell cycle-related functional module, whereas MT1E enriched in mineral absorption module in Kyoto Encyclopedia of Genes and Genomes. CDCA5, KIF4A, and TPX2 expression were increased in HCC cells, and their high expressions were related to poor prognosis. Overexpression of CDCA5, KIF4A, TPX2, and FOXM1 coregulated cell cycle and thereby promoted the development of HCC. The finding provided potential targets for the study and treatment of HCC.

Identification of Flap endonuclease 1 as a potential core gene in hepatocellular carcinoma by integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is a common yet deadly form of malignant cancer. However, the specific mechanisms involved in HCC diagnosis have not yet fully elucidated. Herein, we screened four publically available Gene Expression Omnibus (GEO) expression profiles (GSE14520, GSE29721, GSE45267 and GSE60502), and used them to identify 409 differentially expressed genes (DEGs), including 142 and 267 up- and down-regulated genes, respectively. The DAVID database was used to look for functionally enriched pathways among DEGs, and the STRING database and Cytoscape platform were used to generate a protein-protein interaction (PPI) network for these DEGs. The cytoHubba plug-in was utilized to detect 185 hub genes, and three key clustering modules were constructed with the MCODE plug-in. Gene functional enrichment analyses of these three key clustering modules were further performed, and nine core genes including BIRC5, DLGAP5, DTL, FEN1, KIAA0101, KIF4A, MCM2, MKI67, and RFC4, were identified in the most critical cluster. Subsequently, the hierarchical clustering and expression of core genes in TCGA liver cancer tissues were analyzed using the UCSC Cancer Genomics Browser, and whether elevated core gene expression was linked to a poor prognosis in HCC patients was assessed using the GEPIA database. The PPI of the nine core genes revealed an interaction between FEN1, MCM2, RFC4, and BIRC5. Furthermore, the expression of FEN1 was positively correlated with that of three other core genes in TCGA liver cancer tissues. FEN1 expression in HCC and other tumor types was assessed with the FIREBROWSE and ONCOMINE databases, and results were verified in HCC samples and hepatoma cells. FEN1 levels were also positively correlated with tumor size, distant metastasis and vascular invasion. In conclusion, we identified nine core genes associated with HCC development, offering novel insight into HCC progression. In particular, the aberrantly elevated FEN1 may represent a potential biomarker for HCC diagnosis and treatment.

Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis

Background

Hepatocellular carcinoma (HCC) is a common malignant tumor affecting the digestive system and causes serious financial burden worldwide. Hepatitis B virus (HBV) is the main causative agent of HCC in China. The present study aimed to investigate the potential mechanisms underlying HBV-related HCC and to identify core biomarkers by integrated bioinformatics analyses.

Methods

In the present study, HBV-related HCC GSE19665, GSE55092, GSE94660 and GSE121248 expression profiles were downloaded from the Gene Expression Omnibus database. These databases contain data for 299 samples, including 145 HBV-related HCC tissues and 154 non-cancerous tissues (from patients with chronic hepatitis B). The differentially expressed genes (DEGs) from each dataset were integrated and analyzed using the RobustRankAggreg (RRA) method and R software, and the integrated DEGs were identified. Subsequently, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool, and the protein-protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Finally, hub genes were identified, and the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC.

Results

First, 341 DEGs (117 upregulated and 224 downregulated) were identified from the four datasets. Next, GO analysis showed that the upregulated genes were mainly involved in cell cycle, mitotic spindle, and adenosine triphosphate binding. The majority of the downregulated genes were involved in oxidation reduction, extracellular region, and electron carrier activity. Signaling pathway analysis showed that the integrated DEGs shared common pathways in retinol metabolism, drug metabolism, tryptophan metabolism, caffeine metabolism, and metabolism of xenobiotics by cytochrome P450. The integrated DEG PPI network complex comprised 288 nodes, and two important modules with high degree were detected using the MCODE plug-in. The top ten hub genes identified from the PPI network were SHCBP1, FOXM1, KIF4A, ANLN, KIF15, KIF18A, FANCI, NEK2, ECT2, and RAD51AP1. Finally, survival analysis revealed that patients with HCC showing altered ANLN and KIF18A expression profiles showed worse disease-free survival. Nonetheless, patients with FOXM1, NEK2, RAD51AP1, ANLN, and KIF18A alterations showed worse overall survival.

Conclusions

The present study identified key genes and pathways involved in HBV-related HCC, which improved our understanding of the mechanisms underlying the development and recurrence of HCC and identified candidate targets for the diagnosis and treatment of HBV-related HCC.

Genome‑wide investigation of the clinical significance and prospective molecular mechanisms of kinesin family member genes in patients with lung adenocarcinoma

The current study aimed to identify the potential clinical significance and molecular mechanisms of kinesin (KIF) family member genes in lung adenocarcinoma (LUAD) using genome-wide RNA sequencing (RNA-seq) datasets derived from The Cancer Genome Atlas (TCGA) database. Clinical parameters and RNA-seq data of patients with LUAD from the TCGA database enabled the assessment of the clinical significance of KIF genes, while the potential mechanisms of their interactions in LUAD were investigated by gene set enrichment analysis (GSEA). A gene signature with potential prognostic value was constructed via a stepwise multivariable Cox analysis. In total, 23 KIF genes were identified to be differentially expressed genes (DEGs) between the LUAD tumor and adjacent non-cancerous tissues. Of these, 8 differentially expressed KIF genes were strongly found to be strongly associated with the overall survival of patients with LUAD. Three of these genes were found to be able to be grouped as a potential prognostic gene signature. Patients with higher risk scores calculated using this gene signature were found to have a markedly higher risk of mortality (adjusted P=0.003; adjusted HR, 1.576; 95% CI, 1.166–2.129). Time-dependent receiver operating characteristic analysis indicated that this prognostic signature was able to accurately predict patient prognosis with an area under curve of 0.636, 0.643,0.665, 0.670 and 0.593 for the 1-, 2-, 3-, 4- and 5-year survival, respectively. This prognostic gene signature was identified as an independent risk factor for LUAD and was able to more accurately predict prognosis in comparison to other known clinical parameters, as shown via comprehensive survival analysis. GSEA enrichment revealed that that KIF14, KIF18B and KIF20A mediated basic cell physiology through the regulation of the cell cycle, DNA replication, and DNA repair biological processes and pathways. On the whole, the findings of this study identified 23 KIF genes that were DEGs between LUAD tumor and adjacent non-cancerous tissues. In total, 8 of these genes had the potential to function as prognostic and diagnostic biomarkers in patients with LUAD.

FOXM1 promotes hepatocellular carcinoma progression by regulating KIF4A expression

BACKGROUND

Forkhead box M1 (FOXM1) is a proliferation-associated transcription factor of the forkhead box proteins superfamily, which includes four isoforms FOXM1a, b, c, and d. FOXM1 has been implicated in hepatocellular carcinoma (HCC) progression, but the underlying molecular mechanism remains elusive. In this study, we aim to clarify the molecular basis for FOXM1-mediated HCC progression.

METHODS

Bioinformatic analysis was used to explore the differentially expressed genes predicting HCC proliferation. The expression of FOXM1 and kinesin family member (KIF)4A was confirmed by western blotting and immunohistochemistry in HCC tissues. Kaplan-Meier survival analysis was conducted to analyze the clinical impact of FOXM1 and KIF4A on HCC. The effect of FOXM1 on the regulation of KIF4A expression was studied in cell biology experiments. The interaction between KIF4A and FOXM1 was analyzed by chromatin immunoprecipitation and luciferase experiments. A series of experiments was performed to explore the functions of FOXM1/KIF4A in HCC progression, such as cell proliferation, cell growth, cell viability, and cell cycle. A xenograft mouse model was used to explore the regulatory effect of FOXM1-KIF4A axis on HCC tumor growth.

RESULTS

FOXM1 and KIF4A were overexpressed in human primary HCC tissues compared to that in matched adjacent normal liver tissue and are significant risk factors for HCC recurrence and shorter survival. We found that KIF4A was dominantly regulated by FOXM1c among the four isoforms, and further identified KIF4A as a direct downstream target of FOXM1c. Inhibiting FOXM1 decreased KIF4A expression in HCC cells, whereas its overexpression had the opposite effect. FOXM1-induced HCC cell proliferation was dependent on elevated KIF4A expression as KIF4A knockdown abolished FOXM1-induced proliferation of HCC cells both in vitro and in vivo.

CONCLUSION

The FOXM1-KIF4A axis mediates human HCC progression and is a potential therapeutic target for HCC treatment.

Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value

Glioblastoma is a common malignant tumor in the central nervous system with an extremely poor outcome; understanding the mechanisms of glioblastoma at the molecular level is essential for clinical treatment. In the present study, we used bioinformatics analysis to identify potential biomarkers associated with prognosis in glioblastoma and elucidate the underlying mechanisms. The result revealed that 552 common genes were differentially expressed between glioblastoma and normal tissues based on TCGA, GSE4290, and GSE 50161 datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network were carried out to gain insight into the actions of differentially expressed genes (DEGs). As a result, 20 genes (CALB1, CDC20, CDCA8, CDK1, CEP55, DLGAP5, KIF20A, KIF4A, NDC80, PBK, RRM2, SYN1, SYP, SYT1, TPX2, TTK, VEGFA, BDNF, GNG3, and TOP2A) were found as hub genes via CytoHubba in Cytoscape and functioned mainly by participating in cell cycle and p53 signaling pathway; among them, RRM2 and CEP55 were considered to have relationship with the prognosis of glioblastoma, especially RRM2. High expression of RRM2 was consistent with shorter overall survival time. In conclusion, our study displayed the bioinformatic analysis methods in screening potential oncogenes in glioblastoma and underlying mechanisms. What is more is that we successfully identified RRM2 as a novel biomarker linked with prognosis, which might be expected to be a promising target for the therapy of glioblastoma.

Alternatively activated NUSAP1 promotes tumor growth and indicates poor prognosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies with high mortality. The key genes involved in initiation and development of HCC is not entirely clear.

METHODS

We performed a meta-analysis of available transcriptome data from 6 independent HCC datasets [5 datasets from the Gene Expression Omnibus (GEO) and 1 dataset from The Cancer Genome Atlas (TCGA)]. The associations of the nucleolar and spindle-associated protein 1 (NUSAP1) expression level with clinicopathological factors and survival times were analyzed. Two representative HCC cell models were built to observe the proliferation capacity of HCC cells when NUSAP1 expression was inhibited by shNUSAP1.

RESULTS

Based on the transcriptome and survival data in the GEO and TCGA databases, NUSAP1 gene was markedly upregulated in HCC. High expression of NUSAP1 in HCC is related to the iCluster1 molecular subgroup, poor survival, poor tumor differentiation and TNM stage. Additionally, pathway analysis based on RNAseq data suggested that NUSAP1 could activate the expression of genes involves in cell proliferation. Furthermore, downregulation of NUSAP1 expression could significantly inhibit the proliferation of SMMC-7721 and Huh7 cells in vitro.

CONCLUSIONS

Our study provides evidence that NUSAP1 may serve as a candidate prognostic marker and a target for future therapeutic intervention in HCC.

Distinct Diagnostic and Prognostic Values of Kinesin Family Member Genes Expression in Patients with Breast Cancer

BACKGROUND This study investigated the diagnostic and prognostic values of kinesin superfamily proteins (KIFs) in breast cancer (BC) patients. MATERIAL AND METHODS All data were obtained from the Cancer Genome Atlas. DESeq was run to test for differentially expressed KIF genes. Patients were divided into high- and low-expression groups according to the median expression values of each KIF genes. Survival data were calculated using the Cox proportional hazard model. Comprehensive survival analysis was performed to evaluate the prognostic value of the prognostic signature. Gene set enrichment analysis (GSEA) was conducted to identify associated gene ontology and KEGG pathways. RESULTS Bioinformatics analysis showed that all KIF genes were significantly enriched during DNA replication and the cell cycle, and co-expressed with each other. Thirteen KIF genes were differentially expressed in cancer and adjacent tissues, and high levels of KIF15, KIF20A, KIF23, KIF2C and KIF4A genes were significantly correlated with poor overall survival (OS). GSEA showed that BC patients with high expression of KIF15, KIF20A, KIF23, KIF2C and KIF4A were enriched in the cell cycle process, P53 regulation pathway and mismatch repair. Combinations of low expression of KIF15, KIF20A, KIF23, KIF2C and KIF4A were more highly correlated with favorable OS. Nomograms showed that the KIF4A risk score provided the maximum number of risk points (range 0-100), whereas other genes made a lower contribution. CONCLUSIONS We conclude that 13 KIF genes are differentially expressed in BC tumor tissues, and KIF15, KIF20A, KIF23, KIF2C and KIF4A are associated with prognostic factors in BC.

An integrated bioinformatical analysis to evaluate the role of KIF4A as a prognostic biomarker for breast cancer

Purpose

The aim of this study was to investigate the diagnostic and prognostic value of human kinesin family member 4A (KIF4A) as an effective biomarker for breast cancer.

Materials and methods

Cancer Genome Atlas data and 12 independent public breast cancer microarray data sets were downloaded and analyzed using individual and pooled approaches.

Results

The results of our study revealed a strong and positive correlation between KIF4A expression and malignant features of breast cancer. KIF4A had a strong prognostic value in both ER-positive and ER-negative breast cancers comparable to or even better than tumor size, lymph node invasion, and Elston grade. We also found that KIF4A might be the target gene of microRNA-335, which can suppress KIF4A expression by targeting the 3′-untranslated region of its mRNA.

Conclusion

KIF4A might serve as a robust prognostic predictor for breast cancer. Targeting KIF4A activity could be a promising therapeutic option in breast cancer treatment.

The Identification of Core Gene Expression Signature in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies, which causes serious financial burden worldwide. This study aims to investigate the potential mechanisms contributing to HCC and identify core biomarkers. The HCC gene expression profile GSE41804 was picked out to analyze the differentially expressed genes (DEGs). Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out using DAVID. We constructed a protein-protein interaction (PPI) network to visualize interactions of the DEGs. The survival analysis of these hub genes was conducted to evaluate their potential effects on HCC. In this analysis, 503 DEGs were captured (360 downregulated genes and 143 upregulated genes). Meanwhile, 15 hub genes were identified. GO analysis showed that the DEGs were mainly enriched in oxidative stress, cell cycle, and extracellular structure. KEGG analysis suggested the DEGs were enriched in the absorption, metabolism, and cell cycle pathway. PPI network disclosed that the top3 modules were mainly enriched in cell cycle, oxidative stress, and liver detoxification. In conclusion, our analysis uncovered that the alterations of oxidative stress and cell cycle are two major signatures of HCC. TOP2A, CCNB1, and KIF4A might promote the development of HCC, especially in proliferation and differentiation, which could be novel biomarkers and targets for diagnosis and treatment of HCC.

Systematic analyses of a novel lncRNA-associated signature as the prognostic biomarker for Hepatocellular Carcinoma

Accumulating evidence implies that long noncoding RNAs (lncRNAs) play a crucial role in predicting survival for Hepatocellular carcinoma (HCC) patients. This study aims to capture the current research hotspots of HCC, based on the analysis of publications related to HCC research from 2013 to 2017, and to identify a novel lncRNA signature for HCC prognosis through the data mining in The Cancer Genome Atlas (TCGA). “Prognosis” and “biomarker” were located in the core of the HCC research hotspot. Moreover, long noncoding RNA was the top one research frontier in HCC research. The associations between survival outcome and the expression of lncRNAs were evaluated by the univariate and multivariate Cox proportional hazards regression analyses. Four lncRNAs (LINC00261, TRELM3P, GBP1P1, and CDKN2B‐AS1) were identified as significantly correlated with overall survival (OS). These four lncRNAs were gathered as a single prognostic signature. There was a significant positive correlation between HCC patients with low‐risk scores and overall survival (HR = 1.802, 95%CI [1.224‐2.652], P = .003). Further analysis suggested that the prognostic value of this four‐lncRNA signature was independent in clinical features. The enrichment analysis of prognostic lncRNA‐related gene was performed to find out the related pathways. Our study indicates that this novel lncRNA expression signature may be a useful biomarker of the prognosis for HCC patients, based on bioinformatics analysis.

KIF4A facilitates cell proliferation via induction of p21-mediated cell cycle progression and promotes metastasis in colorectal cancer

Kinesin family member 4A (KIF4A) was found to be implicated in the regulation of chromosome condensation and segregation during mitotic cell division, which is essential for eukaryotic cell proliferation. However, little is known about the role of KIF4A in colorectal carcinoma (CRC). This study explored the biological function of KIF4A in CRC progression and investigated the potential molecular mechanisms involved. Here, we found that KIF4A was remarkably upregulated in primary CRC tissues and cell lines compared with paired non-cancerous tissues and normal colorectal epithelium. Elevated expression of KIF4A in CRC tissues was significantly correlated with clinicopathological characteristics in patients as well as with shorter overall and disease-free cumulative survival. Multivariate Cox regression analysis revealed that KIF4A was an independent prognostic factor for poor survival in human CRC patients. Functional assays, including a CCK-8 cell proliferation assay, colony formation analysis, cancer xenografts in nude mice, cell cycle and apoptosis analysis, indicated that KIF4A obviously enhanced cell proliferation by promoting cell cycle progression in vitro and in vivo. Furthermore, gene set enrichment analysis, Luciferase reporter assays, and ChIP assays revealed that KIF4A facilitates cell proliferation via regulating the p21 promoter, whereas KIF4A had no effect on cell apoptosis. In addition, Transwell analysis indicated that KIF4A promotes migration and invasion in CRC. Taken together, these findings not only demonstrate that KIF4A contributes to CRC proliferation via modulation of p21-mediated cell cycle progression but also suggest the potential value of KIF4A as a clinical prognostic marker and target for molecular treatments.

Increased KIF4A expression is a potential prognostic factor in prostate cancer

The kinesin super-family protein (KIF) 4A gene is reported to be overexpressed and associated with poor clinical prognosis in human cancers; however, its clinical significance in prostate cancer (PCa) has not been well studied. The present study performed dataset analyses and revealed that KIF4A expression was significantly increased in castration-resistant PCa patients. Additionally, KIF4A expression was significantly highly expressed in PCa tissues compared with non-cancerous tissues, particularly in advanced PCa pathological stages. Upregulated KIF4A mRNA expression in PCa tissues was significantly correlated with shorter overall survival and prostate-specific antigen failure. Furthermore, both univariate and multivariate analyses revealed that upregulated KIF4A may predict poor biochemical recurrence (BCR)-free survival. The data suggested that KIF4A may play a key role in PCa progression. Notably, increased KIF4A expression may potentially predict poor BCR-free survival in PCa patients.