Overexpression of kinesin superfamily members as prognostic biomarkers of breast cancer

DEP domain containing 1B (DEPDC1B) exerts the tumor promoter in hepatocellular carcinoma through activating p53 signaling pathway via kinesin family member 23 (KIF23)

Hepatocellular carcinoma (HCC) is closely associated with chronic liver disease and possesses a high incidence. DEP domain containing 1B (DEPDC1B) expression has been found to be upregulated in HCC according to bioinformatics analysis. This paper sought to study the specific role of DEPDC1B in HCC. The data of DEPDC1B expression and individual overall survival in HCC and normal liver tissues were acquired from UALCAN database. The association between DEPDC1B and the downstream signal, kinesin family member 23 (KIF23), was determined using LinkedOmics and STRING database, and subsequently confirmed by co-immunoprecipitation assay. The expression levels of DEPDC1B and KIF23 in normal hepatic epithelial cells and HCC cell lines were assessed by RT-qPCR and Western blotting, respectively. Following transfection with small interference RNA-DEPDC1B, the influences of DEPDC1B knockdown on cell proliferation, colony formation, cell cycle, cell invasion, migration, and KIF23 expression were evaluated. In addition, the effects of KIF23 overexpression on the above aspects of HCC cells were also determined, as well as the expression level of p53 signaling-related proteins. The results indicated that DEPDC1B was highly expressed in HCC cells. DEPDC1B knockdown inhibited the proliferation, migration, invasion, cycle, and KIF23 expression in HCC cells. Moreover, KIF23 overexpression reversed the inhibitory effect of DEPDC1B knockdown in HCC cells and the activation of the p53 signaling. In conclusion, DEPDC1B knockdown exerts anti-cancer role in HCC by activating the p53 signaling through KIF23.

Kinesin family member 23 exerts a protumor function in breast cancer via stimulation of the Wnt/β-catenin pathway

Kinesin family member 23 (KIF23) has been described as one of the main genes that are associated with malignant transformation in numerous cancers. However, the exact significance of KIF23 in breast cancer has not been well-addressed. The present study was dedicated to the comprehensive investigation of KIF23 in breast cancer. Initial expression analysis through The Cancer Genome Atlas (TCGA) demonstrated high KIF23 levels in breast cancer compared with normal controls. These in silico data showing high levels of KIF23 in breast cancer were verified by assessing clinical specimens using real-time quantitative PCR and immunoblot assays. Moreover, a high KIF23 level was correlated with adverse clinical outcomes in breast cancer patients. Cellular functional experiments showed that the down-regulation of KIF23 affected the malignant behaviors of breast cancer cells in vitro, whereas the forced expression of KIF23 stimulated them. Mechanistic studies revealed that KIF23 restraint down-regulated the levels of phosphorylated glycogen synthetase kinase-3β (GSK-3β), β-catenin, cyclin D1 and c-myc in breast cancer cells, showing an inhibitory effect on the Wnt/β-catenin pathway. The suppression of GSK-3β was able to reverse KIF23-silencing-induced inactivation of the Wnt/β-catenin pathway. Inhibition of the Wnt/β-catenin pathway abolished KIF23 overexpression-mediated protumor effects in breast cancer. A xenograft assay confirmed the in vivo antitumor function of KIF23 inhibition. In conclusion, these findings suggest that KIF23 may exert a protumor function in breast cancer by stimulating the Wnt/β-catenin pathway. This work suggests that KIF23 has potential values for targeted therapy and prognosis in breast cancer.

Functional roles of the SHCBP1 and KIF23 interaction in modulating the cell-cycle and cisplatin resistance of head and neck squamous cell carcinoma

BACKGROUND

This study aimed to explore the functional roles of Shc SH2-domain-binding protein 1 (SHCBP1) and Kinesin Family Member 23 (KIF23) in HPV-negative head and neck squamous cell carcinoma (HNSCC).

METHODS

Bioinformatic analysis was conducted using data from The Cancer Genome Atlas (TCGA) and GSE103322. HNSCC cell lines were used for in vitro and in vivo analysis.

RESULTS

SHCBP1 upregulation was associated with unfavorable survival. SHCBP1 knockdown reduced cell proliferation and increased the cisplatin sensitivity of SCC9/SCC25 cells. SHCBP1 interacted with KIF23 via its Nesd homology domain (NHD) domain, which was important for its nucleus localization. SHCBP1 positively modulated KIF23 expression and activated phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt), extracellular signal regulated kinase (ERK)1/2, nuclear factor kappa B (NF/κB)-p65, and Wnt/β-catenin signaling. KIF23 knockdown abrogated cisplatin resistance induced by SHCBP1 overexpression.

CONCLUSION

SHCBP1 interacts with KIF23 and cooperatively regulates cell-cycle progression and cisplatin resistance of HNSCC tumor cells.

Identification of keygenes, miRNAs and miRNA-mRNA regulatory pathways for chemotherapy resistance in ovarian cancer

Background

Chemotherapy resistance, especially platinum resistance, is the main cause of poor prognosis of ovarian cancer. It is of great urgency to find molecular markers and mechanism related to platinum resistance in ovarian cancer.

Methods

One mRNA dataset (GSE28739) and one miRNA dataset (GSE25202) were acquired from Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) between platinum-resistant and platinum-sensitive ovarian cancer patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs were performed using the DAVID to present the most visibly enriched pathways. Protein–protein interaction (PPI) of these DEGs was constructed based on the information of the STRING database. Hub genes related to platinum resistance were visualized by Cytoscape software. Then, we chose seven interested hub genes to further validate using qRT-PCR in A2780 ovarian cancer cell lines. And, at last, the TF-miRNA-target genes regulatory network was predicted and constructed using miRNet software.

Results

A total of 63 upregulated DEGs, 124 downregulated DEGs, four upregulated miRNAs and six downregulated miRNAs were identified. From the PPI network, the top 10 hub genes were identified, which were associated with platinum resistance. Our further qRT-PCR showed that seven hub genes (BUB1, KIF2C, NUP43, NDC80, NUF2, CCNB2 and CENPN) were differentially expressed in platinum-resistant ovarian cancer cells. Furthermore, the upstream transcription factors (TF) for upregulated DE-miRNAs were SMAD4, NFKB1, SMAD3, TP53 and HNF4A. Three overlapping downstream target genes (KIF2C, STAT3 and BUB1) were identified by miRNet, which was regulated by hsa-miR-494.

Conclusions

The TF-miRNA–mRNA regulatory pairs, that is TF (SMAD4, NFKB1 and SMAD3)-miR-494-target genes (KIF2C, STAT3 and BUB1), were established. In conclusion, the present study is of great significance to find the key genes of platinum resistance in ovarian cancer. Further study is needed to identify the mechanism of these genes in ovarian cancer.

Development and Validation of a Robust Pyroptosis-Related Signature for Predicting Prognosis and Immune Status in Patients with Colon Cancer

BACKGROUND

Pyroptosis has been confirmed as a type of inflammatory programmed cell death in recent years. However, the prognostic role of pyroptosis in colon cancer (CC) remains unclear.

METHODS

Dataset TCGA-COAD which came from the TCGA portal was taken as the training cohort. GSE17538 from the GEO database was treated as validation cohorts. Differential expression genes (DEGs) between normal and tumor tissues were confirmed. Patients were classified into two subgroups according to the expression characteristics of pyroptosis-related DEGs. The LASSO regression analysis was used to build the best prognostic signature, and its reliability was validated using Kaplan-Meier, ROC, PCA, and t-SNE analyses. And a nomogram based on the multivariate Cox analysis was developed. The enrichment analysis was performed in the GO and KEGG to investigate the potential mechanism. In addition, we explored the difference in the abundance of infiltrating immune cells and immune microenvironment between high- and low-risk groups. And we also predicted the association of common immune checkpoints with risk scores. Finally, we verified the expression of the pyroptosis-related hub gene at the protein level by immunohistochemistry.

RESULTS

A total of 23 pyroptosis-related DEGs were identified in the TCGA cohort. Patients were classified into two molecular clusters (MC) based on DEGs. Kaplan-Meier survival analysis indicated that patients with MC1 represented significantly poorer OS than patients with MC2. 13 overall survival- (OS-) related DEGs in MCs were used to construct the prognostic signature. Patients in the high-risk group exhibited poorer OS compared to those in the low-risk group. Combined with the clinical features, the risk score was found to be an independent prognostic factor of CC patients. The above results are verified in the external dataset GSE17538. A nomogram was established and showed excellent performance. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the varied prognostic performance between high- and low-risk groups may be related to the immune response mediated by local inflammation. Further analysis showed that the high-risk group has stronger immune cell infiltration and lower tumor purity than the low-risk group. Through the correlation between risk score and immune checkpoint expression, T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) was predicted as a potential therapeutic target for the high-risk group.

CONCLUSION

The 13-gene signature was associated with OS, immune cells, tumor purity, and immune checkpoints in CC patients, and it could provide the basis for immunotherapy and predicting prognosis and help clinicians make decisions for individualized treatment.

Targeting lysosomes in human disease: from basic research to clinical applications

In recent years, accumulating evidence has elucidated the role of lysosomes in dynamically regulating cellular and organismal homeostasis. Lysosomal changes and dysfunction have been correlated with the development of numerous diseases. In this review, we interpreted the key biological functions of lysosomes in four areas: cellular metabolism, cell proliferation and differentiation, immunity, and cell death. More importantly, we actively sought to determine the characteristic changes and dysfunction of lysosomes in cells affected by these diseases, the causes of these changes and dysfunction, and their significance to the development and treatment of human disease. Furthermore, we outlined currently available targeting strategies: (1) targeting lysosomal acidification; (2) targeting lysosomal cathepsins; (3) targeting lysosomal membrane permeability and integrity; (4) targeting lysosomal calcium signaling; (5) targeting mTOR signaling; and (6) emerging potential targeting strategies. Moreover, we systematically summarized the corresponding drugs and their application in clinical trials. By integrating basic research with clinical findings, we discussed the current opportunities and challenges of targeting lysosomes in human disease.

Application of comprehensive bioinformatics approaches to reconnoiter crucial genes and pathways underpinning hepatocellular carcinoma: a drug repurposing endeavor

Hepatocellular carcinoma (HCC) is the fifth most common neoplasm in the world. Chronic inflammation of liver and associated wound healing processes collectively contribute to the development of cirrhosis which further progresses to dysplastic nodule and then to HCC. Etiological mediators and ongoing manipulations at cellular level in HCC are well established; however, key protein interactions and genetic alterations involved in stepwise hepatocarcinogenic pathways are seldom explored. This study aims to unravel novel targets of HCC and repurpose the FDA-approved drugs against the same. Genetic data pertinent to different stages of HCC were retrieved from GSE6764 dataset and analyzed via GEO2R. Subsequently, protein-protein interaction network analysis of differentially expressed genes was performed to identify the hub genes with significant interaction. Hub genes displaying higher interactions were considered as potential HCC targets and were validated thorough UALCAN and GEPIA databases. These targets were screened against FDA-approved drugs through molecular docking and dynamics simulation studies to capture the drugs with potential activity against HCC. Finally, cytotoxicity of the shortlisted drug was confirmed in vitro by MTT assay. CDC20 was identified as potential druggable target. Docking, binding energy calculations, and dynamic studies revealed significant interaction exhibited by Labetalol with CDC20. Further, in MTT assay, Labetalol demonstrated an IC₅₀ of 200.29 µg/ml in inhibiting the cell growth of HepG2 cell line. In conclusion, this study discloses a series of key genetic underpinnings of HCC and recommends the pertinence of labetalol as a potential repurposable drug against HCC.

Screening and predicted value of potential biomarkers for breast cancer using bioinformatics analysis

Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. Increasing molecular targets have been discovered for breast cancer prognosis and therapy. However, there is still an urgent need to identify new biomarkers. Therefore, we evaluated biomarkers that may aid the diagnosis and treatment of breast cancer. We searched three mRNA microarray datasets (GSE134359, GSE31448 and GSE42568) and identified differentially expressed genes (DEGs) by comparing tumor and non-tumor tissues using GEO2R. Functional and pathway enrichment analyses of the DEGs were performed using the DAVID database. The protein-protein interaction (PPI) network was plotted with STRING and visualized using Cytoscape. Module analysis of the PPI network was done using MCODE. The associations between the identified genes and overall survival (OS) were analyzed using an online Kaplan-Meier tool. The redundancy analysis was conducted by DepMap. Finally, we verified the screened HUB gene at the protein level. A total of 268 DEGs were identified, which were mostly enriched in cell division, cell proliferation, and signal transduction. The PPI network comprised 236 nodes and 2132 edges. Two significant modules were identified in the PPI network. Elevated expression of the genes Discs large-associated protein 5 (DLGAP5), aurora kinase A (AURKA), ubiquitin-conjugating enzyme E2 C (UBE2C), ribonucleotide reductase regulatory subunit M2(RRM2), kinesin family member 23(KIF23), kinesin family member 11(KIF11), non-structural maintenance of chromosome condensin 1 complex subunit G (NCAPG), ZW10 interactor (ZWINT), and denticleless E3 ubiquitin protein ligase homolog(DTL) are associated with poor OS of breast cancer patients. The enriched functions and pathways included cell cycle, oocyte meiosis and the p53 signaling pathway. The DEGs in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.

KIF2C Is a Novel Prognostic Biomarker and Correlated with Immune Infiltration in Endometrial Cancer

Endometrial cancer (EC) is commonly diagnosed cancer in women, and the prognosis of advanced types of EC is extremely poor. Kinesin family member 2C (KIF2C) has been reported as an oncogene in cancers. However, its pathophysiological roles and the correlation with tumor-infiltrating lymphocytes in EC remain unclear. The mRNA and protein levels of KIF2C in EC tissues were detected by qRT-PCR, Western blot (WB), and IHC. CCK8, Transwell, and colony formation assay were applied to assess the effects of KIF2C on cell proliferation, migration, and invasion. Cell apoptosis and cell cycle were analyzed by flow cytometry. The antitumor effect was further validated in the nude mouse xenograft cancer model and humanized mouse model. KIF2C expression was higher in EC. Knockdown of KIF2C prolonged the G1 phases and inhibited EC cell proliferation, migration, and invasion in vitro. Bioinformatics analysis indicated that KIF2C is negatively correlated with the infiltration level of CD8⁺ T cells but positively with the poor prognosis of EC patients. The apoptosis of CD8⁺ T cell was inhibited after the knockdown of KIF2C and was further inhibited when it is combined with anti-PD1. Conversely, compared to the knockdown of KIF2C expression alone, the combination of anti-PD1 further promoted the apoptosis of Ishikawa and RL95-2 cells. Moreover, the knockdown of KIF2C inhibited the expression of Ki-67 and the growth of tumors in the nude mouse xenograft cancer model. Our study found that the antitumor efficacy was further evaluated by the combination of anti-PD1 and KIF2C knockdown in a humanized mouse model. This study indicated that KIF2C is a novel prognostic biomarker that determines cancer progression and also a target for the therapy of EC and correlated with tumor immune cells infiltration in EC.

Prognostic Impact and Functional Annotations of KIF11 and KIF14 Expression in Patients with Colorectal Cancer

Genomic instability (GIN) has an important contribution to the pathology of colorectal cancer (CRC). Therefore, we selected mitosis and cytokinesis kinesins, KIF11 and KIF14, as factors of potential clinical and functional value in CRC, as their aberrant expression has been suspected to underlie GIN. We examined the expression and the prognostic and biological significance of KIF11 and KIF14 in CRC via in-house immunohistochemistry on tissue microarrays, public mRNA expression datasets, as well as bioinformatics tools. We found that KIF11 and KIF14 expression, at both the protein and mRNA level, was markedly altered in cancer tissues compared to respective controls, which was reflected in the clinical outcome of CRC patients. Specifically, we provide the first evidence that KIF11 protein and mRNA, KIF14 mRNA, as well as both proteins together, can significantly discriminate between CRC patients with better and worse overall survival independently of other relevant clinical risk factors. The negative prognostic factors for OS were high KIF11 protein, high KIF11 protein + low KIF14 protein, low KIF11 mRNA and low KIF14 mRNA. Functional enrichment analysis revealed that the gene sets related to the cell cycle, DNA replication, DNA repair and recombination, among others, were positively associated with KIF11 or KIF14 expression in CRC tissues. In TCGA cohort, the positive correlations between several measures related to GIN and the expression of KIFs were also demonstrated. In conclusion, our results suggest that CRC patients can be stratified into distinct risk categories by biological and molecular determinants, such as KIF11 and KIF14 expression and, mechanistically, this is likely attributable to their role in maintaining genome integrity.

Silencing of KIF18B restricts proliferation and invasion and enhances the chemosensitivity of breast cancer via modulating Akt/GSK-3β/β-catenin pathway

Kinesin family member 18B (KIF18B) is a new tumor-associated protein that contributes to the carcinogenesis of multiple malignancies. However, the detailed relevance of KIF18B in breast cancer has not been fully elucidated. This work aimed was to evaluate a possible relationship between KIF18B and breast cancer progression. Our findings show KIF18B is increased in breast cancer and demonstrate that high KIF18B level predicts a reduced survival rate. Cellular functional studies revealed that knockdown of KIF18B markedly reduces the proliferation, invasion, and epithelial-mesenchymal transition of breast cancer cells and enhances their chemosensitivity toward doxorubicin. Further studies showed that KIF18B modulates the level of phospho-Akt, phospho-glycogen synthase kinase-3β, and β-catenin. Notably, suppression of Akt abolished KIF18B-overexpression-induced increases in activation of Wnt/β-catenin pathway. In addition, re-expression of β-catenin reversed KIF18B-silencing-induced cancer-promoting effect. In vivo animal experiments elucidated that knockdown of KIF18B significantly weakened the tumorigenicity of breast cancer cells. Taken together, data of this study illustrate that KIF18B exerts a potential cancer-promoting function in breast cancer via enhancement of Wnt/β-catenin pathway through modulation of the Akt/GSK-3β axis.

Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

BACKGROUND

Recent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand.

RESULTS

In this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1, and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression.

CONCLUSIONS

We established a predictive five-gene signature, which could be helpful for a personalized management in breast cancer patients.

DSN1 is a prognostic biomarker and correlated with clinical characterize in breast cancer

DSN1 affects cell cycle progression and is associated with clinical-pathological features in colorectal and hepatocellular carcinomas. However, the biological function of DSN1 in breast cancer is still indistinct. In this study, we comprehensively analyzed the correlation between DSN1 expression in different molecular subtypes or stages of breast cancer and investigated the prognostic value of DSN1 in databases such as Oncomine, Cancer Cell Line Encyclopedia, UALCAN, Human Protein Atlas, Kaplan-Meier Plotter, OncoLnc, GEPIA. Moreover, we investigated the correlation of DSN1 with tumor-infiltrating immune cells in the different tumor microenvironments via Tumor Immune Estimation Resource database and explore DSN1 co-expression networks in breast cancer via LinkedOmics analysis, NetworkAnalyst database analysis. Finally, we also performed our immunohistochemical experiments to explore the expression of DSN1 in different stages or subtypes of breast cancer. The findings in this article shed light on the essential role of DSN1 in breast cancers as well as suggested that the upregulation of DSN1 expression was strongly associated with poor prognosis and decreased survival in breast cancer, and there were significant differences in its expression in different pathological subtypes and stages of breast cancer.

Identification of genes and pathways related to breast cancer metastasis in an integrated cohort

BACKGROUND

Breast cancer is the most common malignant disease in women. Metastasis is the most common cause of death from this cancer. Screening genes related to breast cancer metastasis may help elucidate the mechanisms governing metastasis and identify molecular targets for antimetastatic therapy. The development of advanced algorithms enables us to perform cross-study analysis to improve the robustness of the results.

MATERIALS AND METHODS

Ten data sets meeting our criteria for differential expression analyses were obtained from the Gene Expression Omnibus (GEO) database. Among these data sets, five based on the same platform were formed into a large cohort using the XPN algorithm. Differentially expressed genes (DEGs) associated with breast cancer metastasis were identified using the differential expression via distance synthesis (DEDS) algorithm. A cross-platform method was employed to verify these DEGs in all ten selected data sets. The top 50 validated DEGs are represented with heat maps. Based on the validated DEGs, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein interaction (PPI) networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect breast cancer patient prognosis.

RESULTS

A total of 817 DEGs were identified using the DEDS algorithm. Of these DEGs, 450 genes were validated by the second algorithm. Enriched KEGG pathway terms demonstrated that these 450 DEGs may be involved in the cell cycle and oocyte meiosis in addition to their functions in ECM-receptor interaction and protein digestion and absorption. PPI network analysis for the proteins encoded by the DEGs indicated that these genes may be primarily involved in the cell cycle and extracellular matrix. In particular, several genes played roles in multiple signalling pathways and were related to patient survival. These genes were also observed to be targetable in the CTD2 database.

CONCLUSIONS

Our study analysed multiple cross-platform data sets using two different algorithms, helping elucidate the molecular mechanisms and identify several potential therapeutic targets of metastatic breast cancer. In addition, several genes exhibited promise for applications in targeted therapy against metastasis in future research.

Identification of differentially expressed genes-related prognostic risk model for survival prediction in breast carcinoma patients

Since the imbalance of gene expression has been demonstrated to tightly related to breast cancer (BRCA) genesis and growth, common genes expressed of BRCA were screened to explore the essence in-between. In current work, most common differentially expressed genes (DEGs) in various subtypes of BRCA were identified. Functional enrichment analysis illustrated the driving factor of deactivation of the cell cycle and the oocyte meiosis, which critically triggers the development of BRCA. Herein, we constructed a 12-gene prognostic risk model relative to differential gene expression. Subsequently, the K-M curves, analysis on time-ROC curve and Cox regression were performed to assess this risk model by determining the respective prognostic value, and the prediction performance were ascertained for both training and validation cohorts. In addition, multivariate Cox regression was analysed to reveal the independence between risk score and prognostic stage, and the accuracy and sensitivity of prognosis are particularly improved after clinical indicators are included into the analysis. In summary, this study offers novel insights into the imbalance of gene expression within BRCA, and highlights 12 selected genes associated with patient prognosis. The risk model can help individualize treatment for patients at different risks, and propose precise strategies and treatments for BRCA therapy.

In silico analysis of differentially expressed genesets in metastatic breast cancer identifies potential prognostic biomarkers

BACKGROUND

Identification of specific biological functions, pathways, and appropriate prognostic biomarkers is essential to accurately predict the clinical outcomes of and apply efficient treatment for breast cancer patients.

METHODS

To search for metastatic breast cancer-specific biological functions, pathways, and novel biomarkers in breast cancer, gene expression datasets of metastatic breast cancer were obtained from Oncomine, an online data mining platform. Over- and under-expressed genesets were collected and the differentially expressed genes were screened from four datasets with large sample sizes (N > 200). They were analyzed for gene ontology (GO), KEGG pathway, protein-protein interaction, and hub gene analyses using online bioinformatic tools (Enrichr, STRING, and Cytoscape) to find enriched functions and pathways in metastatic breast cancer. To identify novel prognostic biomarkers in breast cancer, differentially expressed genes were screened from the entire twelve datasets with any sample sizes and tested for expression correlation and survival analyses using online tools such as KM plotter and bc-GenExMiner.

RESULTS

Compared to non-metastatic breast cancer, 193 and 144 genes were differentially over- and under-expressed in metastatic breast cancer, respectively, and they were significantly enriched in regulating cell death, epidermal growth factor receptor signaling, and membrane and cytoskeletal structures according to the GO analyses. In addition, genes involved in progesterone- and estrogen-related signalings were enriched according to KEGG pathway analyses. Hub genes were identified via protein-protein interaction network analysis. Moreover, four differentially over-expressed (CCNA2, CENPN, DEPDC1, and TTK) and three differentially under-expressed genes (ABAT, LRIG1, and PGR) were further identified as novel biomarker candidate genes from the entire twelve datasets. Over- and under-expressed biomarker candidate genes were positively and negatively correlated with the aggressive and metastatic nature of breast cancer and were associated with poor and good prognosis of breast cancer patients, respectively.

CONCLUSIONS

Transcriptome datasets of metastatic breast cancer obtained from Oncomine allow the identification of metastatic breast cancer-specific biological functions, pathways, and novel biomarkers to predict clinical outcomes of breast cancer patients. Further functional studies are needed to warrant validation of their roles as functional tumor-promoting or tumor-suppressing genes.

Identification of genes modulated by interferon gamma in breast cancer cells

Interferon gamma (IFNγ) plays a context-dependent dual tumor-suppressor and pro-tumorigenic roles in cancer. IFNγ induces morphological changes in breast cancer (BC) cells with or without estrogen receptor alpha (ERα) expression. However, IFNγ-regulated genes in BC cells remain unexplored. Here, we performed a cDNA microarray analysis of MCF-7 (ERα+) and MDA-MB-231 (HER2-/PR-/ERα-) cells with and without IFNγ treatment. We identified specific IFNγ−modulated genes in each cell type, and a small group of genes regulated by IFNγ common in both cell types. IFNγ treatment for an extended time mainly repressed gene expression shared by both cell types. Nonetheless, some of these IFNγ-repressed genes were seemingly deregulated in human mammary tumor samples, along with decreased IFNGR1 (an IFNγ receptor) expression. Thus, IFNγ signaling-elicited anti-tumor activities may be mediated by the downregulation of main IFNγ target genes in BC; however, it may be deregulated by the tumor microenvironment in a tumor stage-dependent manner.

•

Identification of new potential genes regulated by IFNγ in breast cancer cells.

•

A small group of common genes is regulated by IFNγ in ERα- and ERα+ breast cancer cells.

•

IFNγ treatment for a long time mainly represses gene expression in breast cancer cells.

•

The tumor environment may lead to a decrease in IFNGR1 expression in mammary tumors.

KIF23 promotes triple negative breast cancer through activating epithelial-mesenchymal transition

Background

KIF23 is a member of kinesin family, recent researches indicate KIF23 plays an important role in the proliferation and migration of malignant cancer cells. While the function and specific molecule mechanism of KIF23 in triple negative breast cancer remains unclear.

Methods

QRT-PCR and immunohistochemistry were conducted to analyze expression of KIF23 in triple negative breast cancer tissues and paired paracancer tissues. CCK-8 assay, colony formation assay, wound healing assay and transwell assay were applied for exploring phenotype changing of triple negative breast cancer cell lines MDA-MB-231 and BT549 after siRNA-induced knockdown of KIF23. Several bioinformatic databases were used for predicting miRNAs that combing with KIF23 mRNA and verified by dual luciferase reporter assay. Western blot assay was performed to explore downstream signaling pathway of KIF23.

Results

KIF23 was overexpressed in triple negative breast cancer, knockdown of KIF23 by siRNA inhibited proliferation and migration of TNBC cell lines MDA-MB-231 and BT549. Mechanistically, knockdown of KIF23 resulted in the suppression of Epithelial-Mesenchymal Transition. Meanwhile, miR-195-5p was downregulated in TNBC, and dual luciferase reporter assay indicated miR-195-5p could combine with 3'UTR of KIF23 thus promoting degradation of KIF23.

Conclusions

KIF23 is a potential oncogene in triple negative breast cancer, miR-195-5p could combine with 3'UTR of KIF23. Our study reveals a new sight into triple negative breast cancer.

Substratum stiffness signals through integrin-linked kinase and β1-integrin to regulate midbody proteins and abscission during EMT

Abscission is the final stage of cytokinesis during which the parent cell physically separates to yield two identical daughters. Failure of abscission results in multinucleation (MNC), a sign of genomic instability and a precursor to aneuploidy, enabling characteristics of neoplastic progression. Induction of epithelial-mesenchymal transition (EMT) causes MNC in mammary epithelial cells cultured on stiff microenvironments that have mechanical properties similar to those found in breast tumors, but not on soft microenvironments reminiscent of the normal mammary gland. Here we report that on stiff microenvironments, EMT signaling through Snail up-regulates the midbody-associated proteins septin-6, Mklp1, and anillin, leading to abscission failure and MNC. To uncover the mechanism by which stiff microenvironments promote MNC in cells undergoing EMT, we investigated the role of cell-matrix adhesion through β1-integrin and integrin-linked kinase (ILK). We found that ILK expression, but not kinase activity, is required for EMT-associated MNC in cells on stiff microenvironments. Conversely, increasing focal adhesions by expressing an autoclustering mutant of β1-integrin promotes MNC in cells on soft microenvironments. Our data suggest that signaling through focal adhesions causes failure of cytokinesis in cells actively undergoing EMT. These results highlight the importance of tissue mechanics and adhesion in regulating the cellular response to EMT inducers.

Microtubule-Associated Protein ATIP3, an Emerging Target for Personalized Medicine in Breast Cancer

Breast cancer is the leading cause of death by malignancy among women worldwide. Clinical data and molecular characteristics of breast tumors are essential to guide clinician’s therapeutic decisions. In the new era of precision medicine, that aims at personalizing the treatment for each patient, there is urgent need to identify robust companion biomarkers for new targeted therapies. This review focuses on ATIP3, a potent anti-cancer protein encoded by candidate tumor suppressor gene MTUS1, whose expression levels are markedly down-regulated in breast cancer. ATIP3 is a microtubule-associated protein identified both as a prognostic biomarker of patient survival and a predictive biomarker of breast tumors response to taxane-based chemotherapy. We present here recent studies pointing out ATIP3 as an emerging anti-cancer protein and a potential companion biomarker to be combined with future personalized therapy against ATIP3-deficient breast cancer.

A narrative review of the roles of muscle segment homeobox transcription factor family in cancer

Deregulation of many homeobox genes has been observed in various cancers and has caused functional implications in the tumor progression. In this review, we will focus on the roles of the human muscle segment homeobox (MSX) transcription factor family in the process of tumorigenesis. The MSX transcription factors, through complex downstream regulation mechanisms, are promoters or inhibitors of diverse cancers by participating in cell proliferation, cell invasion, cell metastasis, cell apoptosis, cell differentiation, drug resistance of tumors, maintenance of tumor stemness, and tumor angiogenesis. Moreover, their upstream regulatory mechanisms in cancers may include: gene mutation and chromosome aberration; DNA methylation and chromatin modification; regulation by non-coding RNAs; regulation by other transcription factors and post-translational modification. These mechanisms may provide a better understanding of why MSX transcription factors are abnormally expressed in tumors. Notably, intermolecular interactions and post-translational modification can regulate the transcriptional activity of MSX transcription factors. It is also crucial to know what affects the transcriptional activity of MSX transcription factors in tumors for possible interventions in them in the future. This systematic summary of the regulatory patterns of the MSX transcription factor family may help to further understand the mechanisms involved in transcriptional regulation and also provide new therapeutic approaches for tumor progression.

Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0-81.4% and 74.6-78% respectively (rfm, ACC 63.2-65.5%, AUC 61.9-74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10^-8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

Identification of potential markers for differentiating epithelial ovarian cancer from ovarian low malignant potential tumors through integrated bioinformatics analysis

Background

Epithelial ovarian cancer (EOC), as a lethal malignancy in women, is often diagnosed as advanced stages. In contrast, intermediating between benign and malignant tumors, ovarian low malignant potential (LMP) tumors show a good prognosis. However, the differential diagnosis of the two diseases is not ideal, resulting in delays or unnecessary therapies. Therefore, unveiling the molecular differences between LMP and EOC may contribute to differential diagnosis and novel therapeutic and preventive policies development for EOC.

Methods

In this study, three microarray data (GSE9899, GSE57477 and GSE27651) were used to explore the differentially expressed genes (DEGs) between LMP and EOC samples. Then, 5 genes were screened by protein–protein interaction (PPI) network, receiver operating characteristic (ROC), survival and Pearson correlation analysis. Meanwhile, chemical-core gene network construction was performed to identify the potential drugs or risk factors for EOC based on 5 core genes. Finally, we also identified the potential function of the 5 genes for EOC through pathway analysis.

Results

Two hundred thirty-four DEGs were successfully screened, including 81 up-regulated genes and 153 down-regulated genes. Then, 5 core genes (CCNB1, KIF20A, ASPM, AURKA, and KIF23) were identified through PPI network analysis, ROC analysis, survival and Pearson correlation analysis, which show better diagnostic efficiency and higher prognostic value for EOC. Furthermore, NetworkAnalyst was used to identify top 15 chemicals that link with the 5 core genes. Among them, 11 chemicals were potential drugs and 4 chemicals were risk factors for EOC. Finally, we found that all 5 core genes mainly regulate EOC development via the cell cycle pathway by the bioinformatic analysis.

Conclusion

Based on an integrated bioinformatic analysis, we identified potential biomarkers, risk factors and drugs for EOC, which may help to provide new ideas for EOC diagnosis, condition appraisal, prevention and treatment in future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00794-0.

Silencing of KIF3B Suppresses Breast Cancer Progression by Regulating EMT and Wnt/β-Catenin Signaling

Breast cancer is the most common malignant tumors in women. Kinesin family member 3B (KIF3B) is a critical regulator in mitotic progression. The objective of this study was to explore the expression, regulation, and mechanism of KIF3B in 103 cases of breast cancer tissues, 35 metastatic lymph nodes and breast cancer cell lines, including MDA-MB-231, MDA-MB-453, T47D, and MCF-7. The results showed that KIF3B expression was up-regulated in breast cancer tissues and cell lines, and the expression level was correlated with tumor recurrence and lymph node metastasis, while knockdown of KIF3B suppressed cell proliferation, migration, and invasion both in vivo and in vitro. In addition, UALCAN analysis showed that KIF3B expression in breast cancer is increased, and the high expression of KIF3B in breast cancer is associated with poor prognosis. Furthermore, we found that silencing of KIF3B decreased the expression of Dvl2, phospho-GSK-3β, total and nucleus β-catenin, then subsequent down-regulation of Wnt/β-catenin signaling target genes such as CyclinD1, C-myc, MMP-2, MMP-7 and MMP-9 in breast cancer cells. In addition, KIF3B depletion inhibited epithelial mesenchymal transition (EMT) in breast cancer cells. Taken together, our results revealed that KIF3B is up-regulated in breast cancer which is potentially involved in breast cancer progression and metastasis. Silencing KIF3B might suppress the Wnt/β-catenin signaling pathway and EMT in breast cancer cells.

Kinesin Family Member C1 (KIFC1) Regulated by Centrosome Protein E (CENPE) Promotes Proliferation, Migration, and Epithelial-Mesenchymal Transition of Ovarian Cancer

Background

Centrosome amplification is recognized as a hallmark of cancer. Kinesin family member C1 (KIFC1), a centrosome-clustering molecule, is essential for the viability of extra centrosome-bearing cancer cells and may be the basis for the progression of ovarian cancer. However, its biological function and mechanism in ovarian cancer have not yet been studied.

Material/Methods

Quantitative reverse-transcription polymerase chain reaction was performed to detect the levels of KIFC1 and centrosome protein E (CENPE). Further, cell viability was analyzed with CCK-8 assay, and immunofluorescence was used to measure the expression of Ki67 and PCNA. Cell migration was analyzed with wound healing and transwell assays. Western blot analysis was performed to measure the expression of proteins in ovarian cancer cells. The relationship between KIFC1 and CENPE was investigated by performing co-immunoprecipitation.

Results

KIFC1 was upregulated in ovarian cancer cells, especially in SKOV3 cells. Additionally, we found that KIFC1 silencing in SKOV3 cells inhibited cell proliferation and downregulated the expression of Ki67 and PCNA. Further, the knockdown of KIFC1 suppressed cell migration and epithelial-mesenchymal transition (EMT) and regulated the expression of matrix metalloproteinase (MMP)2, MMP9, E-cadherin, N-cadherin, Snail, and ZEB1. Next, we found that KIFC1 bound to and positively regulated CENPE, a tumor promoter in certain human cancers. All the suppressive effects triggered by KIFC1 inhibition were reversed by CENPE overexpression.

Conclusions

KIFC1 contributed to cell proliferation, migration, and EMT via interacting with CENPE in ovarian cancer. KIFC1 might be a potential biomarker and therapeutic target in ovarian cancer patients.

Kinesin Superfamily Member 18B (KIF18B) Promotes Cell Proliferation in Colon Adenocarcinoma

BACKGROUND

The role of kinesin superfamily proteins (KIFs) has been reported in a variety of tumors and KIFs contributed to the proliferation of cancer cells. But few studies were focus on colon adenocarcinoma.

METHODS

Through bioinformatics analysis and immunohistochemistry (IHC) assays, the expression of KIF18B in colon adenocarcinoma tissues was determined. Stable KIF18B-depleted cell lines were constructed using lentivirus-mediated shRNA of KIF18B. Cell colony formation assay and CCK8 assay were performed to assess cell proliferation degree, and the expression level of KI67 and PCNA was used to indicate cell proliferation in vitro and verified using xenograft tumors in vivo.

RESULTS

KIF18B is highly expressed in colon adenocarcinoma tissues and has a negative correlation with the prognosis and tumor grade of colon adenocarcinoma. Interfering with KIF18B inhibits cell proliferation in vitro and in vivo.

CONCLUSION

KIF18B can be used as a prognostic marker for colon adenocarcinoma and may be a therapeutic target for colon adenocarcinoma treatment.

miR-29c-3p regulates proliferation and migration in ovarian cancer by targeting KIF4A

Background

Increasing evidence suggested that microRNA and kinesin superfamily proteins play an essential role in ovarian cancer. The association between KIF4A and ovarian cancer (OC) was investigated in this study.

Methods

We performed bioinformatics analysis in the GEO database to screen out the differentially expressed miRNAs (DEmiRNAs) associated with ovarian cancer prognosis. Upstream targeting prediction for KIF4A was acquired by using the mirDIP database. The potential regulatory factor miR-29c-3p for KIF4A was obtained from the intersection of the above all miRNAs. The prognosis of KIF4A and target-miRNA in OC was obtained in the subsequent analysis. qRT-PCR and Western blot detected KIF4A expression level in IOSE80 (human normal ovarian epithelial cell line). In the meantime, the gene expression level was detected in A2780, HO-8910PM, COC1, and SKOV3 cell lines (human ovarian carcinoma cell line). MTT and colony formation assays were used to detect cell proliferation of SKOV3 cell line. The following assays detected cell migration through the use of transwell and wound heal assays. Targeted binding relationship between KIF4A and miRNA was detected by using the dual-luciferase reporter assay.

Results

Both high expression of KIF4A and lower expression of miR-29c-3p could be used as biomarkers indicating poor prognosis in OC patients. Cellular function tests confirmed that when KIF4A was silenced, it inhibited the proliferation and migration of OC cells. In addition, 3′-UTR of KIF4A had a direct binding site with miR-29c-3p, which indicated that the expression of KIF4A could be regulated by miR-29c-3p. In subsequent assays, the proliferation and migration of OC cells were inhibited by the overexpression of miR-29c-3p. At the same time, rescue experiments also confirmed that the promotion of KIF4A could be reversed by miR-29c-3p.

Conclusion

In a word, our data revealed a new mechanism for the role of KIF4A in the occurrence and development of OC.

Silencing KIF18B enhances radiosensitivity: identification of a promising therapeutic target in sarcoma

Background

Sarcomas are rare heterogeneous tumours, derived from primitive mesenchymal stem cells, with more than 100 distinct subtypes. Radioresistance remains a major clinical challenge for sarcomas, demanding urgent for effective biomarkers of radiosensitivity.

Methods

The radiosensitive gene Kinesin family member 18B (KIF18B) was mined through bioinformatics with integrating of 15 Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) database. We used radiotherapy-sh-KIF18B combination to observe the anti-tumour effect in sarcoma cells and subcutaneous or orthotopic xenograft models. The KIF18B-sensitive drug T0901317 (T09) was further mined to act as radiosensitizer using the Genomics of Drug Sensitivity in Cancer (GDSC) database.

Findings

KIF18B mRNA was significantly up-regulated in most of the subtypes of bone and soft tissue sarcoma. Multivariate Cox regression analysis showed that KIF18B high expression was an independent risk factor for prognosis in sarcoma patients with radiotherapy. Silencing KIF18B or using T09 significantly improved the radiosensitivity of sarcoma cells, delayed tumour growth in subcutaneous and orthotopic xenograft model, and elongated mice survival time. Furthermore, we predicted that T09 might bind to the structural region of KIF18B to exert radiosensitization.

Interpretation

These results indicated that sarcomas with low expression of KIF18B may benefit from radiotherapy. Moreover, the radiosensitivity of sarcomas with overexpressed KIF18B could be effectively improved by silencing KIF18B or using T09, which may provide promising strategies for radiotherapy treatment of sarcoma.

Fundings

A full list of funding can be found in the Funding Sources section.

LINC00649 underexpression is an adverse prognostic marker in acute myeloid leukemia

BACKGROUND

Long noncoding RNAs (lncRNA) play a role in leukemogenesis, maintenance, development, and therapeutic resistance of AML. While few studies have focused on the prognostic significance of LINC00649 in AML, which we aim to investigate in this present study.

METHODS

We compared the expression level of LINC00649 between AML patients and healthy controls. The Kaplan-Meier curves of AML patients expressing high versus low level of LINC00649 was performed. The LINC00649 correlated genes/miRNAs/lncRNAs and methylation CpG sites were screened by Pearson correlation analysis with R (version 3.6.0), using TCGA-LAML database. The LINC00649 associated ceRNA network was established using lncBase 2.0 and miRWalk 2.0 online tools, combining results from correlation analysis. Finally, a prediction model was constructed using LASSO-Cox regression.

RESULTS

LINC00649 was underexpressed in bone marrow of AML group than that in healthy control group. The patients of LINC00649-low group have significantly inferior PFS and OS. A total of 154 mRNAs, 31 miRNAs, 28 lncRNAs and 1590 methylated CpG sites were identified to be significantly correlated with LINC00649. Furthermore, the network of ceRNA was established with 6 miRNAs and 122 mRNAs. The Lasso-Cox model fitted OS/PFS to novel prediction models, which integrated clinical factors, ELN risk stratification, mRNA/miRNA expression and methylation profiles. The analysis of time-dependent ROC for our model showed a superior AUC (AUC = 0.916 at 1 year, AUC = 0.916 at 3 years, and AUC = 0.891 at 5 years).

CONCLUSIONS

Low expression of LINC00649 is a potential unfavorable prognostic marker for AML patients, which requires the further validation. The analysis by LASSO-COX regression identified a novel comprehensive model with a superior diagnostic utility, which integrated clinical and genetic variables.