Upregulation of CENPM facilitates tumor metastasis via the mTOR/p70S6K signaling pathway in pancreatic cancer

Knockdown of CENPM activates cGAS-STING pathway to inhibit ovarian cancer by promoting pyroptosis

OBJECTIVE

We aimed to screen novel gene signatures for ovarian cancer (OC) and explore the role of biomarkers in OC via regulating pyroptosis using bioinformatics analysis.

METHODS

Differentially expressed genes (DEGs) of OC were screened from GSE12470 and GSE16709 datasets. Hub genes were determined from protein-protein interaction networks after bioinformatics analysis. The role of Centromeric protein M (CENPM) in OC was assessed by subcutaneous tumor experiment using hematoxylin-eosin and immunohistochemical staining. Tumor metastasis was evaluated by detecting epithelial-mesenchymal transition-related proteins. The proliferation, migration, and invasion were determined using cell counting kit and transwell assay. Enzyme-linked immunosorbent assay was applied to measure inflammatory factors. The mRNA and protein expression were detected using real-time quantitative PCR and western blot.

RESULTS

We determined 9 hub genes (KIFC1, PCLAF, CDCA5, KNTC1, MCM3, OIP5, CENPM, KIF15, and ASF1B) with high prediction value for OC. In SKOV3 and A2780 cells, the expression levels of hub genes were significantly up-regulated, compared with normal ovarian cells. CENPM was selected as a key gene. Knockdown of CENPM suppressed proliferation, migration, and invasion of OC cells. Subcutaneous tumor experiment revealed that CENPM knockdown significantly suppressed tumor growth and metastasis. Additionally, pyroptosis was promoted in OC cells and xenograft tumors after CENPM knockdown. Furthermore, CENPM knockdown activated cGAS-STING pathway and the pathway inhibitor reversed the inhibitory effect of CENPM knockdown on viability, migration, and invasion of OC cells.

CONCLUSION

CENPM was a novel biomarker of OC, and knockdown of CENPM inhibited OC progression by promoting pyroptosis and activating cGAS-STING pathway.

Upregulation of CENPM promotes breast carcinogenesis by altering immune infiltration

BACKGROUND

The involvement of centromere protein M (CENPM) in various types of cancer has been established, however, its impact on breast cancer and immune infiltration remains unknown.

METHODS

We examined the expression of CENPM in different cancer types by utilizing the Cancer Genome Atlas (TCGA) and Genotype Tissue Expression Pan-Cancer (GEO) databases. Using data from the TCGA, we examined the correlation between the expression of CENPM, the prognosis, and the clinicopathological features of individuals diagnosed with breast cancer. We conducted an enrichment analysis of CENPM using the clusterProfiler R software tool, utilizing data obtained from breast cancer patients and specimens at our institution. In addition to examining the correlation between CENPM expression and genes associated with immune checkpoints, the TIDE algorithm was employed to explore the potential of CENPM as a biomarker for immunotherapy in breast cancer. The impact of CENPM on the growth of breast cancer cells was evaluated through the utilization of the CCK8 test and the colony formation assay. The effect of CENPM on the migration of breast cancer cells was assessed using scratch and transwell assays.

RESULTS

Research findings indicate that elevated levels of CENPM are linked to patient outcomes in breast cancer and various clinicopathological features. Furthermore, elevated levels of CENPM expression correlated with decreased levels of CD8 + T cells and mast cells, increased levels of Tregs and Th2, and reduced levels of CD8 + T cells. Additionally, the coexpression of CENPM with the majority of genes related to immune checkpoints indicates its potential to forecast the effectiveness of treatment in breast cancer. Suppression of CENPM hampers the growth and movement of breast tumor cells.

CONCLUSIONS

In summary, our study findings indicate that CENPM may serve as a cancer-causing gene in breast cancer and also as a biomarker for predicting the efficacy of immunotherapy. The oncogene CENPM is associated with breast cancer and is involved in cell proliferation and immune infiltration.

Decoding the metastatic potential and optimal postoperative adjuvant therapy of melanoma based on metastasis score

Metastasis is a formidable challenge in the prognosis of melanoma. Accurately predicting the metastatic potential of non-metastatic melanoma (NMM) and determining effective postoperative adjuvant treatments for inhibiting metastasis remain uncertain. In this study, we conducted comprehensive analyses of melanoma metastases using bulk and single-cell RNA sequencing data, enabling the construction of a metastasis score (MET score) through diverse machine-learning algorithms. The reliability and robustness of the MET score were validated using various in vitro assays and in vivo models. Our findings revealed a distinct molecular landscape in metastatic melanoma characterized by the enrichment of metastasis-related pathways, intricate cell-cell communication, and heightened infiltration of pro-angiogenic tumor-associated macrophages compared to NMM. Importantly, patients in the high MET score group exhibited poorer prognoses and an immunosuppressive microenvironment, featuring increased infiltration of regulatory T cells and decreased infiltration of CD8+ T cells, compared to the low MET score patient group. Expression of PD-1 was markedly higher in patients with low MET scores. Anti-PD-1 (aPD-1) therapy profoundly affected antitumor immunity activation and metastasis inhibition in these patients. In summary, our study demonstrates the effectiveness of the MET score in predicting melanoma metastatic potential. For patients with low MET scores, aPD-1 therapy may be a potential treatment strategy to inhibit metastasis. Patients with high MET scores may benefit from combination therapies.

Pan-Cancer Analysis Reveals CENPI as a Potential Biomarker and Therapeutic Target in Adrenocortical Carcinoma

Background

Centromere protein I (CENPI) has been shown to affect the tumorigenesis of breast and colorectal cancers. However, its biological role and prognostic value in other kinds of cancer, especially adrenocortical carcinoma (ACC), remained to be further investigated.

Methods

Various bioinformatics tools were adopted for exploring the significance of differential expression of CENPI in several malignant tumors from databases such as Depmap portal, GTEx, and TCGA. ACC was selected for further analyzed, and information such as clinicopathological features, the prognostic outcome of diverse subgroups, differentially expressed genes (DEGs), co-expression genes, as well as levels of tumor-infiltrating immune cells (TIIC), was extracted from multiple databases. To verify the possibility of CENPI as a therapeutic target in ACC, drug sensitivity assay and si-RNA mediate knockdown of CENPI were carried out.

Results

The pan-cancer analyses showed that the CENPI mRNA expression levels differed significantly among most cancer types. Additionally, a high precision in cancer prediction and close relation with cancer survival indicated that CENPI could be a potential candidate biomarker to diagnose and predict cancer prognosis. In ACC, CENPI was closely related to multiple clinical characteristics, such as pathological stage and primary therapy outcome. High CENPI levels predicted poor overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) of ACC patients, particularly for different clinical subgroups. Moreover, the expression of CENPI showed positive relationship to Th2 cells but negatively related to most of the TIICs. Furthermore, drug sensitivity assay showed that vorinostat inhibit CENPI expression and ACC cell growth. Additionally, si-RNA mediated knockdown of CENPI inhibited ACC cell growth and invasion and showed synergistic anti-proliferation effect with AURKB inhibitor barasertib.

Conclusion

Pan-cancer analysis demonstrated that CENPI is a potential diagnostic and prognostic biomarker in various cancers as well as an anti-ACC therapeutic target.

A five-gene expression signature of centromeric proteins with prognostic value in lung adenocarcinoma

Background

Centromere proteins (CENPs) form a large protein family. Sixteen proteins in this family are positioned at the centromere throughout the cell cycle. The overexpression of CENPs is common in many cancers and predicts a poor prognosis. However, a comprehensive analysis of CENPs expression has not been conducted, and their clinical significance in lung adenocarcinoma (LUAD) is unclear.

Methods

We investigated the expression differences of the CENP family in LUAD using The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) cohorts. Kaplan-Meier curve survival analysis was performed to assess their independent prognostic values. We then tested 5 clinical LUAD specimens by quantitative real time polymerase chain reaction (qRT-PCR). The risk model was constructed with least absolute shrinkage and selection operator (LASSO). Cox regression analyses were carried out to determine independent prognostic indicators. Weighted gene coexpression network analysis (WGCNA) was employed to define the coexpression networks.

Results

The messenger RNA (mRNA) expression of 15 differential CENP proteins was higher in LUAD than in normal lung tissues. Among them, 10 CENP proteins had significant prognostic value. The risk model comprising CENPF, CENPU, CENPM, CENPH, and CENPW showed a significant correlation [hazard ratio (HR) 1.75, 95% confidence interval (CI): 1.3-2.35; P=2e-04]. However, the prognostic accuracy was not strong [1-year survival: area under curve (AUC) 0.63; 3-year survival: AUC 0.62; 5-year survival: AUC 0.6]. The qRT-PCR results showed that the 5 CENPs were upregulated in LUAD tissues compared to in normal lung tissues. A total of 441 hub genes coexpressed with the 5 CENPs were identified.

Conclusions

CENPF, CENPU, CENPM, CENPH, and CENPW have prognostic values and may be potential targets for LUAD treatment.

Upregulation of CENPM is associated with poor clinical outcome and suppression of immune profile in clear cell renal cell carcinoma

BACKGROUND

The response of advanced clear cell renal cell carcinoma (ccRCC) to immunotherapy is still not durable, suggesting that the immune landscape of ccRCC still needs to be refined, especially as some molecules that have synergistic effects with immune checkpoint genes need to be explored.

METHODS

The expression levels of CENPM and its relationship with clinicopathological features were explored using the ccRCC dataset from TCGA and GEO databases. Quantitative polymerase chain reaction (qPCR) analysis was performed to validate the expression of CENPM in renal cancer cell lines. Kaplan-Meier analysis, COX regression analysis and Nomogram construction were used to systematically evaluate the prognostic potential of CENPM in ccRCC. Besides, single gene correlation analysis, protein-protein interaction (PPI) network, genetic ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to predict the biological behaviour of CENPM and the possible signalling pathways involved. Finally, a comprehensive analysis of the crosstalk between CENPM and immune features in the tumor microenvironment was performed based on the ssGSEA algorithm, the tumor immune dysfunction and exclusion (TIDE) algorithm, the TIMER2.0 database and the TISIDB database.

RESULTS

CENPM was significantly upregulated in ccRCC tissues and renal cancer cell lines and was closely associated with poor clinicopathological features and prognosis. Pathway enrichment analysis revealed that CENPM may be involved in the regulation of the cell cycle in ccRCC and may have some crosstalk with the immune microenvironment in tumors. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. Furthermore, the TISIDB database provides evidence that not only CENPM is positively associated with immune checkpoint genes such as CTLA4, PDCD1, LAG3, TIGIT, but also chemokines and receptors (such as CCL5, CXCL13, CXCR3, CXCR5) may be responsible for the malignant phenotype of CENPM in ccRCC. Meanwhile, predictions based on the TIDE algorithm support that patients with high CENPM expression have a worse response to immunotherapy.

CONCLUSIONS

The upregulation of CENPM in ccRCC predicts a poor clinical outcome, and this malignant phenotype may be associated with its exacerbation of the immunosuppressive state in the tumor microenvironment.

Biomarkers discovery for endometrial cancer: A graph convolutional sample network method

BACKGROUND

Endometrial carcinoma is the sixth most common cancer in women worldwide. Importantly, endometrial cancer is among the few types of cancers with patient mortality that is still increasing, which indicates that the improvement in its diagnosis and treatment is still urgent. Moreover, biomarker discovery is essential for precise classification and prognostic prediction of endometrial cancer.

METHODS

A novel graph convolutional sample network method was used to identify and validate biomarkers for the classification of endometrial cancer. The sample networks were first constructed for each sample, and the gene pairs with high frequencies were identified to construct a subtype-specific network. Putative biomarkers were then screened using the highest degrees in the subtype-specific network. Finally, simplified sample networks are constructed using the biomarkers for the graph convolutional network (GCN) training and prediction.

RESULTS

Putative biomarkers (23) were identified using the novel bioinformatics model. These biomarkers were then rationalised with functional analyses and were found to be correlated to disease survival with network entropy characterisation. These biomarkers will be helpful in future investigations of the molecular mechanisms and therapeutic targets of endometrial cancers.

CONCLUSIONS

A novel bioinformatics model combining sample network construction with GCN modelling is proposed and validated for biomarker discovery in endometrial cancer. The model can be generalized and applied to biomarker discovery in other complex diseases.

Upregulation of CENPM facilitates lung adenocarcinoma progression via PI3K/AKT/mTOR signaling pathway

Centromere protein M (CENPM) is essential for chromosome separation during mitosis. However, its roles in lung adenocarcinoma (LUAD) progression and metastasis remain unknown. In this study, we aimed to explore the effects of CENPM on LUAD progression as well as the underlying mechanisms. We analyzed the expression of CENPM and its correlation with clinicopathological characteristics using GEO LUAD chip datasets and TCGA dataset. We further investigated the impact of CENPM on LUAD and . In silico analysis and qRT-PCR revealed that CENPM is upregulated in LUAD compared with that in normal lung tissues. Via gain/loss-of-function assays, we further found that CENPM promotes the LUAD cell cycle, cell proliferation, migration and invasion, and inhibits cell apoptosis. The study showed that loss of CENPM inhibits the growth of A549 xenografts. Furthermore, we found that CENPM can promote the phosphorylation of mTOR rather than directly affect the mTOR content. Inhibition of mTOR activity abrogates the promoting effects of CENPM on cell cycle progression, cell proliferation, migration and invasion. Taken together, these results show that CENPM plays an important role in the growth and metastasis of LUAD and may be a promising therapeutic target in LUAD.

Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection

Background

Early recurrence of hepatocellular carcinoma (HCC) is a major obstacle to improving the prognosis, and no widely accepted adjuvant therapy guideline for patients post-liver resection is available. Currently, all available methods and biomarkers are insufficient to accurately predict post-operation HCC patients’ risk of early recurrence and their response to adjuvant therapy.

Methods

In this study, we downloaded four gene expression datasets (GSE14520, GSE54236, GSE87630, and GSE109211) from the Gene Expression Omnibus database and identified 34 common differentially expressed genes associated with HCC dysregulation and response to adjuvant sorafenib. Then, we constructed a novel 11-messenger RNA predictive model by using ROC curves analysis, univariate Cox regression analysis, and LASSO Cox regression analysis. Furthermore, we validated the predictive values of the risk model in GSE14520 and TCGA-LIHC cohorts by using Kaplan–Meier survival analysis, multivariable Cox regression analysis, and decision curve analysis, respectively.

Results

The risk score model could identify patients with a high risk of HCC recurrence at the early stage and could predict the response of patients to adjuvant sorafenib. Patients with a high risk score had a worse recurrence rate in training cohorts (2-year: p < 0.0001, hazard ratio (HR): 4.658, confidence interval 95% CI [2.895–7.495]; 5-year: p < 0.0001, HR: 3.251, 95% CI [2.155–4.904]) and external validation cohorts (2-year: p < 0.001, HR: 3.65, 95% CI [2.001–6.658]; 5-year: p < 0.001, HR: 3.156, 95% CI [1.78–5.596]). The AUC values of the risk score model for predicting tumor early recurrence were 0.746 and 0.618, and that of the risk score model for predicting the response to adjuvant sorafenib were 0.722 and 0.708 in the different cohort, respectively. Multivariable Cox regression analysis and decision curve analysis also showed that the risk score model was superior to and independent of other clinicopathologic characteristics. Moreover, the risk score model had excellent abilities to predict the overall survival and HCC recurrence of patients with the same tumor stage category.

Conclusions

Our risk model is a reliable and superior predictive tool. With this model, we could optimize the risk stratification based on early tumor recurrence and could evaluate the response of patients to adjuvant sorafenib after liver resection.

ATF2-Induced Overexpression of lncRNA LINC00882, as a Novel Therapeutic Target, Accelerates Hepatocellular Carcinoma Progression via Sponging miR-214-3p to Upregulate CENPM

Background

Long intergenic non-protein coding RNA 882 (LINC00882) are abnormally expressed in several tumors. Our research aimed to uncover the functions and the potential mechanisms of LINC00882 in hepatocellular carcinoma (HCC) progression.

Methods

RT-qPCR was applied to identify LINC00882 and miR-214-3p levels in HCC specimens and cells. Luciferase reporter was applied for the exploration of whether activating transcription factor 2 (ATF2) could bind to the promoter region of LINC00882. Cell proliferation, invasion, and migration were evaluated. In vivo tumor xenograft models were constructed to assess tumorigenicity. RT-PCR, Western blot and Luciferase reporter assays were conducted to examine the regulatory relationships among LINC00882, miR-214-3p and ATF2.

Results

LINC00882 was markedly upregulated in HCC cells and clinical specimens. Additionally, ATF2 could bind directly to the LINC00882 promoter region and activate its transcription. Loss-of-function studies further demonstrated that LINC00882 knockdown inhibited proliferation, invasion, and migration of HCC cells. Mechanistically, LINC00882 adsorbed miR-214-3p, thus promoting the expressions of CENPM. Rescue assays demonstrated that functions of LINC00882 deficiency in HCC cells were reversed through suppressing miR-214-3p.

Conclusion

Our group identified a novel regulatory axis of ATF2/LINC00882/miR-214-3p/CENPM, which may provide potential therapeutic targets for HCC.

Prognostic Value of a Novel Signature With Nine Hepatitis C Virus-Induced Genes in Hepatic Cancer by Mining GEO and TCGA Databases

Background

Hepatitis C virus-induced genes (HCVIGs) play a critical role in regulating tumor development in hepatic cancer. The role of HCVIGs in hepatic cancer remains unknown. This study aimed to construct a prognostic signature and assess the value of the risk model for predicting the prognosis of hepatic cancer.

Methods

Differentially expressed HCVIGs were identified in hepatic cancer data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases using the library (“limma”) package of R software. The protein–protein interaction (PPI) network was constructed using the Cytoscape software. Functional enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate and multivariate Cox proportional hazard regression analyses were applied to screen for prognostic HCVIGs. The signature of HCVIGs was constructed. Gene Set Enrichment Analysis (GSEA) compared the low-risk and high-risk groups. Finally, the International Cancer Genome Consortium (ICGC) database was used to validate this prognostic signature. Polymerase chain reaction (PCR) was performed to validate the expression of nine HCVIGs in the hepatic cancer cell lines.

Results

A total of 143 differentially expressed HCVIGs were identified in TCGA hepatic cancer dataset. Functional enrichment analysis showed that DNA replication was associated with the development of hepatic cancer. The risk score signature was constructed based on the expression of ZIC2, SLC7A11, PSRC1, TMEM106C, TRAIP, DTYMK, FAM72D, TRIP13, and CENPM. In this study, the risk score was an independent prognostic factor in the multivariate Cox regression analysis [hazard ratio (HR) = 1.433, 95% CI = 1.280–1.605, P < 0.001]. The overall survival curve revealed that the high-risk group had a poor prognosis. The Kaplan–Meier Plotter online database showed that the survival time of hepatic cancer patients with overexpression of HCVIGs in this signature was significantly shorter. The prognostic signature-associated GO and KEGG pathways were significantly enriched in the risk group. This prognostic signature was validated using external data from the ICGC databases. The expression of nine prognostic genes was validated in HepG2 and LO-2.

Conclusion

This study evaluates a potential prognostic signature and provides a way to explore the mechanism of HCVIGs in hepatic cancer.

Everolimus regulates the activity of gemcitabine-resistant pancreatic cancer cells by targeting the Warburg effect via PI3K/AKT/mTOR signaling

BACKGROUND

Gemcitabine (GEM) resistance remains a significant clinical challenge in pancreatic cancer treatment. Here, we investigated the therapeutic utility of everolimus (Evr), an inhibitor of mammalian target of rapamycin (mTOR), in targeting the Warburg effect to overcome GEM resistance in pancreatic cancer.

METHODS

The effect of Evr and/or mTOR overexpression or GEM on cell viability, migration, apoptosis, and glucose metabolism (Warburg effect) was evaluated in GEM-sensitive (GEMsen) and GEM-resistant (GEMres) pancreatic cancer cells.

RESULTS

We demonstrated that the upregulation of mTOR enhanced cell viability and favored the Warburg effect in pancreatic cancer cells via the regulation of PI3K/AKT/mTOR signaling. However, this effect was counteracted by Evr, which inhibited aerobic glycolysis by reducing the levels of glucose, lactic acid, and adenosine triphosphate and suppressing the expression of glucose transporter 1, lactate dehydrogenase-B, hexokinase 2, and pyruvate kinase M2 in GEMsen and GEMres cells. Evr also promoted apoptosis by upregulating the pro-apoptotic proteins Bax and cytochrome-c and downregulating the anti-apoptotic protein Bcl-2. GEM was minimally effective in suppressing GEMres cell activity, but the therapeutic effectiveness of Evr against pancreatic cancer growth was greater in GEMres cells than that in GEMsen cells. In vivo studies confirmed that while GEM failed to inhibit the progression of GEMres tumors, Evr significantly decreased the volume of GEMres tumors while suppressing tumor cell proliferation and enhancing tumor apoptosis in the presence of GEM.

CONCLUSIONS

Evr treatment may be a promising strategy to target the growth and activity of GEM-resistant pancreatic cancer cells by regulating glucose metabolism via inactivation of PI3K/AKT/mTOR signaling.