The microRNA212 regulated PEA15 promotes ovarian cancer progression by inhibiting of apoptosis

Identification and verification of a prognostic autophagy-related gene signature in hepatocellular carcinoma

This study aimed to investigate the potential of autophagy-related genes (ATGs) as a prognostic signature for HCC and explore their relationships with immune cells and immune checkpoint molecules. A total of 483 samples were collected from the GEO database (n = 115) and The Cancer Genome Atlas (TCGA) database (n = 368). The GEO dataset was used as the training set, while the TCGA dataset was used for validation. The list of ATGs was obtained from the human autophagy database (HADB). Using Cox regression and LASSO regression methods, a prognostic signature based on ATGs was established. The independent use of this prognostic signature was tested through subgroup analysis. Additionally, the predictive value of this signature for immune-related profiles was explored. Following selection through univariate Cox regression analysis and iterative LASSO Cox analysis, a total of 11 ATGs were used in the GEO dataset to establish a prognostic signature that stratified patients into high- and low-risk groups based on survival. The robustness of this prognostic signature was validated using an external dataset. This signature remained a prognostic factor even in subgroups with different clinical features. Analysis of immune profiles revealed that patients in the high-risk group exhibited immunosuppressive states characterized by lower immune scores and ESTIMATE scores, greater tumour purity, and increased expression of immune checkpoint molecules. Furthermore, this signature was found to be correlated with the infiltration of different immune cell subpopulations. The results suggest that the ATG-based signature can be utilized to evaluate the prognosis of HCC patients and predict the immune status within the tumour microenvironment (TME). However, it is important to note that this study represents a preliminary attempt to use ATGs as prognostic indicators for HCC, and further validation is necessary to determine the predictive power of this signature.

Hypoxia-induced m6A demethylase ALKBH5 promotes ovarian cancer tumorigenicity by decreasing methylation of the lncRNA RMRP

Ovarian cancer is one of the most lethal and drug-resistant gynecological diseases. Among the various post-transcriptional RNA modifications, N6-methyladenosine (m6A) has been implicated in several malignancies, including breast cancer. Recently, the biological significance of long noncoding RNA (lncRNA) methylation has garnered significant attention. The N6-methyladenosine (m6A) demethylase ALKBH5 (Alkylation Repair Homolog Protein 5) has been shown to promote ovarian cancer development by reducing the methylation of the lncRNA RMRP. In this study, we found that a hypoxic microenvironment induces an increase in ALKBH5 expression in ovarian cancer. Both in vitro and in vivo investigations demonstrated that ALKBH5, which is overexpressed in human ovarian cancer, promotes carcinogenesis. Furthermore, using bioinformatics analysis, we predicted interactions between ALKBH5 and lncRNAs, confirming RMRP as a potential binding lncRNA for ALKBH5. ALKBH5 was found to upregulate RMRP expression via demethylation. Knockdown of RMRP in ovarian cancer cell lines led to a decrease in cell growth and migration. Additionally, we demonstrated that the inhibition of ovarian cancer by ALKBH5 knockdown is partially mediated by RMRP suppression. In conclusion, our findings reveal a novel mechanism in which ALKBH5 promotes ovarian cancer by demethylating the lncRNA RMRP, suggesting its potential as a therapeutic target for the disease.

Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma

Objective: To construct a prognostic evaluation model for clear cell renal cell carcinoma (ccRCC) patients using bioinformatics method and to screen potential drugs for ccRCC. Methods: ccRCC RNA sequencing data, clinical data, and protein expression data were downloaded from the TCGA database. Univariate Cox and Lasso regression analyses were performed on the combined data to screen out the proteins related to the prognosis, and they were included in a multivariate Cox proportional hazard model. The patients were divided into high and low-risk groups for a survival difference analysis. The predictive power of the model was evaluated on the basis of overall survival, progression-free survival, independent prognostic, clinically relevant receiver operating characteristic (ROC) curve, C-index, principal component, and clinical data statistics analyses. GSEA enrichment and immune function correlation analyses were performed. The samples were divided into different subtypes based on the expression of the risk proteins, and survival analysis of the subtypes was performed. The risk-related protein and RNA sequencing data were analyzed to screen out sensitive drugs with significant differences between the high and low-risk groups. Results: A total of 469 ccRCC-related proteins were screened, of which 13 proteins with independent prognostic significance were screened by univariate Cox, Lasso, and multivariate Cox regression analyses to construct the prognostic model. The sensitivity and accuracy of the model in predicting the survival of patients with ccRCC were high (1 year: 0.811, 3 years: 0.783, 5 years: 0.777). The 13 proteins were closely related to immunity, and the model proteins were different between kidney and tumor tissues according to the HPA database. The samples were divided into three subtypes, and there were obvious clinical characteristics of the three subtypes in the grade and T, N and M stages. According to the IC50 values, CGP-60474, vinorelbine, doxorubicin, etoposide, FTI-277, JQ12, OSU-03012, pyrimethamine, and other drugs were more sensitive in the high-risk group. Conclusions: A prognostic model of protein expression in ccRCC was successfully constructed, which had good predictive ability for the prognosis of ccRCC patients. The ccRCC-related proteins in the model can be used as targets for studying the pathogenesis and targeted therapy.

MicroRNA-200b-3p restrains gastric cancer cell proliferation, migration, and invasion via C-X-C motif chemokine ligand 12/CXC chemokine receptor 7 axis

This study was conducted to investigate the impact of microRNA (miR)-200b-3p on viability, migration, and invasion of gastric cancer (GC) cells and its mechanism. Quantitative real-time PCR (qRT-PCR) was conducted to measure miR-200b-3p expression in GC tissues and cells; besides, the relationship between miR-200b-3p expression and overall survival time (OS) was analyzed with OncomiR database; cell counting kit-8 (CCK-8), colony formation assay, flow cytometry, scratch healing assay, and Transwell assay were performed to detect the proliferation, cell cycle progression, migration, and invasion of GC cells; a lung metastasis model in nude mice was used to examine the effect of miR-200b-3p on the metastasis of GC cells in vivo; the interplay between miR-200b-3p and C-X-C motif chemokine ligand 12 (CXCL12) mRNA 3’ UTR was predicted by bioinformatics and verified with a dual-luciferase reporter gene assay; besides, the expression of CXCL12 and CXC chemokine receptor 7 (CXCR7) was probed by Western blot. It was found that miR-200b-3p expression was down-regulated in GC tissues, which was remarkably associated with the lymph node metastasis and decrease of differentiation of GC; transfection with miR-200b-3p mimics restrained the growth, migration, and invasion of GC cells in vitro, induced cell cycle arrest, and inhibited CXCL12 and CXCR7 expression levels; transfection of miR-200b-3p inhibitors worked oppositely in vitro and promoted lung metastasis in vivo. CXCL12 was confirmed as the downstream target of miR-200b-3p and was negatively modulated by miR-200b-3p. In conclusion, miR-200b-3p inhibited GC progression via regulating CXCL12/CXCR7 axis.

Deregulated miRNA clusters in ovarian cancer: Imperative implications in personalized medicine

Ovarian cancer (OC) is one of the most common and fatal types of gynecological cancer. OC is usually detected at the advanced stages of the disease, making it highly lethal. miRNAs are single-stranded, small non-coding RNAs with an approximate size ranging around 22 nt. Interestingly, a considerable proportion of miRNAs are organized in clusters with miRNA genes placed adjacent to one another, getting transcribed together to result in miRNA clusters (MCs). MCs comprise two or more miRNAs that follow the same orientation during transcription. Abnormal expression of the miRNA cluster has been identified as one of the key drivers in OC. MC exists both as tumor-suppressive and oncogenic clusters and has a significant role in OC pathogenesis by facilitating cancer cells to acquire various hallmarks. The present review summarizes the regulation and biological function of MCs in OC. The review also highlights the utility of abnormally expressed MCs in the clinical management of OC.

High Expression of PEA15 Is Associated With Patient Survival in Malignant Pleural Mesothelioma

BACKGROUND/AIM

Malignant pleural mesothelioma (MPM) is a rare but very aggressive tumor that is primarily pleural in origin. The 5-year overall survival rate of patients with MPM has not improved despite therapeutic advances. Therefore, biomarker discovery to identify premalignant or early malignant tumors of the mesothelium are crucial. PEA15 is a cytoplasmic protein that is involved in various human malignancies, including MPM. However, the clinicopathological involvement of PEA15 in MPM has not yet been documented.

MATERIALS AND METHODS

The Oncomine database and GEPIA2 platform were used to analyze PEA15 mRNA expression and patient survival in patients with MPM.

RESULTS

PEA15 was found to be significantly up-regulated in MPM, and this up-regulation inversely correlated with prolonged patient survival. Further, PEA15 expression was found to be increased in other cancer tissues without affecting overall survival.

CONCLUSION

PEA15 may represent a new potential prognostic biomarker in MPM patients.

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.

Materials and Methods

Protein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.

Results

From the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.

Conclusion

We constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

ZNF703 promotes tumor progression in ovarian cancer by interacting with HE4 and epigenetically regulating PEA15

Background

It is known that the transcription factor zinc finger protein 703 (ZNF703) plays an important role in physiological functions and the occurrence and development of various tumors. However, the role and mechanism of ZNF703 in ovarian cancer are unclear.

Materials and methods

Immunohistochemistry was used to analyze the expression of ZNF703 in ovarian cancer patients and to assess the effect of ZNF703 expression on the survival and prognosis of ovarian cancer patients. ZNF703 overexpression and suppression expression experiments were used to evaluate the effect of ZNF703 on malignant biological behavior of ovarian cancer cells in vitro. Detecting the interaction between HE4 and ZNF703 by immunofluorescence colocalization and coprecipitation, and nuclear translocation. Chromatin immunoprecipitation-sequencing (ChIP-Seq), dual luciferase reporter assay, ChIP-PCR, in vivo model were applied to study the molecular mechanism of ZNF703 affecting the development of ovarian cancer.

Results

ZNF703 was highly expressed in ovarian cancer tissues, and its expression level is related to the prognosis of ovarian cancer patients. In vivo and in vitro experiments confirmed that ZNF703 overexpression/inhibition expression will promoted/inhibited the malignant biological behavior of ovarian cancer. Mechanically, ZNF703 interacted with HE4, and HE4 promoted nuclear translocation of ZNF703. ChIP-Seq identified multiple regulatory targets of ZNF703, of which ZNF703 directly binds to the enhancer region of PEA15 to promote the transcription of PEA15 and thereby promoted the proliferation of cancer cells.

Conclusion

The results showed that ZNF703 as an oncogene played an important role in the epigenetic modification of ovarian cancer proliferation, and suggested that ZNF703 as a transcription factor may become a prognostic factor and a potential therapeutic target for ovarian cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-020-01770-0.