Clinical value of survivin and its underlying mechanism in ovarian cancer: A bioinformatics study based on GEO and TCGA data mining

Identification and validation of a gene-based signature reveals SLC25A10 as a novel prognostic indicator for patients with ovarian cancer

Background

Ovarian cancer is a common gynecological cancer with poor prognosis and poses a serious threat to woman life and health. In this study, we aimed to establish a prognostic signature for the risk assessment of ovarian cancer.

Methods

The Cancer Genome Atlas (TCGA) dataset was used as the training set and the International Cancer Genome Consortium (ICGC) dataset was set as an independent external validation. A multi-stage screening strategy was used to determine the prognostic features of ovarian cancer with R software. The relationship between the prognosis of ovarian cancer and the expression level of SLC25A10 was selected for further analysis.

Results

A total of 16 prognosis-associated genes were screened to construct the risk score signature. Survival analysis showed that patients in the high-risk score group had a poor prognosis compared to the low-risk group. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic (ROC) curve and decision curve analysis (DCA), and validated with ICGC cohort. This signature was identified as an independent factor for predicting overall survival (OS). Nomogram constructed by multiple clinical parameters showed excellent performance for OS prediction. Finally, it’s found that patients with low expression of SLC25A10 generally had poor survival and higher resistance to most chemotherapeutic drugs.

Conclusions

In sum, we developed a 16-gene prognostic signature, which could serve as a promising tool for the prognostic prediction of ovarian cancer, and the expression level of SLC25A10 was tightly associated with OS of the patients.

Pan-cancer analysis identifies BIRC5 as a prognostic biomarker

Background

The BIRC5 gene encodes for the Survivin protein, which is a member of the inhibitor of apoptosis family. Survivin is found in humans during fetal development, but generally not in adult cells thereafter. Previous studies have shown that Survivin is abundant in most cancer cells, thereby making it a promising target for anti-cancer drugs and a potential prognostic tool.

Methods

To assess genetic alterations and mutations in the BIRC5 gene as well as BIRC5 co-expression with other genes, genomic and transcriptomic data were downloaded via cBioPortal for approximately 9000 samples from The Cancer Genome Atlas (TCGA) representing 33 different cancer types and 11 pan-cancer organ systems, and validated using the ICGC Data Portal and COSMIC. TCGA BIRC5 RNA sequencing data from 33 different cancer types and matching normal tissue samples for 16 cancer types were downloaded from Broad GDAC Firehose and validated using breast cancer microarray data from our previous work and data sets from the GENT2 web-based tool. Survival data were analyzed with multivariable Cox proportional hazards regression analysis and validated using KM plotter for breast-, ovarian-, lung- and gastric cancer.

Results

Although genetic alterations in BIRC5 were not common in cancer, BIRC5 expression was significantly higher in cancer tissue compared to normal tissue in the 16 different cancer types. For 14/33 cancer types, higher BIRC5 expression was linked to worse overall survival (OS, 4/14 after adjusting for both age and tumor grade and 10/14 after adjusting only for age). Interestingly, higher BIRC5 expression was associated with better OS in lung squamous cell carcinoma and ovarian serous cystadenocarcinoma. Higher BIRC5 expression was also linked to shorter progressive-free interval (PFI) for 14/33 cancer types (4/14 after adjusting for both age and tumor grade and 10/14 after adjusting only for age). External validation showed that high BIRC5 expression was significantly associated with worse OS for breast-, lung-, and gastric cancer.

Conclusions

Our findings suggest that BIRC5 overexpression is associated with the initiation and progression of several cancer types, and thereby a promising prognostic biomarker.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09371-0.

Survival risk prediction model for ESCC based on relief feature selection and CNN

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive system with poor prognosis and high mortality. It is of great significance to predict the prognosis risk of patients with cancer by using medical pathology information. To take full advantage of the clinic pathological information of ESCC patients and improve the accuracy of postoperative survival risk prediction, this paper proposes an ESCC survival risk prediction model based on Relief feature selection and convolutional neural network (CNN). Firstly, statistical analysis methods and relief feature selection algorithm are used to extract the important risk factors related to the survival risk of patients. Then, One-dimensional convolutional neural network (1D-CNN) is used to establish the survival risk prediction model of patients with esophageal cancer. Finally, the data of patients with esophageal cancer provided by the First Affiliated Hospital of Zhengzhou University is used to assess the performance of the model. The results show that the model proposed in this paper has a high accuracy rate, which can effectively predict the postoperative survival risk of the patient through the clinical phenotypic index of the patient.

Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis

Osteosarcoma, which arises from bone tissue, is considered to be one of the most common types of cancer in children and teenagers. As the etiology of osteosarcoma has not been fully elucidated, the overall prognosis for patients is generally poor. In recent years, the development of bioinformatical technology has allowed researchers to identify numerous molecular biological characteristics associated with the prognosis of osteosarcoma using online databases. In the present study, Gene Expression Omnibus (GEO) database was used and three microarray datasets were obtained. The GEO2R web tool was utilized and differentially expressed genes (DEGs) in osteosarcoma tissue were identified. Venn analysis was performed to determine the intersection of the DEG profiles. DEGs were analyzed by Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein-protein interactions (PPIs) between these DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes database, and the PPI network was then visualized using Cytoscape software. The top ten genes were identified based on measurement of degree, density of maximum neighborhood component, maximal clique centrality and mononuclear cell counts in the PPI network, and five overlapping genes [origin recognition complex subunit 6 (ORC6), IGF-binding protein 5 (IGFBP5), minichromosome maintenance 10 replication initiation factor (MCM10), MET proto-oncogene, receptor tyrosine kinase (MET) and centromere protein F (CENPF)] were identified. Additionally, three module networks were analyzed by Molecular Complex Detection (MCODE), and six key genes [ORC6, MCM10, DEP domain containing 1 (DEPDC1), CENPF, TIMELESS interacting protein (TIPIN) and shugoshin 1 (SGOL1)] were screened. Combined with the results from Cytoscape and MCODE, eight hub genes (ORC6, MCM10, DEPDC1, CENPF, TIPIN, SGOL1, MET and IGFBP5) were obtained. Furthermore, Kaplan-Meier plotter survival analysis was used to evaluate the prognostic value of these eight hub genes in patients with osteosarcoma. Oncomine and GEPIA databases were applied to further confirm the expression levels of hub genes in tissue. Finally, the functional roles of the core gene CENPF were investigated using Cell Counting Kit-8, wound healing and Transwell assays, which indicated that CENPF knockdown inhibited the proliferation, migration and invasion of osteosarcoma cells. These results provided potential prognostic markers, as well as a basis for further investigation of the mechanism underlying osteosarcoma.

LncRNA-MSC-AS1 inhibits the ovarian cancer progression by targeting miR-425-5p

BACKGROUND

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) were reported to be aberrantly expressed and related to the pathogenesis of ovarian cancer. However, the role and regulatory mechanism of MSC-AS1 in ovarian cancer has yet to be fully elucidated.

METHODS

Expression of lncRNA MSC-AS1 (MSC-AS1) and microRNA-425-5p (miR-425-5p) in the ovarian cancer tissue samples and cell lines was examined by quantitative real-time polymerase chain reaction (qRT-PCR). The functions of MSC-AS1 on ovarian cancer cell proliferation, cell cycle and apoptosis were determined using MTT, colony formation and flow cytometry analyses. The protein expression levels were evaluated using western blot assay. The targeting relationship MSC-AS1 and miR-425-5p was verified via dual-luciferase reporter assay.

RESULTS

MSC-AS1 expression level was lowly expressed, while miR-425-5p level was highly in ovarian cancer tissues and cells. Elevation of MSC-AS1 has the ability to significantly inhibit cell proliferation and facilitate cell apoptosis in SKOV3 and A2780 cells. Moreover, MSC-AS1 targeted and negatively modulated miR-425-5p. MiR-425-5p up-regulation has been proved to partially reverse the tumor suppressive function of MSC-AS1 overexpression CONCLUSION: MSC-AS1 sponged miR-425-5p to inhibit the ovarian cancer progression. These findings may provide a promising therapeutic target for the treatment of ovarian cancer.

Incomplete thermal ablation-induced up-regulation of transcription factor nuclear receptor subfamily 2, group F, member 6 (NR2F6) contributes to the rapid progression of residual liver tumor in hepatoblastoma

Hepatoblastoma is a kind of extreme malignancy frequently diagnosed in children. Although surgical resection is considered as the first-line treatment for hepatoblastoma, a relatively large population of patients have lost the preferred opportunity for surgery. Administration of locoregional ablation enables local tumor control but with the deficiency of insufficient ablation, residual tumor, and rapid progression. In this study, we integrated 219 hepatoblastoma and 121 non-cancer liver tissues to evaluate the expression of NR2F6, from which a higher NR2F6 level was found in hepatoblastoma compared with non-cancer livers with a standard mean difference (SMD) of 1.04 (95% CI: 0.79, 1.29). The overexpression of NR2F6 also appeared to be an efficient indicator in distinguishing hepatoblastoma tissues from non-cancer liver tissues from the indication of a summarized AUC of 0.90, with a pooled sensitivity of 0.76 and a pooled specificity of 0.89. Interestingly, nude mouse xenografts provided direct evidence that overexpressed NR2F6 was also detected in residual tumor compared to untreated hepatoblastoma. Chromatin immunoprecipitation-binding data in HepG2 cells and transcriptome analysis of HepG2 xenografts were combined to identify target genes regulated by NR2F6. We finally selected 150 novel target genes of NR2F6 in residual tumor of incomplete ablation, and these genes appeared to be associated with the biological regulation of lipid metabolism-related pathway. Accordingly, targeting NR2F6 holds a therapeutic promise in treating residual recurrent hepatoblastoma after incomplete ablation.

Prognostic Significance of Survivin Expression in Patients with Ovarian Carcinoma: A Meta-Analysis

Background: Survivin belongs to the protein family of inhibitors of apoptosis (IAP) and is a regulator of the cell cycle and apoptosis. The aim of this study was to assess the clinical and prognostic significance of expression survivin in patients with ovarian cancer. Methods: We systematically searched for articles in PubMed, the American Chemical Society (Publications), Medline, the Royal Society of Chemistry, Scopus and the Web of Science. Patient clinical data, overall survival (OS), disease-free survival (DFS), and survivin expression were extracted from individual studies. We performed statistical analysis using the STATA 16 package. Eighteen publications containing data from 2233 patients with ovarian cancer were included in this meta-analysis. Results: We found an adverse effect of survivin expression on OS (risk ratio (HR): 1.60; 95% confidence interval (CI): 1.33–1.93, p = 0.00) but this was not observed on DFS (HR: 1.06; 95% CI: 0.55–2.05, p = 0.87). The analysis of clinicopathological parameters showed that survivin expression was associated with the histological grades (G1–2 vs. G3) (odds ratio (OR) = 0.53, 95% CI: 0.34–0.83, p = 0.01) and: International Federation Gynecology and Obstetrics (FIGO) stage (I–II vs. III–IV) (OR = 0.22, 95% CI: 0.09–0.55, p = 0.00), but it was not significantly correlated with the histological subtype (OR = 1.14, 95% CI: 0.83–1.58, p = 0.42). Conclusions: Our meta-analysis suggests that survivin expression may be a marker of poor prognosis in ovarian cancer. Survivin expression was associated with parameters of greater aggressiveness of ovarian cancer. Prospective studies are needed to confirm our results indicating that survivin expression can be used as an ovarian cancer biomarker.

Ovarian Primary and Metastatic Tumors Suppressed by Survivin Knockout or a Novel Survivin Inhibitor

Survivin, a member of the inhibitor of apoptosis family, is upregulated in multiple cancers including ovarian cancer, but is rarely detectable in normal tissues. We previously reported that survivin promoted epithelial-to-mesenchymal transition (EMT) in ovarian cancer cells, suggesting that survivin may contribute to ovarian tumor metastasis and chemoresistance. In this study, we tested whether knockout or pharmacologic inhibition of survivin overcomes chemoresistance and suppresses tumor metastasis. The genetic loss of survivin suppressed tumor metastasis in an orthotopic ovarian cancer mouse model. To pharmacologically test the role of survivin on ovarian tumor metastasis, we treated chemo-resistant ovarian cancer cells with a selective survivin inhibitor, MX106, and found that MX106 effectively overcame chemoresistance in vitro MX106 inhibited cell migration and invasion by attenuating the TGFβ pathway and inhibiting EMT in ovarian cancer cells. To evaluate the efficacy of MX106 in inhibiting ovarian tumor metastasis, we treated an orthotopic ovarian cancer mouse model with MX106, and found that MX106 efficiently inhibited primary tumor growth in ovaries and metastasis in multiple peritoneal organs as compared with vehicle-treated control mice. Our data demonstrate that inhibition of survivin using either genetic knockout or a novel inhibitor MX106 suppresses primary ovarian tumor growth and metastasis, supporting that targeting survivin could be an effective therapeutic approach in ovarian cancer.

Dual-mode US/MRI nanoparticles delivering siRNA and Pt(iv) for ovarian cancer treatment

Phase-shifted dual-mode US/MRI nanoparticles (PFH/siRNA/Fe₃O₄@Pt(iv) NPs-cRGD) delivering si-survivin and Pt(iv) prodrug for enhancing ovarian cancer treatment and realizing real-time monitoring.

Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma

Background

This study compared tumor‐related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma (LUAD) treatment.

Methods

Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses were performed based on LUAD differentially expressed genes from The Cancer Genome Atlas (TCGA) project and genotype‐tissue expression controls. These results were compared to various known compounds using the Connectivity Mapping dataset. The clinical significance of the hub genes identified by overlapping pathway enrichment analysis was further investigated using data mining from multiple sources. A drug‐pathway network for LUAD was constructed, and molecular docking was carried out.

Results

After the integration of 57 LUAD‐related pathways and 35 pathways affected by small molecules, five overlapping pathways were revealed. Among these five pathways, the p53 signaling pathway was the most significant, with CCNB1, CCNB2, CDK1, CDKN2A, and CHEK1 being identified as hub genes. The p53 signaling pathway is implicated as a risk factor for LUAD tumorigenesis and survival. A total of 88 molecules significantly inhibiting the five LUAD‐related oncogenic pathways were involved in the LUAD drug‐pathway network. Daunorubicin, mycophenolic acid, and pyrvinium could potentially target the hub gene CHEK1 directly.

Conclusion

Our study highlights the critical pathways that should be targeted in the search for potential LUAD treatments, most importantly, the p53 signaling pathway. Some compounds, such as ciclopirox and AG‐028671, may have potential roles for LUAD treatment but require further experimental verification.