Construction autophagy-related prognostic risk signature to facilitate survival prediction, individual treatment and biomarker excavation of epithelial ovarian cancer patients

A tumor-infiltrating immune cells-related pseudogenes signature based on machine-learning predicts outcomes and immunotherapy responses in ovarian cancer

Previous researches have provided evidence for the significant involvement of pseudogenes in immune-related functions across different types of cancer. However, the mechanisms by which pseudogenes regulate immunity in ovarian cancer (OV) and their potential impact on clinical outcomes remain unclear. To address this gap in knowledge, our study utilized a novel computational framework to analyze a total of 491 samples from three public datasets. We employed a combination of 10 machine-learning algorithms to construct a signature known as the tumor-infiltrating immune cells-related pseudogenes signature (TIICPS). The TIICPS, consisting of 12 pseudogenes, demonstrated independent prognostic value for overall survival, surpassing conventional clinical traits, 62 published signatures, and TP53 and BRCA mutation status in three cohorts. Patients with low TIICPS exhibited heightened immune-related pathways, intricate genomic alterations, substantial immune infiltration, and greater potential for immunotherapy efficacy. Consequently, TIICPS holds promise as a predictive tool for prognosis and immunotherapy response in ovarian cancer.

Novel immune-related gene signature for risk stratification and prognosis prediction in ovarian cancer

BACKGROUND

The immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene products played an integrated role in ovarian cancer (OC) progression, proved the significance of the immune microenvironment in prognosis. Therefore, we aimed to establish and validate an immune gene prognostic signature for OC patients' prognosis prediction.

METHODS

Differently expressed Immune-related genes (DEIRGs) were identified in 428 OC and 77 normal ovary tissue specimens from 9 independent GEO datasets. The Cancer Genome Atlas (TCGA) cohort was used as a training cohort, Univariate Cox analysis was used to identify prognostic DEIRGs in TCGA cohort. Then, an immune gene-based risk model for prognosis prediction was constructed using the LASSO regression analysis, and validated the accuracy and stability of the model in 374 and 93 OC patients in TCGA training cohort and International Cancer Genome Consortium (ICGC) validation cohort respectively. Finally, the correlation among risk score model, clinicopathological parameters, and immune cell infiltration were analyzed.

RESULTS

Five DEIRGs were identified to establish the immune gene signature and divided OC patients into the low- and high-risk groups. In TCGA and ICGC datasets, patients in the low-risk group showed a substantially higher survival rate than high-risk group. Receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE) analysis and principal component analysis (PCA) showed the good performance of the risk model. Clinicopathological correlation analysis proved the risk score model could serve as an independent prognostic factor in 2 independent datasets.

CONCLUSIONS

The prognostic model based on immune-related genes can function as a superior prognostic indicator for OC patients, which could provide evidence for individualized treatment and clinical decision making.

Role of autophagy and its regulation by noncoding RNAs in ovarian cancer

Autophagy is a self-digestion process by which misfolded proteins and damaged organelles in eukaryotic cells are degraded to maintain cellular homeostasis. This process is involved in the tumorigenesis, metastasis, and chemoresistance of various tumors such as ovarian cancer (OC). Noncoding RNAs (ncRNAs), mainly including microRNAs, long noncoding RNAs, and circular RNAs, have been extensively investigated in cancer research for their roles in the regulation of autophagy. Recent studies have shown that in OC cells, ncRNAs can modulate the formation of autophagosomes, which affect tumor progression and chemoresistance. An understanding of the role of autophagy in OC progression, treatment, and prognosis is important, and the identification of the regulatory roles of ncRNAs in autophagy leads to intervention strategies for OC therapy. This review summarizes the role of autophagy in OC and discusses the role of ncRNA-mediated autophagy in OC, as an understanding of these roles may contribute to the development of potential therapeutic strategies for this disease.

A novel autophagy-related gene signature associated with prognosis and immune microenvironment in ovarian cancer

Ovarian cancer (OV), the most fatal gynecological malignance worldwide, has high recurrence rates and poor prognosis. Recently, emerging evidence supports that autophagy, a highly regulated multi-step self-digestive process, plays an essential role in OV progression. Accordingly, we filtered 52 potential autophagy-related genes (ATGs) among the 6197 differentially expressed genes (DEGs) identified in TCGA-OV samples (n = 372) and normal controls (n = 180). Based on the LASSO-Cox analysis, we distinguished a 2-gene prognostic signature, namely FOXO1 and CASP8, with promising prognostic value (p-value < 0.001). Together with corresponding clinical features, we constructed a nomogram model for 1-year, 2-year, and 3-year survival, which was validated in both in training (TCGA-OV, p-value < 0.001) and validation (ICGC-OV, p-value = 0.030) cohorts. Interestingly, we evaluated the immune infiltration landscape through the CIBERSORT algorithm, which indicated the upregulation of 5 immune cells, including CD8 + T cells, Tregs, and Macrophages M2, and high expression of critical immune checkpoints (CTLA4, HAVCR2, PDCD1LG2, and TIGIT) in high-risk group. Stepwise, high-risk group exhibited better sensitivity towards chemotherapies of Bleomycin, Sorafenib, Veliparib, and Vinblastine, though less sensitive to immunotherapy. Especially, based on the IHC of tissue microarrays among 125 patients in our institution, we demonstrated that aberrant upregulation of FOXO1 in OV was related to metastasis and poor prognosis. Moreover, FOXO1 could significantly promote tumor invasiveness, migration, and proliferation in OV cell lines, which was assessed through the Transwell, wound-healing, and CCK-8 assay, respectively. Briefly, the autophagy-related signature was a reliable tool to evaluate immune responses and predict prognosis in the realm of OV precision medicine.

A novel defined risk signature of endoplasmic reticulum stress-related genes for predicting the prognosis and immune infiltration status of ovarian cancer

Endoplasmic reticulum (ER) stress, as an emerging hallmark feature of cancer, has a considerable impact on cell proliferation, metastasis, invasion, and chemotherapy resistance. Ovarian cancer (OvCa) is one of the leading causes of cancer-related mortality across the world due to the late stage of disease at diagnosis. Studies have explored the influence of ER stress on OvCa in recent years, while the predictive role of ER stress-related genes in OvCa prognosis remains unexplored. Here, we enrolled 552 cases of ER stress-related genes involved in OvCa from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts for the screening of prognosis-related genes. The least absolute shrinkage and selection operator (LASSO) regression was applied to establish an ER stress-related risk signature based on the TCGA cohort. A seven-gene signature revealed a favorable predictive efficacy for the TCGA, International Cancer Genome Consortium (ICGC), and another GEO cohort (P<0.001, P<0.001, and P=0.04, respectively). Moreover, functional annotation indicated that this signature was enriched in cellular response and senescence, cytokines interaction, as well as multiple immune-associated terms. The immune infiltration profiles further delineated an immunologic unresponsive status in the high-risk group. In conclusion, ER stress-related genes are vital factors predicting the prognosis of OvCa, and possess great application potential in the clinic.

Protein expression profiling identifies a prognostic model for ovarian cancer

Background

Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress.

Methods

Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis.

Results

394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication.

Conclusions

The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.

Maggot Extract Inhibits Cell Migration and Tumor Growth by Targeting HSP90AB1 in Ovarian Cancer

Ovarian cancer is one of the most lethal gynecological malignancies, because of metastatic dissemination with poor late clinical therapy. Maggots have been used in traditional Chinese medicine, where they are also known as 'Wu Gu Chong'. Previous studies have indicated that maggot extract (ME) was beneficial for the treatment of gastric cancer when combined with other drugs, but the effect on anti-ovarian cancer and the underlying mechanism remains unclear. The aim of this study was to investigate the effects of ME on suppressing the proliferation and migration of ovarian cancer cells, and to clarify the underlying mechanism. In this research, Cell Counting Kit-8 (CCK-8), colony formation assay, and luciferase-positive cell quantification assay were employed to identify the inhibitory effects of ME on cell proliferation. Then, the pro-apoptosis and anti-metastasis effects of ME were explored by Western blot, dual annexin V-fluorescein isothiocyanate/propidium iodide (FITC/PI) assay, immunofluorescent staining, and wound-healing assay. We further established a xenograft model by subcutaneously or intraperitoneally injecting BALB/c nude mice with SKOV3 cells stably expressing luciferase, and the mice were treated with ME. The results showed that ME therapy effectively restrained the growth and metastasis of ovarian tumors in vivo. Furthermore, the mRNA levels of cancer factors including heat shock protein 90 alpha family class B member 1 (HSP90AB1), MYC, and insulin like growth factor 1 receptor (IGF1R) were analyzed by quantitative real-time PCR assay to explore the possible antitumor mechanisms of ME. Next, HSP90 ATPase activity was inhibited by geldanamycin in A2780, and the cell viability was shown to be dramatically reduced, decreasing further with the combination of ME and cisplatin. In turn, HSP90AB1 overexpression effectively inhibited the effect of ME in suppressing capability for cell viability and migration. In addition, HSP90AB1 overexpression limited the ability of ME to inhibit expression of MYC and IGF1R, while the opposite effect was observed for expression of pro-apoptosis protein caspase3 and BAX. Therefore, this study confirmed the potential roles and mechanisms of ME in inhibiting the growth and metastasis of ovarian tumors and promoting apoptosis of ovarian cancer cells by inhibiting overexpression of HSP90AB1.

Non-apoptotic cell death in ovarian cancer: Treatment, resistance and prognosis

Ovarian cancer is mostly diagnosed at an advanced stage due to the absence of effective screening methods and specific symptoms. Repeated chemotherapy resistance and recurrence before PARPi are used as maintenance therapies, lead to low survival rates and poor prognosis. Apoptotic cell death plays a crucial role in ovarian cancer, which is proved by current researches. With the ongoing development of targeted therapy, non-apoptotic cell death has shown substantial potential in tumor prevention and treatment, including autophagy, ferroptosis, necroptosis, immunogenic cell death, pyroptosis, alkaliptosis, and other modes of cell death. We systematically reviewed the research progress on the role of non-apoptotic cell death in the onset, development, and outcome of ovarian cancer. This review provides a more theoretical basis for exploring therapeutic targets, reversing drug resistance in refractory ovarian cancer, and establishing risk prediction models that help realize the clinical transformation of vital drugs.

Long non-coding RNA HOXA11-AS knockout inhibits proliferation and overcomes drug resistance in ovarian cancer

In ovarian carcinogenesis and progression, long non-coding RNAs (lncRNAs) have been shown to have a role, although the underlying processes remain a mystery. By modulating the degree of autophagy in ovarian cancer cells, we sought to learn more about the function lncRNA HOXA11-AS plays in the development of ovarian cancer. The expression of HOXA11-AS in ovarian normal cells and ovarian cancer cell lines was measured using R package and qRT-PCR. Ovarian cancer cells expressed HOXA11-AS substantially higher than normal cells, while cisplatin-resistant cells expressed HOXA11-AS significantly higher than ovarian cancer cells. Next, we studied the prognostic data of HOXA11-AS in ovarian cancer in the Tissue Cancer Genome Atlas (TCGA). In the next step, lentiviral transfection of ovarian cancer cells A2780, OVCAR3, and A2780/DDP (cisplatin-resistant) were performed, and HOXA11-AS knockdown was found to significantly inhibit cell viability, migration, and invasion of A2780 and OVCAR3 cells, and promote apoptosis by CCK-8 assay, transwell assay, cell cycle, and apoptosis assay, and promoted the sensitivity of A2780/DDP cells to cisplatin. It has been shown by the western blot test that HOXA11-AS knockdown increases the amount of cellular autophagy in cells. In contrast, adding the autophagy inhibitor 3-methyladenine (3-MA) to HOXA11-AS cells knocked down in vivo reduced its anti-tumor properties. As a whole, this study found that HOXA11-AS knockdown increased the expression of autophagy-related proteins and improved cisplatin sensitivity, decreased ovarian cancer cell proliferation, and promoted cell apoptosis. This study provides new insights into the role of HOXA11-AS in ovarian cancer regulation.

Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer

Background

Ferroptosis and iron-metabolism are regulated by Long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in OC is crucial for proposing therapeutic strategies and survival prediction.

Methods

In multi-omics data from OC patients, FIRLs were identified by calculating Pearson correlation coefficients with ferroptosis and iron-metabolism related genes (FIRGs). Cox-Lasso regression analysis was performed on the FIRLs to screen further the lncRNAs participating in FIRLs signature. In addition, all patients were divided into two robust risk subtypes using the FIRLs signature. Receiver operator characteristic (ROC) curve, Kaplan–Meier analysis, decision curve analysis (DCA), Cox regression analysis and calibration curve were used to confirm the clinical benefits of FIRLs signature. Meanwhile, two nomograms were constructed to facilitate clinical application. Moreover, the potential biological functions of the signature were investigated by genes function annotation. Finally, immune microenvironment, chemotherapeutic sensitivity, and the response of PARP inhibitors were compared in different risk groups using diversiform bioinformatics algorithms.

Results

The raw data were randomized into a training set (n = 264) and a testing set (n = 110). According to Pearson coefficients between FIRGs and lncRNAs, 1075 FIRLs were screened for univariate Cox regression analysis, and then LASSO regression analysis was used to construct 8-FIRLs signature. It is worth mentioning that a variety of analytical methods indicated excellent predictive performance for overall survival (OS) of FIRLs signature (p < 0.05). The multivariate Cox regression analysis showed that FIRLs signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the abundance of immune cells, immune-related pathways, and drug response were excavated in different risk subtypes (p < 0.05).

Conclusion

The FIRLs signature can independently predict overall survival and therapeutic effect in OC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00944-y.

A Novel Autophagy-Related Prognostic Risk Model and a Nomogram for Survival Prediction of Oral Cancer Patients

Background. This study is aimed at constructing a risk signature to predict survival outcomes of ORCA patients. Methods. We identified differentially expressed autophagy-related genes (DEARGs) based on the RNA sequencing data in the TCGA database; then, four independent survival-related ARGs were identified to construct an autophagy-associated signature for survival prediction of ORCA patients. The validity and robustness of the prognostic model were validated by clinicopathological data and survival data. Subsequently, four independent prognostic DEARGs that composed the model were evaluated individually. Results. The expressions of 232 autophagy-related genes (ARGs) in 127 ORCA and 13 control tissues were compared, and 36 DEARGs were filtered out. We performed functional enrichment analysis and constructed protein–protein interaction network for 36 DEARGs. Univariate and multivariate Cox regression analyses were adopted for searching prognostic ARGs, and an autophagy-associated signature for ORCA patients was constructed. Eventually, 4 desirable independent survival-related ARGs (WDR45, MAPK9, VEGFA, and ATIC) were confirmed and comprised the prognostic model. We made use of multiple ways to verify the accuracy of the novel autophagy-related signature for survival evaluation, such as receiver-operator characteristic curve, Kaplan–Meier plotter, and clinicopathological correlational analyses. Four independent prognostic DEARGs that formed the model were also associated with the prognosis of ORCA patients. Conclusions. The autophagy-related risk model can evaluate OS for ORCA patients independently since it is accurate and stable. Four prognostic ARGs that composed the model can be studied deeply for target treatment.