A Comprehensive Risk Assessment Model for Ovarian Cancer Patients with Phospho-STAT3 and IL-31 as Immune Infiltration Relevant Genes

The Associations and Causal Relationships of Ovarian Cancer - Construction of a Prediction Model

Purpose

To explore the risk and protective factors for developing ovarian cancer and construct a risk prediction model.

Methods

Information related to patients diagnosed with ovarian cancer on the electronic medical record data platform of three tertiary hospitals in Guangdong Province from May 2018 to September 2023 was collected as the case group. Patients with non-ovarian cancer who attended the clinic during the same period were included in the control group. Logistic regression analysis was used to screen the independent variables and explore the factors associated with the development of ovarian cancer. An ovarian cancer risk prediction model was constructed using a decision tree C4.5 algorithm. The ROC and calibration curves were plotted, and the model was validated.

Results

Logistic regression analysis identified independent risk and protective factors for ovarian cancer. The sample size was divided into training and test sets in a ratio of 7:3 for model construction and validation. The AUC of the training and test sets of the decision tree model were 0.961 (95% CI:0.944-0.978) and 0.902 (95% CI:0.840-0.964), respectively, and the optimal cut-off values and their coordinates were 0.532 (0.091, 0.957), and 0.474 (0.159, 0.842) respectively. The accuracies of the training and test sets were 93.3% and 84.2%, respectively, and their sensitivities were 95.7% and 84.2%, respectively.

Conclusion

The constructed ovarian cancer risk prediction model has good predictive ability, which is conducive to improving the efficiency of early warning of ovarian cancer in high-risk groups.

A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles

Even after debulking surgery combined with chemotherapy or new adjuvant chemotherapy paired with internal surgery, the average year of disease free survival in advanced ovarian cancer was approximately 1.7 years1. The development of a molecular predictor of early recurrence would allow for the identification of ovarian cancer (OC) patients with high risk of relapse. The Ovarian Cancer Disease Free Survival Predictor (ODFSP), a predictive model constructed from a special set of 1580 OC tumors in which gene expression was assessed using both microarray and sequencing platforms, was created by our team. To construct gene expression barcodes that were resistant to biases caused by disparate profiling platforms and batch effects, we employed a meta-analysis methodology that was based on the binary gene pair technique. We demonstrate that ODFSP is a reliable single-sample predictor of early recurrence (1 year or less) using the largest pool of OC transcriptome data sets available to date. The ODFSP model showed significantly high prognostic value for binary recurrence prediction unaffected by clinicopathologic factors, with a meta-estimate of the area under the receiver operating curve of 0.64 (P  =  4.6E-05) and a D-index (robust hazard ratio) of 1.67 (P  =  9.2E-06), respectively. GO analysis of ODFSP's 2040 gene pairs (collapsed to 886 distinct genes) revealed the involvement in small molecular catabolic process, sulfur compound metabolic process, organic acid catabolic process, sulfur compound biosynthetic process, glycosaminoglycan metabolic process and aminometabolic process. Kyoto encyclopedia of genes and genomes pathway analysis of ODFSP's signature genes identified prominent pathways that included cAMP signaling pathway and FoxO signaling pathway. By identifying individuals who might benefit from a more aggressive treatment plan or enrolment in a clinical trial but who will not benefit from standard surgery or chemotherapy, ODFSP could help with treatment decisions.

IL-31: State of the Art for an Inflammation-Oriented Interleukin

Interleukin 31 belongs to the IL-6 superfamily, and it is an itch mediator already studied in several diseases, comprising atopic dermatitis, allergic pathologies, and onco-hematological conditions. This research aims to assess the role of this cytokine in the pathogenesis of these conditions and its potential therapeutic role. The research has been conducted on articles, excluding reviews and meta-analysis, both on animals and humans. The results showed that IL-31 plays a crucial role in the pathogenesis of systemic skin manifestations, prognosis, and itch severity. Traditional therapies target this interleukin indirectly, but monoclonal antibodies (Mab) directed against it have shown efficacy and safety profiles comparable with biological drugs that are already available. Future perspectives could include the development of new antibodies against IL-31 both for humans and animals, thus adding a new approach to the therapy, which often has proven to be prolonged and specific for each patient.