Elastic Net-Based Identification of a Multigene Combination Predicting the Survival of Patients with Cervical Cancer

Integrated proteomics and scRNA-seq analyses of ovarian cancer reveal molecular subtype-associated cell landscapes and immunotherapy targets

BACKGROUND

Epithelial ovarian cancer (EOC) represents the most lethal gynaecological malignancy, yet understanding the connections between its molecular subtypes and their therapeutic implications remains incomplete.

METHODS

We conducted mass spectrometry-based proteomics analyses of 154 EOC tumour samples and 29 normal fallopian tubes, and single-cell RNA sequencing (scRNA-seq) analyses of an additional eight EOC tumours to classify proteomic subtypes and assess their cellular ecosystems and clinical significance. The efficacy of identified therapeutic targets was evaluated in patient-derived xenograft (PDX) and orthotopic mouse models.

RESULTS

We identified four proteomic subtypes with distinct clinical relevance: malignant proliferative (C1), immune infiltrating (C2), Fallopian-like (C3) and differentiated (C4) subtypes. C2 subtype was characterized by lymphocyte infiltration, notably an increased presence of GZMK CD8+ T cells and phagocytosis-like MRC+ macrophages. Additionally, we identified CD40 as a specific prognostic factor for C2 subtype. The interaction between CD40+ phagocytosis-like macrophages and CD40RL+ IL17R CD4+ T cells was correlated with a favourable prognosis. Finally, we established a druggable landscape for non-immune EOC patients and verified a TYMP inhibitor as a promising therapeutic strategy.

CONCLUSIONS

Our study refines the current immune subtype for EOC, highlighting CD40 agonists as promising therapies for C2 subtype patients and targeting TYMP for non-immune patients.

Nomogram model for predicting postoperative survival of patients with stage IB-IIA cervical cancer

To establish a prediction model based on clinical and pathological information for the long-term survival of patients with cervical cancer, we retrospectively analyzed the clinical data of patients pathologically diagnosed with stage IB-IIA cervical cancer between July 2007 and September 2017 in the Chinese Academy of Medical Sciences Cancer Hospital. Factors affecting the overall survival of the patients were analyzed using a Cox model, and a cervical cancer patient prediction nomogram model was established. A total of 2,319 patients were included in the study. According to the multivariate Cox regression analysis, number of complications, surgical methods, neoadjuvant treatment, lymph node metastasis, postoperative treatment, lymphovascular space invasion (LVSI), and other independent factors affecting prognosis were included to establish a nomogram. The nomogram consistency index in the training and validation cohorts was 0.691 and 0.615, respectively. The study established a highly accurate predictive model for the postoperative survival of cervical cancer patients.

Identifying a cervical cancer survival signature based on mRNA expression and genome-wide copy number variations

Cervical cancer mortality is the second highest in gynecological cancers. This study developed a new model based on copy number variation data and mRNA data for overall survival prediction of cervical cancer. Differentially expressed genes from The Cancer Genome Atlas dataset detected by univariate Cox regression analysis were further simplified to six by least absolute shrinkage and selection operator (Lasso) and stepwise Akaike information criterion (stepAIC). The study developed a six-gene signature, which was further verified in independent dataset. Association between immune infiltration and risk score was investigated by immune score. The relation between the signature and functional pathways was examined by gene set enrichment analysis. Ninety-nine differentially expressed genes were detected, and C11orf80, FOXP3, GSN, HCCS, PGAM5, and RIBC2 were identified as key genes to construct a six-gene signature. The prognostic signature showed a significant correlation with overall survival (hazard ratio, HR = 3.45, 95% confidence interval (CI) = 2.08-5.72, p < 0.00001). Immune score showed a negative correlation with the risk score calculated by the signature (p < 0.05). Four immune-related pathways were closely associated with risk score (p < 0.0001). The six-gene prognostic signature was an effective tool to predict overall survival of cervical cancer. In conclusion, the newly identified six genes may be considered as new drug targets for cervical cancer treatment.