Age as a prognostic factor in cervical cancer: A 10-year review of patients treated at a single institution

Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer

Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than $ {44000}$ inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders.

Prediction scoring system based on clinicohematologic parameters for cervical cancer patients undergoing chemoradiation

OBJECTIVE

A scoring system based on clinicohematologic parameters in cervical cancer patients receiving chemoradiation has not been reported to date. The aim of this study was to determine the prognostic value of clinicohematologic parameters in patients with cervical cancer undergoing chemoradiation and to develop a prediction scoring system based on these results.

METHODS

A total of 107 patients who received definitive chemoradiation for cervical cancer were enrolled in this study. The clinical data and hematologic parameters were retrospectively reviewed, and their prognostic value in predicting survival was analyzed. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) and the changes in these hematologic parameters (ΔNLR, ΔPLR, and ΔLMR) between pre- and post-treatment were calculated to determine the specific value of these parameters for predicting patient survival.

RESULTS

The median follow-up time was 39.9 (range 2.7-114.6) months. The 3-year overall survival rate and progression-free survival rate were 80.9% (95% CI 72.7 to 90.0) and 53.4% (95% CI 44.1 to 64.8), respectively. The median progression-free survival was 67.5 months and the median overall survival was not reached. According to multivariable analysis, a ΔNLR≥0 was significantly associated with decreased progression-free survival (HR=2.91, 95% CI 1.43 to 5.94) and overall survival (HR=3.13, 95% CI 1.18 to 8.27). In addition, age (age <58.5 years; progression-free survival: HR=2.55, 95% CI 1.38 to 4.70; overall survival: HR=4.49, 95% CI 1.78 to 11.33) and the International Federation of Gynecology and Obstetrics (FIGO) stage (Ⅲ-Ⅳ; progression-free survival: HR=2.49, 95% CI 1.40 to 4.43; overall survival: HR=3.02, 95% CI 1.32 to 6.90) were identified as predictors of poor survival.

CONCLUSIONS

Both the age and FIGO stage, as clinical parameters, and the ΔNLR, as a hematologic parameter, were independent prognostic factors for survival for cervical cancer patients treated with chemoradiation. Based on these results, we developed a risk score-based classification system for predicting survival.

Epigenetic age acceleration of cervical squamous cell carcinoma converged to human papillomavirus 16/18 expression, immunoactivation, and favourable prognosis

BACKGROUND

Ageing-associated molecular changes have been assumed to trigger malignant transformations and the epigenetic clock, and the DNA methylation age has been shown to be highly correlated with chronological age. However, the associations between the epigenetic clock and cervical squamous cell carcinoma (CSCC) prognosis, other molecular characteristics, and clinicopathological features have not been systematically investigated. To this end, we computed the DNA methylation (DNAm) age of 252 CSCC patients and 200 normal samples from TCGA and three external cohorts by using the Horvath clock model. We characterized the differences in human papillomavirus (HPV) 16/18 expression, pathway activity, genomic alteration, and chemosensitivity between two DNAm age subgroups. We then used Cox proportional hazards regression and restricted cubic spline (RCS) analysis to assess the prognostic value of epigenetic acceleration.

RESULTS

DNAm age was significantly associated with chronological age, but it was differentiated between tumour and normal tissue (P < 0.001). Two DNAm age groups, i.e. DNAmAge-ACC and DNAmAge-DEC, were identified; the former had high expression of the E6/E7 oncoproteins of HPV16/18 (P < 0.05), an immunoactive phenotype (all FDRs < 0.05 in enrichment analysis), CpG island hypermethylation (P < 0.001), and lower mutation load (P = 0.011), including for TP53 (P = 0.002). When adjusted for chronological age and tumour stage, every 10-year increase in DNAm age was associated with a 12% decrease in fatality (HR 0.88, 95% CI 0.78-0.99, P = 0.03); DNAmAge-ACC had a 41% lower mortality risk and 47% lower progression rate than DNAmAge-DEC and was more likely to benefit from chemotherapy. RCS revealed a positive non-linear association between DNAm age and both mortality and progression risk (both, P < 0.05).

CONCLUSIONS

DNAm age is an independent predictor of CSCC prognosis. Better prognosis, overexpression of HPV E6/E7 oncoproteins, and higher enrichment of immune signatures were observed in DNAmAge-ACC tumours.