Mathematical Modelling of Cervical Precancerous Lesion Grade Risk Scores: Linear Regression Analysis of Cellular Protein Biomarkers and Human Papillomavirus E6/E7 RNA Staining Patterns

Abstract

The current practice of determining histologic grade with a single molecular biomarker can facilitate differential diagnosis but cannot predict the risk of lesion progression. Cancer is caused by complex mechanisms, and no single biomarker can both make accurate diagnoses and predict progression risk. Modelling using multiple biomarkers can be used to derive scores for risk prediction. Mathematical models (MMs) may be capable of making predictions from biomarker data. Therefore, this study aimed to develop MM–based scores for predicting the risk of precancerous cervical lesion progression and identifying precancerous lesions in patients in northern Thailand by evaluating the expression of multiple biomarkers. The MMs (Models 1–5) were developed in the test sample set based on patient age range (five categories) and biomarker levels (cortactin, p16INK4A, and Ki–67 by immunohistochemistry [IHC], and HPV E6/E7 ribonucleic acid (RNA) by in situ hybridization [ISH]). The risk scores for the prediction of cervical lesion progression (“risk biomolecules”) ranged from 2.56–2.60 in the normal and low–grade squamous intraepithelial lesion (LSIL) cases and from 3.54–3.62 in cases where precancerous lesions were predicted to progress. In Model 4, 23/86 (26.7%) normal and LSIL cases had biomolecule levels that suggested a risk of progression, while 5/86 (5.8%) cases were identified as precancerous lesions. Additionally, histologic grading with a single molecular biomarker did not identify 23 cases with risk, preventing close patient monitoring. These results suggest that biomarker level–based risk scores are useful for predicting the risk of cervical lesion progression and identifying precancerous lesion development. This multiple biomarker–based strategy may ultimately have utility for predicting cancer progression in other contexts.

A Prospective Study on the Progression, Recurrence, and Regression of Cervical Lesions: Assessing Various Screening Approaches

“…Moreover, these results indicate the need for inclusion of more sensitive tests, which can outperform the classical approaches. This finding is supported by recent studies, which outline a higher predictive performance of various molecular or methylation markers [33,34].…”

“…HPV testing was performed on cervical samples using Allplex™ PCR System (Seegene Inc., Songpa-gu, Seoul, Republic of Korea) for detection of human papillomavirus-19 highrisk HPV types (16,18,26,31,33,35,39,45,51,52,53,56,58,59, 66, 68, 69, 73, 82) and 9 low-risk HPV types (6,11,40,42,43,44,54,61,70).…”

Utility of four machine learning approaches for identifying ulcerative colitis and Crohn's disease

“…Machine learning (ML) is an area of study that uses algorithmic techniques to analyze data, having the potential to combine with multiple biomarkers to diagnose, predict the severity of diseases, and monitor the progression of diseases [ 24 ]. Recent research has shown that ML algorithms are more advantageous than traditional statistical methods when constructing predictive models.…”

Serum Lipids, Insulin-Like Growth Factor Binding Protein-3 and Treatment Outcomes in Women with and without Cervical Lesions in South Western Uganda: A Cohort Study

“…In agreement with our finding, studies on metabolic syndrome components have hypothesized that increased triglyceride concentrations can increase a person's risk of developing not only cardiovascular diseases but also other non-communicable diseases, including cervical cancer. 33 Research by Bumrungthai et al 34 indicates that an abnormal serum triglyceride concentration is associated with an increased risk of development of cervical precancerous lesions. Dyslipidemia, in common with other components of metabolic syndrome such as obesity, elevates estrogen levels, 35 which, in turn, can increase the risk of HPV infection.…”