A Qualitative Transcriptional Signature for the Risk Assessment of Precancerous Colorectal Lesions

Abstract

It is meaningful to assess the risk of cancer incidence among patients with precancerous colorectal lesions. Comparing the within-sample relative expression orderings (REOs) of colorectal cancer patients measured by multiple platforms with that of normal colorectal tissues, a qualitative transcriptional signature consisting of 1,840 gene pairs was identified in the training data. Within an evaluation dataset of 16 active and 18 inactive (remissive) ulcerative colitis subjects, the median incidence risk score of colorectal carcinoma was 0.6402 in active ulcerative colitis subjects, significantly higher than that in remissive subjects (0.3114). Evaluation of two other independent datasets yielded similar results. Moreover, we found that the score significantly positively correlated with the degree of dysplasia in the case of colorectal adenomas. In the merged dataset, the median incidence risk score was 0.9027 among high-grade adenoma samples, significantly higher than that among low-grade adenomas (0.8565). In summary, the developed incidence risk score could well predict the incidence risk of precancerous colorectal lesions and has value in clinical application.

Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning

“…In contrast, the biomarkers based on the within-sample relative expression orderings (REOs) of genes have been reported to be robust against batch effects ( Guan et al., 2018 ), monotone data normalization ( Eddy et al., 2010 ; Wang et al., 2013 ), and poor sample preparation ( Chen et al., 2017 ; Cheng et al., 2017 ; Liu et al., 2017 ). Moreover, the prognostic value and classification performance have been widely validated in different cancer types ( Ao et al., 2018 ; Chen et al., 2020 ; Guan et al., 2020 ; Lin et al., 2009 ; Qi et al., 2016 ). Thus, it is a promising alternative way to use the REOs-based methods to develop classification or prognostic biomarkers for ccRCC.…”

A 41-Gene Pair Signature for Predicting the Pathological Response of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiation

“…In this study, we identified a predictive signature for the LARC response to nCRT based on REO and random forest algorithms. In previous studies, the REO algorithm showed resistance to experimental batch effects (7). Based on this, a more efficient and robust strategy (random forest) was used to identify our signature, which solved the limitations of our previous method regarding the selection of starting features (17).…”

“…Several prediction models based on tumor tissue expression profiles have shown high accuracy on their respective datasets (5)(6)(7)(8)(9)(10)(11)(12), but the high variability and batch effects make it difficult to apply these predictive models to independent data (13). In addition, the standardization process of adjusting batch effects in gene expression profiling requires the collection of a certain number of samples, which delays the subsequent treatment of patients in clinical practice (14).…”

Bioinformatics Analysis of RNA-seq Data Reveals Genes Related to Cancer Stem Cells in Colorectal Cancerogenesis

“…In the present study, we identified six CSC-related genes associated with CRC development by analyzing RNA-seq data from GEO and TCGA databases, comparing data of normal mucosa, adenoma, and CRC. Previous bioinformatics studies on CRC have identified many gene expression profiles mainly between normal tissue, carcinoma, and metastases; however, studies on gene expression profiles between adenoma and carcinoma are less common [ 22 , 23 , 26 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 49 , 50 , 51 , 52 , 53 ]. The DEGs between adenoma and carcinoma are important for identification of potential biomarkers of malignant transformation [ 22 , 23 , 38 , 39 , 40 , 42 , 43 , 44 , 51 , 52 , 53 ], some of which were previously identified by our group, by analyzing publicly available data on microarray analyses from the GEO database [ 26 ].…”

“…Publications investigating colorectal adenoma to adenocarcinoma and normal mucosa were in the past mainly focused on proteomic analyses and more recently mainly on the serum and plasma expression profiling from CRC patients [ 29 , 34 , 35 ]. However, analyses of gene expression investigating adenoma in comparison to adenocarcinoma are limited and have thus far not elucidated the CSC involvement in CRC development and progression [ 20 , 22 , 23 , 26 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].…”