Identification of Potential Biomarkers and Biological Pathways for Poor Clinical Outcome in Mucinous Colorectal Adenocarcinoma

Abstract

Colorectal cancer (CRC) comprises several histological subtypes, but the influences of the histological subtypes on prognosis remains unclear. We sought to evaluate the prognosis of mucinous adenocarcinoma (MAC), compared to that of traditional adenocarcinoma (TAC). This study used the data of patients diagnosed with CRC between 2004 and 2016, as obtained from the Surveillance, Epidemiology, and End Results database. We established a predictive model for disease-specific survival using conditional survival forest, model, non-linear Cox proportional hazards, and neural multi-task logistic regression model and identified the gene signatures for predicting poor prognosis based on the arrayexpress datasets. In total, 9096 (42.1%) patients with MAC and 12,490 (58.9%) patients with TAC were included. Those with the MAC subtype were more likely to have a poorer overall survival rate compared to those with the TAC subtype in stage II CRC (p = 0.002). The eight major genes including RPS18, RPL30, NME2, USP33, GAB2, RPS3A, RPS25, and CEP57 were found in the interacting network pathway. MAC was found to have a poorer prognosis compared to TAC, especially in Stage II CRC. In addition, our findings suggest that identifying potential biomarkers and biological pathways can be useful in CRC prognosis.

Single-cell RNA sequencing combined with mendelian randomization analysis identifies putatively causal genes associated with Gastric cancer

“…Ribosomal protein S18(RPS18), also known as HKE3, is a type of ribosomal protein which has been supposed to an unfavorable predictor in several cancers including breast cancer (BC) 23 and colorectal cancer (CRC) 24 . Besides, RPS18 which was related to the recurrence and prognosis of prostate cancer (PCa) was shown to be a potential prognostic biomarker of PCa 25 .…”

Identification and validation of a novel six-gene signature based on mucinous adenocarcinoma-related gene molecular typing in colorectal cancer

“…Previous studies have identified genes with important biological roles for MAC in CRC, such as RPS18 , RPL30 , CXCL9, and IDO1 [ 46 , 47 ]. Machine learning has been applied extensively in biomedicine.…”

Deep Learning Techniques to Characterize the RPS28P7 Pseudogene and the Metazoa-SRP Gene as Drug Potential Targets in Pancreatic Cancer Patients

“…In spite of some authors considering RPL30 as a classical reference gene for cancer research due to its stable expression [ 46 ], our results showed that the overexpression of RPL30 is a hallmark of the dead group in PaCa, being the highest overexpressed gene in this GDC cohort (26.06-fold on average). Likewise, the RPL30 gene has been suggested as one out of eight major genes that predict poor clinical outcomes in mucinous colorectal adenocarcinoma [ 47 ] and is also informative for lethality in medulloblastoma [ 48 ]. In addition, RPL30 is one of seven genes whose expression levels have been proposed for diagnosing prostate cancer [ 49 ].…”