Transcriptional information underlying the generation of CSCs and the construction of a nine-mRNA signature to improve prognosis prediction in colorectal cancer

Abstract

Background: Despite recent progress in screening survival-related genes, there have been few attempts to apply methods based on cancer stem cells (CSCs) for prognosis. We aimed to identify a CSC-based model to predict survival in colorectal cancer (CRC) patients. Material/Methods: Differentially expressed genes between CRC and normal tissues and between CD133-and CD133+ cells were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, and intersections were evaluated. Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyzes were performed. STRING was used to investigate interactions between the encoded proteins and the Kaplan-Meier method to verify mRNAs associated with survival. A prognostic model based on CSCs was established via univariate and multivariate Cox regression. Receiver operating characteristic curve analysis was conducted to test the model's sensitivity and specificity. The KS test was applied to provide evidence for relationships between expression levels of nine mRNAs in our model and pathological stage. Results: In total, 155 common differentially expressed mRNAs were identified, and nine (AOC1, UCN, MTUS1, CDC20, SNCB, MAT1A, TUBB2B, GABRA4 and ALPP) were screened after regression analyses to establish a predictive model for classifying patients into high-and low-risk groups with significantly different overall survival times, especially for stage II and IV patients. Conclusions: We developed a novel model that provides additional and powerful prognostic information beyond conventional clinicopathological factors for CRC survival prediction. It also provides new insight into the molecular mechanisms underlying the transition from normal tissues to CSCs and formation of tumor tissues.

Microtubule-Associated Protein ATIP3, an Emerging Target for Personalized Medicine in Breast Cancer

“…Furthermore, p53-regulation of ATIP1 transcripts suggested a link between MTUS1 gene regulation and cancer [ 18 ]. Indeed, MTUS1 down-regulation in cancer tissues was frequently reported, including in tumors from the breast [ 19 , 20 , 21 , 22 , 23 ], bladder [ 24 , 25 ], colon [ 26 , 27 , 28 , 29 ], gallbladder [ 30 ], gastric tissues [ 31 , 32 ], lung (NSCLC) [ 33 ], head-and-neck [ 34 , 35 , 36 , 37 , 38 , 39 ], clear cell renal cell carcinoma (cc-RCC) [ 40 , 41 ], and uveal melanoma [ 42 ], with the exception of prostate cancer, in which MTUS1 expression was reported to increase with cancer progression [ 43 , 44 ] ( Table 1 ). Only few studies were designed to discriminate between different ATIP isoforms, and they all pointed to ATIP3 as the major MTUS1 isoform altered in human malignancies [ 19 , 21 , 36 , 43 ], ATIP1 being a minor form expressed in normal peripheral tissues [ 14 ].…”

Role of stemness‐related genes TIMP1, PGF, and SNAI1 in the prognosis of colorectal cancer through single‐cell RNA‐seq

“…Although treatment strategies such as surgery, chemotherapy, radiotherapy, immunotherapy, and targeted therapy have certain inhibitory effects on the development of CRC, the survival rate of CRC patients is still very low, 2 and varies greatly for patients at different pathological stages. Therefore, there are still great challenges for the prognosis of CRC 3 . In addition, the heterogeneity of CRC makes the decisions on its treatment difficult 4,5 .…”

“…Therefore, there are still great challenges for the prognosis of CRC. 3 In addition, the heterogeneity of CRC makes the decisions on its treatment difficult. 4 , 5 Although many biomarkers associated with the prognosis of CRC have been previously reported, 6 it is still of great significance to find new prognostic biomarkers of CRC as its potential therapeutic targets.…”

Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

“…In the past decades, a lot of data mining analysis of mRNA, microRNA, long non-coding RNA, and DNA methylation have been performed on human cancers, including colon cancer [16][17][18][19]. As the biomarkers identified by the above techniques are of diagnostic and prognostic values in cancers and the revolution of sequencing technologies and bioinformatics tools facilitates the identification of more potential biomarkers related to disease progression [20][21][22][23], the more potential biomarkers identified, the more recognition and options for the diagnosis and treatment of colon cancer.…”