Chemotherapy-Related Cerebral Venous Sinus Thrombosis in a Colon Cancer Patient Receiving Adjuvant Chemotherapy: Case Report and Literature Review

Oxaliplatin-combination treatment has been adopted as a standard adjuvant treatment for high-risk stage II and stage III colorectal cancer. Cerebral venous sinus thrombosis (CVST) is a serious adverse event related to this combination treatment. The benefits of this combination treatment outweigh the risks, yet some physicians are reluctant to resume the treatment after the clot has resolved. The authors reported a case of CVST, and the success in resolving this situation with the use of a secondary prophylaxis, a low-molecular weight heparin. Keywords: Oxaliplatin; Central venous sinus thrombosis; Chemotherapy-induced VTE

Identification of a Nervous System Gene Expression Signature in Colon Cancer Stem Cells Reveals a Role for Neural Crest Regulators EGR2 and SOX2 in Tumorigenesis

SUMMARYRecent data support a hierarchical model of colon cancer driven by a population of cancer stem cells (CSCs). Greater understanding of the mechanisms that regulate CSCs may therefore lead to more effective treatments. Serial limiting dilution xenotransplantation assays of colon cancer patient-derived tumors demonstrated ALDHPositive cells to be enriched for tumorigenic self-renewing CSCs. In order to identify CSC modulators, we performed RNA-sequencing analysis of ALDHPositive CSCs from a panel of colon cancer patient-derived organoids (PDOs) and xenografts (PDXs). These studies demonstrated CSCs to be enriched for embryonic and neural development gene sets. Functional analyses of genes differentially expressed in both ALDHPositive PDO and PDX CSCs demonstrated the neural crest stem cell (NCSC) regulator and wound response gene EGR2 to be required for CSC tumorigenicity and to control expression of homeobox superfamily embryonic master transcriptional regulator HOX genes and the embryonic and neural stem cell regulator SOX2. In addition, we identify EGR2, HOXA2, HOXA4, HOXA5, HOXA7, HOXB2, HOXB3 and the tumor suppressor ATOH1 as new prognostic biomarkers in colorectal cancer.

Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice

Background Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary. Methods We implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients. Results A five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis. Conclusions Transcription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective.