Immune-related gene expression signatures in colorectal cancer

miR-182, miR-221 and miR-222 are potential urinary extracellular vesicle biomarkers for canine urothelial carcinoma

Current diagnostic methods for canine urothelial carcinoma (UC) are technically challenging or can lack specificity, hence there is a need for novel biomarkers of UC. To this end, we analysed the microRNA (miRNA) cargo of extracellular vesicles (EVs) from urine samples of dogs with UC to identify candidate miRNA biomarkers. Urine was fractionated using ultrafiltration combined with size-exclusion chromatography and small RNA sequencing analysis was performed on both the EV enriched and (EV free) protein fractions. A greater number of candidate miRNA biomarkers were detected in the EV fraction than the protein fraction, and further validation using droplet digital PCR (ddPCR) was performed on the EV enriched fraction of a second cohort of dogs with UC which indicated that miR-182, miR-221 and miR-222 were significantly overrepresented in dogs with UC when compared with healthy dogs and dogs with urinary tract infections. Pathway analysis confirmed that these three miRNAs are involved in cancer. In addition, their potential downstream gene targets were predicted and PIK3R1, a well-known oncogene is likely to be a shared target between miRNA-182 and miRNA-221/222. In summary, this study highlights the potential of urinary EV-associated miRNAs as a source of biomarkers for the diagnosis of canine UC.

CEBPB-mediated upregulation of SERPINA1 promotes colorectal cancer progression by enhancing STAT3 signaling

Colorectal cancer (CRC) is a highly malignant carcinoma associated with poor prognosis, and metastasis is one of the most common causes of death in CRC. Serpin Family A Member 1 (SERPINA1) is a serine protease inhibitor from the Serpin family. Till now, the function and mechanism of SERPINA1 in CRC progression have not been fully illustrated. We established highly metastatic colorectal cancer cells named as RKO-H and Caco2-H by mice liver metastasis model. By integrative bioinformatic approaches, we analyzed the prognostic value and clinical significance of SERPINA1 in CRC, and predicted potential transcription factors. Colony formation, EDU, MTS, Transwell and wound healing assay were performed to evaluate the biological functions of SERPINA1 in CRC in vitro. Experiments in vivo were conducted to explore the effects of SERPINA1 on liver metastasis of CRC. ChIP and luciferase reporter gene assays were performed to identify the transcriptional regulatory mechanism of SERPINA1 by CEBPB. Our results show that SERPINA1 is highly expressed in CRC and correlated with poor clinical outcomes. SERPINA1 promotes the proliferation, migration by activating STAT3 pathway. Mechanistically, CEBPB binds SERPINA1 gene promoter sequence and promotes the transcription of SERPINA1. SERPINA1 drives CEBPB-induced tumor cell growth and migration via augmenting STAT3 signaling. Our results suggest that SERPINA1 is a potential prognostic marker and may serve as a novel treatment target for CRC.

RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation

BACKGROUND

Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg).

METHODS

Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets.

RESULTS

Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival.

CONCLUSION

The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.

Development of novel models for predicting mismatch repair protein deficiency and relevant disease-free survival in colorectal cancer patients

PURPOSE

DNA mismatch repair (MMR) protein deficiency has attached more attention for its potential to be a biomarker of immunotherapy for colorectal cancer (CRC) patients. However, clinical models involving the expression status of MMR protein are rare. Herein, we sought to develop two clinical models (a diagnostic model for the prediction of MMR status and a prognostic model for the prediction of disease-free survival) for CRC patients.

METHODS

A total of 582 CRC patients were finally included. There were 53 patients with deficient expression of MMR protein. The differences between the deficient MMR (dMMR) group and the proficient MMR (pMMR) group were analyzed.

RESULTS

Compared to pMMR patients, those with dMMR status were younger and had better pathological features (depth of invasion, lymph node metastasis, distant metastasis, pathological stage, perineuronal invasion, and PLT level) and disease-free survival (DFS). The tumor location of the left colon, adenocarcinoma, and abnormal PLT level were identified as the independent predictors for pMMR. Based on these data, we developed the diagnostic model using Logistic regression analysis. It showed a satisfactory accuracy (AUC = 82.3% in the derivate set; AUC = 73.6% in the validation set). Furthermore, pMMR, poorer differentiation, perineuronal invasion, distant metastasis, lower hemoglobin level, and abnormal CEA level were established as the independent prognostic factors of poorer DFS. Based on them, a prognostic model with valuable performance (1-year AUC = 75.5%/3-year AUC = 76.9% in the derivate set; 1-year AUC = 72.3%/3-year AUC = 73.8% in the validation set) was developed.

CONCLUSIONS

Our diagnostic and prognostic models could identify CRC patients at risk for pMMR protein expression and disease recurrence. It may contribute to improving the diagnosis and treatment of CRC patients at an individual level.