A robust microsatellite instability detection model for unpaired colorectal cancer tissue samples

Background:Microsatellite instability(MSI)is a key biomarker for cancer immunotherapy and prognosis.Integration of MSI testing into a next-generation-sequencing(NGS)panel could save tissue sample,reduce turn-around time and cost,and provide MSI status and comprehensive genomic profiling in single test.We aimed to develop an MSI calling model to detect MSI status along with the NGS panel-based profiling test using tumor-only samples.Methods:From January 2019 to December 2020,a total of 174 colorectal cancer(CRC)patients were enrolled,including 31 MSI-high(MSI-H)and 143 microsatellite stability(MSS)cases.Among them,56 paired tumor and normal samples(10 MSI-H and 46 MSS)were used for modeling,and another 118 tumor-only samples were used for validation.MSI polymerase chain reaction(MSI-PCR)was performed as the gold standard.A baseline was built for the selected microsatellite loci using the NGS data of 56 normal blood samples.An MSI detection model was constructed by analyzing the NGS data of tissue samples.The performance of the model was compared with the results of MSI-PCR.Results:We first intersected the target genomic regions of the NGS panels used in this study to select common microsatellite loci.A total of 42 loci including 23 mononucleotide repeat sites and 19 longer repeat sites were candidates for modeling.As mononucleotide repeat sites are more sensitive and specific for detecting MSI status than sites with longer length motif and the mononucleotide repeat sites performed even better than the total sites,a model containing 23 mononucleotide repeat sites was constructed and named Colorectal Cancer Microsatellite Instability test(CRC-MSI).The model achieved 100%sensitivity and 100%specificity when compared with MSI-PCR in both training and validation sets.Furthermore,the CRC-MSI model was robust with the tumor content as low as 6%.In addition,8 out of 10 MSI-H samples showed alternations in the four mismatch repair genes(MLH1,MSH2,MSH6,and PMS2).Conclusion:MSI status can be accurately determined along the targeted NGS panels using only tumor samples.The performance of mononucleotide repeat sites surpasses loci with longer repeat motif in MSI calling.

Transcriptional Regulation of YP3680 by Rov A and Rcs AB in Yersinia pestis biovar Microtus

Type-VI secretion system(T6SS)is a widespread bacteriophage-like complex in bacteria that participates in multiple physiological processes,including metal ion uptake,bacterial competition,and biofilm formation^([1]).Yersinia pestis is the causative agent of plague.There are five T6SS gene clusters in Y.pestis CO92.

Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients

Background: Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. Methods: In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). Results: A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. Conclusions: A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.