miR-4636 inhibits tumor cell proliferation, migration and invasion, and serves as a candidate clinical biomarker for gastric cancer

Identification of differentially expressed genes and SNPs linked to harvest body weight of genetically improved rohu carp, Labeo rohita

In most of the aquaculture selection programs, harvest body weight has been a preferred performance trait for improvement. Molecular interplay of genes linked to higher body weight is not elucidated in major carp species. The genetically improved rohu carp with 18% average genetic gain per generation with respect to harvest body weight is a promising candidate for studying genes' underlying performance traits. In the present study, muscle transcriptome sequencing of two groups of individuals, with significant difference in breeding value, belonging to the tenth generation of rohu carp was performed using the Illumina HiSeq 2000 platform. A total of 178 million paired-end raw reads were generated to give rise to 173 million reads after quality control and trimming. The genome-guided transcriptome assembly and differential gene expression produced 11,86,119 transcripts and 451 upregulated and 181 downregulated differentially expressed genes (DEGs) between high-breeding value and low-breeding value (HB & LB) groups, respectively. Similarly, 39,158 high-quality coding SNPs were identified with the Ts/Tv ratio of 1.23. Out of a total of 17 qPCR-validated transcripts, eight were associated with cellular growth and proliferation and harbored 13 SNPs. The gene expression pattern was observed to be positively correlated with RNA-seq data for genes such as myogenic factor 6, titin isoform X11, IGF-1 like, acetyl-CoA, and thyroid receptor hormone beta. A total of 26 miRNA target interactions were also identified to be associated with significant DETs (p-value < 0.05). Genes such as Myo6, IGF-1-like, and acetyl-CoA linked to higher harvest body weight may serve as candidate genes in marker-assisted breeding and SNP array construction for genome-wide association studies and genomic selection.

A Six-microRNA Signature Nomogram for Preoperative Prediction of Tumor Deposits in Colorectal Cancer

Purpose

Tumor deposits (TDs) are acknowledged negative prognostic factors in colorectal cancer (CRC), and their pathogenesis remains a puzzle. This study aimed to construct and validate a nomogram available for preoperative TDs prediction in CRC patients.

Patients and Methods

Patients from the Surveillance, Epidemiology, and End Results (SEER) and the cancer genome atlas (TCGA) databases were randomly divided into training and validation sets according to the sample size ratio of 7:3. Univariate logistic regression was performed for identifying differentially expressed microRNAs between TDs and non-TDs. Nomograms for TDs prediction were developed from the multivariate logistic regression model with least absolute shrinkage and selection operator and were validated internally in terms of accuracy, calibration, and clinical utility. Based on the target genes, pathways tightly associated with TDs were selected using enrichment analysis.

Results

Six clinicopathologic factors and expressions of six microRNAs (miR-614, miR-1197, miR-4770, miR-3136, miR-3173, and miR-4636) differed significantly between TDs and non-TDs CRC patients from the SEER and TCGA training sets. We compared potential prediction discrimination between two nomograms: a clinicopathologic nomogram and a six-microRNA signature nomogram. The six-microRNA signature nomogram revealed better accuracy than the clinicopathologic one for TDs prediction (AUC values of 0.96 and 0.93 in the validation cohort). The calibration plots and decision curve analysis demonstrated that the six-microRNA signature nomogram had better validity and a greater prognostic benefit versus the clinicopathologic one for TDs prediction. Calcium signaling pathways were closely associated with roles of the six microRNAs in TDs of CRC patients.

Conclusion

The six-microRNA signature nomogram can be used as an efficient tool for preoperative TDs prediction in CRC patients.