Prognostic Factors of Colorectal Cancer: A Comparative Study on Patients With or Without Liver Metastasis

Comprehensive Multiomics Analyses Establish the Optimal Prognostic Model for Resectable Gastric Cancer : Prognosis Prediction for Resectable GC

BACKGROUND

Prognostic models based on multiomics data may provide better predictive capability than those established at the single-omics level. Here we aimed to establish a prognostic model for resectable gastric cancer (GC) with multiomics information involving mutational, copy number, transcriptional, methylation, and clinicopathological alterations.

PATIENTS AND METHODS

The mutational, copy number, transcriptional, methylation data of 268, 265, 226, and 252 patients with stages I-III GC were downloaded from the TCGA database, respectively. Alterations from all omics were characterized, and prognostic models were established at the individual omics level and optimized at the multiomics level. All models were validated with a cohort of 99 patients with stages I-III GC.

RESULTS

TTN, TP53, and MUC16 were among the genes with the highest mutational frequency, while UBR5, ZFHX4, PREX2, and ARID1A exhibited the most prominent copy number variations (CNVs). Upregulated COL10A1, CST1, and HOXC10 and downregulated GAST represented the biggest transcriptional alterations. Aberrant methylation of some well-known genes was revealed, including CLDN18, NDRG4, and SDC2. Many alterations were found to predict the patient prognosis by univariate analysis, while four mutant genes, two CNVs, five transcriptionally altered genes, and seven aberrantly methylated genes were identified as independent risk factors in multivariate analysis. Prognostic models at the single-omics level were established with these alterations, and optimized combination of selected alterations with clinicopathological factors was used to establish a final multiomics model. All single-omics models and the final multiomics model were validated by an independent cohort. The optimal area under the curve (AUC) was 0.73, 0.71, 0.71, and 0.85 for mutational, CNV, transcriptional, and methylation models, respectively. The final multiomics model significantly increased the AUC to 0.92 (P < 0.05).

CONCLUSIONS

Multiomics model exhibited significantly better capability in predicting the prognosis of resectable GC than single-omics models.

A recurrence-predictive model based on eight genes and tumor mutational burden/microsatellite instability status in Stage II/III colorectal cancer

BACKGROUND

Although adjuvant chemotherapy (ACT) is widely used to treat patients with Stage II/III colorectal cancer (CRC), administering ACT to specific patients remains a challenge. The decision to ACT requires an accurate assessment of recurrence risk and absolute treatment benefit. However, the traditional TNM staging system does not accurately assess a patient's individual risk of recurrence.

METHODS

To identify recurrence risk-related genetic factors for Stage II/III CRC patients after radical surgery, we conducted an analysis of whole-exome sequencing of 47 patients with Stage II/III CRC who underwent radical surgery at five institutions. Patients were grouped into non-recurrence group (NR, n = 24, recurrence-free survival [RFS] > 5 years) and recurrence group (R, n = 23, RFS <2 years). The TCGA-COAD/READ cohort was employed as the validation dataset.

RESULTS

A recurrence-predictive model (G8plus score) based on eight gene (CUL9, PCDHA12, HECTD3, DCX, SMARCA2, FAM193A, AATK, and SORCS2) mutations and tumor mutation burden/microsatellite instability (TMB/MSI) status was constructed, with 97.87% accuracy in our data and 100% negative predictive value in the TCGA-COAD/READ cohort. For the TCGA-COAD/READ cohort, the G8plus-high group had better RFS (HR = 0.22, p = 0.024); the G8plus-high tumors had significantly more infiltrated immune cell types, higher tertiary lymphoid structure signature scores, and higher immunological signature scores. The G8plus score was also a predict biomarker for immunotherapeutic in advanced CRC in the PUCH cohort.

CONCLUSIONS

In conclusion, the G8plus score is a powerful biomarker for predicting the risk of recurrence in patients with stage II/III CRC. It can be used to stratify patients who benefit from ACT and immunotherapy.

Genetic Association Analysis of Copy Number Variations for Meat Quality in Beef Cattle

Meat quality is an economically important trait for global food production. Copy number variations (CNVs) have been previously implicated in elucidating the genetic basis of complex traits. In this article, we detected a total of 112,198 CNVs and 10,102 CNV regions (CNVRs) based on the Bovine HD SNP array. Next, we performed a CNV-based genome-wide association analysis (GWAS) of six meat quality traits and identified 12 significant CNV segments corresponding to eight candidate genes, including PCDH15, CSMD3, etc. Using region-based association analysis, we further identified six CNV segments relevant to meat quality in beef cattle. Among these, TRIM77 and TRIM64 within CNVR4 on BTA29 were detected as candidate genes for backfat thickness (BFT). Notably, we identified a 34 kb duplication for meat color (MC) which was supported by read-depth signals, and this duplication was embedded within the keratin gene family including KRT4, KRT78, and KRT79. Our findings will help to dissect the genetic architecture of meat quality traits from the aspects of CNVs, and subsequently improve the selection process in breeding programs.