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

Prognostic Factors of Postoperative Mortality in Patients with Complicated Right Colon Cancer

The incidence of right colon cancer presenting in a stage with complications is significant. There are major differences in therapeutic approach between elective colon cancer surgery and emergency surgery. Complications such as hemorrhage, obstruction, and perforation require careful evaluation of prognostic factors, with morbidity and mortality rates being much higher compared to elective colon surgery. We retrospectively analyzed a group of 95 patients admitted in an emergency to the County Emergency Hospital St. Apostol Apostol Andrei Galati with complicated tumors of the right colon-occlusive, perforated, or hemorrhagic. A series of clinical and biological parameters were followed in order to identify the prognostic factors in the occurrence of death. We analyzed the specialized literature, comparing our study with other similar research from the most important databases. The postoperative death rate in patients with complicated right colon cancer was high. Most complications were occlusive, followed by hemorrhagic and perforative.

Association between lymphovascular invasion and lymph node metastases in colon cancer: A National Cancer Database analysis

AIM

Lymphovascular invasion (LVI) is a well-known risk factor in colorectal cancer that is associated with a worse prognosis. The present study aimed to assess the characteristics of patients with LVI-positive colon cancer according to the status of nodal metastases and to study the association between LVI-nodal status and survival.

METHOD

This retrospective study assessed the association between LVI and lymph node metastases in colon cancer, using data from the National Cancer Database. Patients were classified according to the pathological N stage into pN0 and pN1-2. The risk factors for LVI were determined in each group using multivariable regression analyses. The primary outcome was LVI and the secondary outcome was 5-year overall survival (OS). A modification of the tumour, node, metastasis (TNM) staging system that incorporates LVI in each stage was proposed.

RESULTS

The study included 357 724 patients (51.1% female, median age 70 years). LVI was detected in 11.6% and 52.5% of patients with node-negative and node-positive disease, respectively. The independent predictors of LVI in pN0 stage were poorly differentiated carcinomas (OR: 3.6, p < 0.001), undifferentiated carcinomas (OR: 3.3, p < 0.001), mucinous carcinomas (OR: 0.61, p < 0.001), and perineural invasion (OR: 4.2, p < 0.001). The independent predictors of LVI in pN1-2 disease were poorly differentiated carcinomas (OR: 2.36, p < 0.001), undifferentiated carcinomas (OR: 3.23, p < 0.001), and perineural invasion (OR: 3.33, p < 0.001). LVI was significantly associated with worse 5-year OS and the adverse survival impact of LVI was higher in pN1-2 disease (HR: 1.47, p < 0.001) than in pN0 disease (HR: 1.28, p < 0.001). When LVI was present, the 5-year OS was reduced by 1.5% in stage I, 5.6% in stage II, and 11.5% in stage III.

CONCLUSION

LVI was more prevalent in patients with colon cancer with lymph node metastases than in patients with node-negative disease. However, LVI was not detected in approximately half of patients with nodal disease. The adverse survival effect of LVI was proportional to the stage of colon cancer.

Rapid detection of liver metastasis risk in colorectal cancer patients through blood test indicators

Introduction

Colorectal cancer (CRC) is one of the most common malignancies, with liver metastasis being its most common form of metastasis. The diagnosis of colorectal cancer liver metastasis (CRCLM) mainly relies on imaging techniques and puncture biopsy techniques, but there is no simple and quick early diagnosisof CRCLM.

Methods

This study aims to develop a method for rapidly detecting the risk of liver metastasis in CRC patients through blood test indicators based on machine learning (ML) techniques, thereby improving treatment outcomes. To achieve this, blood test indicators from 246 CRC patients and 256 CRCLM patients were collected and analyzed, including routine blood tests, liver function tests, electrolyte tests, renal function tests, glucose determination, cardiac enzyme profiles, blood lipids, and tumor markers. Six commonly used ML models were used for CRC and CRCLM classification and optimized by using a feature selection strategy.

Results

The results showed that AdaBoost algorithm can achieve the highest accuracy of 89.3% among the six models, which improved to 91.1% after feature selection strategy, resulting with 20 key markers.

Conclusions

The results demonstrate that the combination of machine learning techniques with blood markers is feasible and effective for the rapid diagnosis of CRCLM, significantly im-proving diagnostic ac-curacy and patient prognosis.

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.