The causal relationship between white blood cell counts and hepatocellular carcinoma: a Mendelian randomization study

Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction

Gastric cancer (GC), characterized by its inconspicuous initial symptoms and rapid invasiveness, presents a formidable challenge. Overlooking postoperative intervention opportunities may result in the dissemination of tumors to adjacent areas and distant organs, thereby substantially diminishing prospects for patient survival. Consequently, the prompt recognition and management of GC postoperative recurrence emerge as a matter of paramount urgency to mitigate the deleterious implications of the ailment. This study proposes an enhanced feature selection model, bRSPSO-FKNN, integrating boosted particle swarm optimization (RSPSO) with fuzzy k-nearest neighbor (FKNN), for predicting GC. It incorporates the Runge-Kutta search, for improved model accuracy, and Gaussian sampling, enhancing the search performance and helping to avoid locally optimal solutions. It outperforms the sophisticated variants of particle swarm optimization when evaluated in the CEC 2014 test suite. Furthermore, the bRSPSO-FKNN feature selection model was introduced for GC recurrence prediction analysis, achieving up to 82.082 % and 86.185 % accuracy and specificity, respectively. In summation, this model attains a notable level of precision, poised to ameliorate the early warning system for GC recurrence and, in turn, advance therapeutic options for afflicted patients.

The dynamic changes of peripheral blood cell counts predict the clinical outcomes of aneurysmal subarachnoid hemorrhage

Background

Aneurysmal subarachnoid hemorrhage (aSAH) is a serious type of hemorrhagic stroke. It is very important to predict the prognosis at early phase. In this work, we intend to characterize early changes in peripheral blood cells after aSAH and explore the association between peripheral blood cells and clinical outcomes after aSAH.

Methods

aSAH patients admitted between December 2019 and September 2022 were enrolled. A retrospective observational study was performed. Total leukocytes, monocytes, neutrophils, erythrocytes, lymphocytes and platelets counts were recorded on the day of admission (day 1), day 3, day 5 and day 7. Statistical tests included Chi-square test, analysis of variance and multivariate logistic regression (MLR) models. 197 patients were analyzed.

Results

Leukocytes and neutrophils were higher in poor outcome groups from day 1 to day 7 and in delayed cerebral ischemia (DCI) groups from day 3 to day 7. Lymphocytes were higher at day 5 and day 7 in good outcome groups and no DCI groups. Neutrophil-to-lymphocyte ratio (NLR) was lower from day 3 to day 7 in good outcome groups and no DCI groups. Erythrocytes were higher from day 3 to day 7 in good outcome groups and no DCI groups. Lymphocytes were negatively related to poor outcomes on day 1 (OR = 0.457), indicating higher lymphocytes predicted good outcomes, Neutrophils were positively related to poor outcomes on day 3 (OR = 3.003) indicating higher neutrophils predicted poor outcomes. Lymphocytes were negatively related to DCI on day 5 (OR = 0.388) indicating higher lymphocytes predicted no DCI, Erythrocytes were negatively related to DCI on day 5 (OR = 0.335) and day 7 (OR = 0.204) indicating higher erythrocytes predicted no DCI. The improved ability of neutrophils, lymphocytes and erythrocytes to predict DCI or poor functional outcomes were revealed by ROC curve analysis.

Conclusions

The dynamic changes of peripheral blood cell counts were related to poor functional outcomes and DCI after aSAH. Elevated neutrophils, leukocytes, NLR, and decreased lymphocytes, erythrocytes were accompanied by DCI and poor outcome. Neutrophils, lymphocytes and erythrocytes counts could be beneficial to predict DCI and outcomes after aSAH.

Causal relationship between psychological factors and hepatocellular carcinoma as revealed by Mendelian randomization

PURPOSE

The impact of psychological factors on the incidence of hepatocellular carcinoma (HCC) in humans remains unclear. Mendelian randomization (MR) study is a novel approach aimed at unbiased detection of causal effects. Therefore, we conducted a two-sample MR to determine if there is a causal relationship between psychological distress (PD), participation in leisure/social activities of religious groups (LARG), and HCC.

METHODS

The genetic summary data of exposures and outcome were retrieved from genome-wide association studies (GWAS). We used PD and LARG as exposures and HCC as outcome. Five MR methods were used to investigate the causal relationship between PD, LARG, and HCC. The result of inverse variance weighted (IVW) method was deemed as principal result. Besides, we performed a comprehensive sensitivity analysis to verify the robustness of the results.

RESULTS

The IVW results showed that PD [odds ratio (OR) 1.006, 95% confidence interval (CI) 1.000-1.011, P = 0.033] and LARG (OR 0.994, 95% CI 0.988-1.000, P = 0.035) were causally associated with the incidence of HCC. Sensitivity analysis did not identify any bias in the results.

CONCLUSION

PD turned out to be a mild risk factor for HCC. In contrast, LARG is a protective factor for HCC. Therefore, it is highly recommended that people with PD are seeking positive leisure activities such as participation in formal religious social activities, which may help them reduce the risk of HCC.

Liver function indicators and risk of hepatocellular carcinoma: a bidirectional mendelian randomization study

Observational studies have shown an association between liver dysfunction and hepatocellular carcinoma (HCC), but the causality relationship between them is unclear. We aimed to determine whether there is a bidirectional causal relationship between liver function indicators (alanine aminotransferase, ALT; aspartate aminotransferase, AST; alkaline phosphatase, ALP; γ-glutamyltransferase, GGT) and HCC. Our two-sample Mendelian randomization (MR) study acquired single nucleotide polymorphisms (SNPs) associated with liver function indicators (ALT, n = 134,182; AST, n = 134,154; GGT, n = 118,309; ALP, n = 105,030) and with HCC (n = 197,611) from publicly available genome-wide association studies (GWAS) of East Asian ancestry in Japan (BioBank Japan, BBJ). Univariable MR analyses were performed to identify whether the genetic evidence of exposure was significantly associated with outcome. Multivariable MR analysis was conducted to estimate the independent effects of exposures on outcome. Univariable MR analysis indicated that the level of ALT, AST, and GGT was the risk factor for HCC incidence. Meanwhile, multivariable MR analysis revealed that AST was an independent risk factor for HCC. The hazard ratio (HR) of the probability of HCC was 3.045 [95% confidence interval (95%CI), 1.697-5.463, p = 0.003] for AST. The results of reverse MR analyses showed that gene-predictive HCC incidence could increase the levels of AST (HR = 1.031, 95%CI: 1.009-1.054, p = 2.52 × 10-4) and ALT (HR = 1.040, 95%CI: 1.019-1.063, p = 0.005). Meanwhile, HCC may be negatively correlated with ALP levels (HR = 0.971, 95%CI: 0.947-0.995, p = 0.018). This study provides evidence to support that genetically predicted higher levels of AST are related to increased risk of HCC, with no strong evidence of a causal effect of genetically predicted ALP, ALP, and GGT on HCC. In addition, genetic predisposition to HCC could influence blood concentration of ALT, AST, and ALP. Thus, this may create a vicious cycle.

WGCNA combined with machine learning to find potential biomarkers of liver cancer

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

The causal relationship between metabolic factors, drinking, smoking and intrahepatic cholangiocarcinoma: a Mendelian randomization study

Background

Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer. While multiple risk factors for iCCA have been established, metabolic diseases (obesity, diabetes, NAFLD, dyslipidemia, and hypertension) and other risk factors, including smoking and drinking, are still controversial due to their potential confounders. Here, Mendelian randomization (MR) analysis was performed to identify the causal relationship between them.

Method

In this study, we obtained GWAS data related to exposures from corresponding large genome-wide association studies. Summary-level statistical data for iCCA were obtained from the UK Biobank (UKB). We performed a univariable MR analysis to identify whether genetic evidence of exposure was significantly associated with iCCA risk. A multivariable MR analysis was conducted to estimate the independent effects of exposures on iCCA.

Results

Univariable and multivariable MR analysis based on the large GWAS data indicated that there is little evidence to support the genetic role of metabolic factors, smoking, drinking, and NAFLD in iCCA development (P >0.05). In contrast to most current studies, their impact on iCCA development, if any, might be smaller than we thought. The previous positive results might be due to the comorbidities between diseases and potentially unavoidable confounding factors.

Conclusion

In this MR study, we found no strong evidence to support causal associations between metabolic factors, NAFLD, smoking, drinking, and iCCA risk.

The relationship between the serum lipid profile and hepatocellular carcinoma in east Asian population: A mendelian randomization study

Background

Although several studies have found that the serum lipid profile may be correlated with hepatocellular carcinoma (HCC), the causal relationships between the serum lipid profile and HCC have not been determined due to potential confounder. Here, Mendelian randomization (MR) analysis was performed to identify the relationship between the serum lipid profile and HCC in the East Asian population.

Method

Our study made a MR analysis with the validation of two data sets. We obtained genome-wide association study (GWAS) data related to the serum lipid profile from Asian Genetic Epidemiology Network (AGEN). Then, the data from a recent large GWAS of the East Asian ancestry in Japan (BioBank Japan, BBJ) were extracted. Summary-level statistical data for HCC were obtained from a large GWAS of the East Asian ancestry in Japan. Univariable MR analysis were performed to identify whether the genetic evidence of serum lipid profile was significantly associated with HCC risk. Multivariable MR analysis was conducted to estimate the independent effects of exposures on HCC.

Results

Univariable and multivariable MR analyses indicated that the serum lipid profile was not a risk factor for HCC incidence in either data set based on the East Asian population. Multivariable MR analysis revealed that the hazard ratios of the probability of HCC in AGEN were 1.134 (95% confidence interval (CI), 0.903-1.424) for TG, 1.010 (95% CI: 0.824-1.237) for HDL-C, 0.974 (95% CI: 0.746-1.271) for TC, 0.918 (95% CI: 0.734-1.147) for LDL-C, while the results in BBJ were also non-significant: 1.111 (95% CI: 0.869-1.419) for TG, 0.957 (95% CI: 0.790-1.158) for HDL-C, 0.917 (95% CI: 0.643-1.308) for TC, 0.932 (95% CI: 0.699-1.243) for LDL-C.

Conclusion

Our MR study with the validation of two data sets found no strong evidence to support causal associations between the serum lipid profile and HCC risk.