Detecting heterogeneity in the causal direction of dependence: A model-based recursive partitioning approach.

Methods of causal discovery and direction of dependence to evaluate causal properties of variable relations have experienced rapid development. The majority of causal discovery methods, however, relies on the assumption of causal effect homogeneity, that is, the identified causal structure is expected to hold for the entire population. Because causal mechanisms can vary across subpopulations, we propose combining methods of model-based recursive partitioning and non-Gaussian causal discovery to identify such subpopulations. The resulting algorithm can discover subpopulations with potentially varying magnitude and causal direction of effects under mild parameter inequality assumptions. Feasibility conditions are described and results from synthetic data experiments are presented suggesting that large effects and large sample sizes are beneficial for detecting causally competing subgroups with acceptable statistical performance. In a real-world data example, the extraction of meaningful subgroups that differ in the causal mechanism underlying the development of numerical cognition is illustrated. Potential extensions and recommendations for best practice applications are discussed.

Covariate selection in causal learning under non-Gaussianity.

Understanding causal mechanisms is a central goal in the behavioral, developmental, and social sciences. When estimating and probing causal effects using observational data, covariate adjustment is a crucial element to remove dependencies between focal predictors and the error term. Covariate selection, however, constitutes a challenging task because availability alone is not an adequate criterion to decide whether a covariate should be included in the statistical model. The present study introduces a non-Gaussian method for covariate selection and provides a forward selection algorithm for linear models (i.e., non-Gaussian forward selection; nGFS) to select appropriate covariates from a set of potential control variables to avoid inconsistent and biased estimators of the causal effect of interest. Further, we demonstrate that the forward selection algorithm has properties compatible with principles of direction of dependence, i.e., probing whether the causal target model is correctly specified with respect to the causal direction of effects. Results of a Monte Carlo simulation study suggest that the selection algorithm performs well, in particular when sample sizes are large (i.e., n ≥ 250) and data strongly deviate from Gaussianity (e.g., distributions with skewness beyond 1.5). An empirical example is given for illustrative purposes.

A human pan-cancer system analysis of regulator of chromatin condensation 2.

Regulation of chromosome condensation 2 (RCC2) is associated with the cell cycle and is a crucial regulator of the chromatin condensation 1 (RCC1) family. The members of this family were normally regulators in the process of DNA replication and nucleocytoplasmic transport. RCC2 overexpression may lead to tumor formation and poor prognosis in some tumors including breast cancer and lung adenocarcinoma. However, the possible role of RCC2 in tumor formation and its prognostic function remains unclear. In this study, expression analysis from databases including The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were combined to perform the first integrative and comprehensive analysis of RCC2 in human pan-cancer. RCC2 was highly expressed in most tumors which may lead to a poor prognosis. RCC2 expression was associated with immune/stromal infiltration, immune checkpoints, tumor mutational burden, and microsatellite instability. Thus, RCC2 could be a novel biomarker for prognosis and a promising cancer therapy target.

PLODs: Novel prognostic biomarkers and potential immunotherapy targets for head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) comprise a group of malignant tumors arising from the squamous epithelium of the oral cavity, pharynx, and larynx. HNSCC is the 6th most common cancer in the world, with approximately 650,000 new cases and 400,000 deaths annually. Although survival rates have improved, HNSCC therapy may result in short - or long-term morbidity in approximately 50% of cases. Previous studies have also indicated that the overexpression of procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenases (PLOD) family proteins could lead to certain diseases or even tumors. However, there has been no dedicated evaluation of the relationship between PLOD family members and HNSCC. Here we used data from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Human Protein Atlas (HPA) databases to explore the potential role of PLOD family proteins in HNSCC. Our evaluations suggest that increased expression of PLOD family proteins may be associated with poorer prognosis and increased immune infiltration in HNSCC, making these proteins a potential biomarker for personalized treatment of HNSCC.

Accounting for Heteroskedasticity Resulting from Between-Group Differences in Multilevel Models.

Homogeneity of variance (HOV) is a well-known but often untested assumption in the context of multilevel models (MLMs). However, depending on how large the violation is, how different group sizes are, and the variance pairing, standard errors can be over or underestimated even when using MLMs, resulting in questionable inferential tests. We evaluate several tests (e.g., the H statistic, Breusch Pagan, Levene's test) that can be used with MLMs to assess violations of HOV. Although the traditional robust standard errors used with MLMs require at least 50 clusters to be effective, we assess a robust standard error adjustment (i.e., the CR2 estimator) that can be used even with a few clusters. Findings are assessed using a Monte Carlo simulation and are further illustrated using an applied example. We show that explicitly modeling the heterogenous variance structures or using the CR2 estimator are both effective at ameliorating the issues associated with the fixed effects of the regression model related to violations of HOV resulting from between-group differences.

Multi-omics analysis of kinesin family member 2C in human tumors: novel prognostic biomarker and tumor microenvironment regulator.

Kinesin family member 2C (KIF2C) is the best-characterized member of the kinesin-13 family and is involved in accurately fine-tuned dynamics of mitotic spindles. As KIF2C is involved in both spindle formation and regulation of DNA double-strand breaks, precise regulation of KIF2C is essential to prevent malignant transformation associated with gains and losses of DNA content. In the present study, we initially reviewed The Cancer Genome Atlas database and observed that KIF2C is abundantly expressed in most tumor types. We then analyzed the gene alteration profile, protein expression, prognosis, and immune reactivities of KIF2C in more than 10,000 samples from several well-established databases. In addition, we conducted a gene enrichment set analysis to investigate the potential mechanisms underlying the role of KIF2C in tumorigenesis. Multi-omics analysis of KIF2C demonstrated significant statistical correlations between KIF2C expression and clinical prognosis, oncogenic signature gene sets, myeloid-derived suppressor cell infiltration, ImmunoScore, immune checkpoints, microsatellite instability, and tumor mutational burden across multiple tumors. Single-cell data showed that KIF2C is abundantly expressed in malignant cells. The experimental validation demonstrated that KIF2C is highly expressed in gastric cancer cell lines, gastric adenocarcinoma, and hepatocelluar carcinoma. The findings of this study provide important insight for understanding the role and mechanisms of KIF2C in tumorigenesis and immunotherapy in a variety of cancers.

Distributional causal effects: Beyond an "averagarian" view of intervention effects.

The usefulness of mean aggregates in the analysis of intervention effectiveness is a matter of considerable debate in the psychological, educational, and social sciences. In addition to studying "average treatment effects," the evaluation of "distributional treatment effects," (i.e., effects that go beyond means), has been suggested to obtain a broader picture of how an intervention affects the study outcome. We continue this discussion by considering distributional causal effects. We present formal definitions of causal effects that go beyond means and utilize a distributional regression framework known as generalized additive models for location, scale, and shape (GAMLSS). GAMLSS allows one to characterize an intervention effect in its totality through simultaneously modeling means, variances, skewnesses, kurtoses, as well as ceiling and floor effects of outcome distributions. Based on data from a large-scale randomized controlled trial, we use GAMLSS to evaluate the impact of a teacher classroom management program on student academic performance. Results suggest the teacher classroom management training increased mean academic competence as well as the chance to obtain the maximum score on the academic competence scale. These effects would have been completely overlooked in a traditional evaluation of mean aggregates. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Effective guide for behaviour of farmers in the withdrawal of rural homesteads: An evolutionary game-based study.

In this study, we determined how farmers can be effectively encouraged to withdraw from their idle homesteads, in addition to revitalising the rural construction land stock and realising the market-oriented allocation of land resources. We constructed an evolutionary game model under three scenarios: without penalty mechanism; with a static penalty mechanism; and with a dynamic penalty mechanism. Further, we explicitly describe the strategic behaviours and dynamic evolution processes of local governments and farmers during withdrawal from their rural homesteads. According to the results of the evolutionary stable strategy, under effect of the dynamic penalty mechanism, the strategy systems formed by local governments as well as farmers can gradually converge and stabilise after short-term shocks, compared with that under the no penalty and static penalty mechanisms. Overall, the penalty mechanism mitigates the instability in the game process during participants' incremental changes and strategy choices, while the dynamic mechanism is optimal. Both static and dynamic penalty mechanisms influence the binary equilibrium strategies of local governments as well as farmers, and farmers' strategies evolve towards this state of withdrawal from their homesteads with increasing penalty. When the model is dynamically improved, the probability of farmers' withdrawal of their homesteads increases with increasing penalty. Thus, clearly, the establishment of a penalty mechanism can promote stability of the participants' system; higher penalty implies higher motivation for farmers to withdraw their idle homesteads, enabling revitalisation of the rural stock of construction land and promotion of the optimal allocation of land resource elements.

Diaphanous-related formin subfamily: Novel prognostic biomarkers and tumor microenvironment regulators for pancreatic adenocarcinoma.

The diaphanous-related formin subfamily includes diaphanous homolog 1 (DIAPH1), DIAPH2, and DIAPH3. DIAPHs play a role in the regulation of actin nucleation and polymerization and in microtubule stability. DIAPH3 also regulates the assembly and bipolarity of mitotic spindles. Accumulating evidence has shown that DIAPHs are anomalously regulated during malignancy. In this study, we reviewed The Cancer Genome Atlas database and found that DIAPHs are abundantly expressed in pancreatic adenocarcinoma (PAAD). Furthermore, we analyzed the gene alteration profiles, protein expression, prognosis, and immune reactivity of DIAPHs in PAAD using data from several well-established databases. In addition, we conducted gene set enrichment analysis to investigate the potential mechanisms underlying the roles of DIAPHs in the carcinogenesis of PAAD. Finally, we performed the experimental validation of DIAPHs expression in several pancreatic cancer cell lines and tissues of patients. This study demonstrated significant correlations between DIAPHs expression and clinical prognosis, oncogenic signature gene sets, T helper 2 cell infiltration, plasmacytoid dendritic cell infiltration, myeloid-derived suppressor cell infiltration, ImmunoScore, and immune checkpoints in PAAD. These data may provide important information regarding the role and mechanisms of DIAPHs in tumorigenesis and PAAD immunotherapy.

Inflammatory pseudotumor-like follicular dendritic cell sarcoma: A brief report of two cases.

BACKGROUND: Follicular dendritic cell (FDC) sarcoma/tumor is a rare malignant tumor of follicular dendritic cells, which is considered a low-grade sarcoma that can involve lymph nodes or extranodal sites. Conventional FDC sarcomas are negative for Epstein-Barr virus (EBV), whereas the inflammatory pseudotumor-like variant consistently shows EBV in the neoplastic cells. CASE SUMMARY: We report two cases of inflammatory pseudotumor-like FDC sarcoma in the liver that received 3D laparoscopic right hepatectomy and open right hepatectomy separately. CONCLUSION: EBV probe-based in situ hybridization and detection of immunohistochemical markers of FDC play an important role in the diagnosis and differential diagnosis of inflammatory pseudotumor-like FDC sarcoma. Complete surgical excision combined with regional lymphadenectomy may be effective in reducing the postoperative recurrence and metastasis and improving long-term survival rates.