The sufficient component cause (SCC) model and counterfactual model are two common methods for causal inference, each with their own advantages: the SCC model allows the mechanistic interaction to be detailed, whereas the counterfactual model features a systemic framework for quantifying causal effects. Hence, integrating the SCC and counterfactual models may facilitate the conceptualization of causation. Based on the marginal SCC (mSCC) model, we propose a novel counterfactual mSCC framework that includes the steps of definition, identification, and estimation. We further propose a six‐way effect decomposition for assessing mediation and the mechanistic interaction. The results demonstrate that when all variables are binary, the six‐way decomposition is an extension of four‐way decomposition and that without agonism, the six‐way decomposition is reduced to four‐way decomposition. To illustrate the utility of the proposed decomposition, we apply it to a Taiwanese cohort to examine the mechanism of hepatitis C virus (HCV)‐induced hepatocellular carcinoma (HCC) with liver inflammation measured by alanine aminotransferase (ALT) as a mediator. Among the HCV‐induced HCC cases, 62.27% are not explained by either mediation or interaction in relation to ALT; 9.32% are purely mediated by ALT; 16.53% are caused by the synergistic effect of HCV and ALT; and 9.31% are due to the mediated synergistic effect of HCV and ALT. In summary, we introduce an SCC model framework based on counterfactual theory and detail the required identification assumptions and estimation procedures; we also propose a six‐way effect decomposition to unify mediation and mechanistic interaction analyses.

Recent methodological developments in causal mediation analysis have addressed several issues regarding multiple mediators. However, these developed methods differ in their definitions of causal parameters, assumptions for identification, and interpretations of causal effects, making it unclear which method ought to be selected when investigating a given causal effect. Thus, in this study, we construct an integrated framework, which unifies all existing methodologies, as a standard for mediation analysis with multiple mediators. To clarify the relationship between existing methods, we propose four strategies for effect decomposition: two‐way, partially forward, partially backward, and complete decompositions. This study reveals how the direct and indirect effects of each strategy are explicitly and correctly interpreted as path‐specific effects under different causal mediation structures. In the integrated framework, we further verify the utility of the interventional analogues of direct and indirect effects, especially when natural direct and indirect effects cannot be identified or when crossworld exchangeability is invalid. Consequently, this study yields a robustness‐specificity trade‐off in the choice of strategies. Inverse probability weighting is considered for estimation. The four strategies are further applied to a simulation study for performance evaluation and for analyzing the Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer dataset from Taiwan to investigate the causal effect of hepatitis C virus infection on mortality.

BackgroundUltra-deep targeted sequencing (UDT-Seq) has advanced our knowledge on the incidence and functional significance of somatic mutations. However, the utility of UDT-Seq in detecting copy number alterations (CNAs) remains unclear. With the goal of improving molecular prognostication and identifying new therapeutic targets, we designed this study to assess whether UDT-Seq may be useful for detecting CNA in oral cavity squamous cell carcinoma (OSCC).MethodsWe sequenced a panel of clinically actionable cancer mutations in 310 formalin-fixed paraffin-embedded OSCC specimens. A linear model was developed to overcome uneven coverage across target regions and multiple samples. The 5-year rates of secondary primary tumors, local recurrence, neck recurrence, distant metastases, and survival served as the outcome measures. We confirmed the prognostic significance of the CNA signatures in an independent sample of 105 primary OSCC specimens.ResultsThe CNA burden across 10 targeted genes was found to predict prognosis in two independent cohorts. FGFR1 and PIK3CAamplifications were associated with prognosis independent of clinical risk factors. Genes exhibiting CNA were clustered in the proteoglycan metabolism, the FOXO signaling, and the PI3K-AKT signaling pathways, for which targeted drugs are already available or currently under development.ConclusionsUDT-Seq is clinically useful to identify CNA, which significantly improve the prognostic information provided by traditional clinicopathological risk factors in OSCC patients.

Deciphering the causal networks of gene interactions is critical for identifying disease pathways and disease-causing genes. We introduce a method to reconstruct causal networks based on exploring phenotype-specific modules in the human interactome and including the expression quantitative trait loci (eQTLs) that underlie the joint expression variation of each module. Closely associated eQTLs help anchor the orientation of the network. To overcome the inherent computational complexity of causal network reconstruction, we first deduce the local causality of individual subnetworks using the selected eQTLs and module transcripts. These subnetworks are then integrated to infer a global causal network using a random-field ranking method, which was motivated by animal sociology. We demonstrate how effectively the inferred causality restores the regulatory structure of the networks that mediate lymph node metastasis in oral cancer. Network rewiring clearly characterizes the dynamic regulatory systems of distinct disease states. This study is the first to associate an RXRB-causal network with increased risks of nodal metastasis, tumor relapse, distant metastases and poor survival for oral cancer. Thus, identifying crucial upstream drivers of a signal cascade can facilitate the discovery of potential biomarkers and effective therapeutic targets.

Intraoperative neuromonitoring can qualify and quantify RLN function during thyroid surgery. This study investigated how the severity and mechanism of RLN dysfunction during monitored thyroid surgery affected postoperative voice. This retrospective study analyzed 1021 patients that received standardized monitored thyroidectomy. Patients had post-dissection RLN(R2) signal <50%, 50–90% and >90% decrease from pre-dissection RLN(R1) signal were classified into Group A-no/mild, B-moderate, and C-severe RLN dysfunction, respectively. Demographic characteristics, RLN injury mechanisms(mechanical/thermal) and voice analysis parameters were recorded. More patients in the group with higher severity of RLN dysfunction had malignant pathology results (A/B/C = 35%/48%/55%, p = 0.017), received neck dissection (A/B/C = 17%/31%/55%, p < 0.001), had thermal injury (p = 0.006), and had asymmetric vocal fold motion in long-term postoperative periods (A/B/C = 0%/8%/62%, p < 0.001). In postoperative periods, Group C patients had significantly worse voice outcomes in several voice parameters in comparison to Group A/B. Thermal injury was associated with larger voice impairments compared to mechanical injury. This report is the first to discuss the severity and mechanism of RLN dysfunction and postoperative voice in patients who received monitored thyroidectomy. To optimize voice and swallowing outcomes after thyroidectomy, avoiding thermal injury is mandatory, and mechanical injury must be identified early to avoid a more severe dysfunction.