Nodal metastasis signatures in breast cancer

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma

PURPOSE

To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data.

METHODS

Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model.

RESULTS

Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan-Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P<0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model.

CONCLUSION

Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.

Axillary Lymph Node Involvement in Breast Cancer: A Random Walk Model of Tumor Burden

We reinvestigate the relationship between axillary lymph node involvement in breast cancer and the overall risk of death. Patients were women from the Surveillance, Epidemiology, and End Results (SEER) program, aged between 50 and 65 years, presenting a first primary T1-T2 (tumor size ≤5 cm), node-positive, non-metastasized unilateral breast carcinoma, diagnosed from 1988 to 1997, treated with mastectomy without radiotherapy. Hazard ratios (HRs) were computed at each percentage of involved nodes using the proportional hazards model, adjusting for the patient's demographic and tumor characteristics. The pattern of the hazard ratios was examined using serial correlations. Significance testing used the "portmanteau" test. Based on 4,387 records available for analysis, the relation between adjusted mortality and axillary lymph node involvement was modeled as Ht - Ht- 1 = μ + at, where t is the percentage of involved nodes, Ht is the mortality hazard ratio at the percentage t, μ is a constant, and at is white noise. The constant μ was estimated at 0.020, corresponding to a 2% increment in the mortality hazard ratio per 1% increase in the percentage of positive nodes. The model was considered acceptable by the "portmanteau" test (P=0.205). We conclude that the effect of the tumor burden might be expressed as a random walk difference model, relating the mortality hazard ratio with the percentage of involved nodes. We will use the model to explore how treatments affect the course of the disease.

Lymph Nodes in Breast Cancer - What Can We Learn from Translational Research?

Clinical observations about lack of survival benefit after extensive axillary surgery and biological discordance between primary breast tumors and axillary lymph nodes raise the question of the actual metastatic potential of axillary nodal disease. The exploration of intratumoral heterogeneity and detection of genomic differences between the primary and lymph nodes indicate some similarity between the number of mutations in synchronous axillary node metastases and those in the primary lesion, suggesting a favorable prognosis. The hematogenous route of metastasis needs to be considered in findings of different subclones between nodal and distant metastases. Modern tools such as whole-genome sequencing applied in multiple tumor areas may guide more precisely the extent of axillary surgery.

Functions of actin-interacting protein 1 (AIP1)/WD repeat protein 1 (WDR1) in actin filament dynamics and cytoskeletal regulation

Actin-depolymerizing factor (ADF)/cofilin and actin-interacting protein 1 (AIP1), also known as WD-repeat protein 1 (WDR1), are conserved among eukaryotes and play critical roles in dynamic reorganization of the actin cytoskeleton. AIP1 preferentially promotes disassembly of ADF/cofilin-decorated actin filaments but exhibits minimal effects on bare actin filaments. Therefore, AIP1 has been often considered to be an ancillary co-factor of ADF/cofilin that merely boosts ADF/cofilin activity level. However, genetic and cell biological studies show that AIP1 deficiency often causes lethality or severe abnormalities in multiple tissues and organs including muscle, epithelia, and blood, suggesting that AIP1 is a major regulator of many biological processes that depend on actin dynamics. This review summarizes recent progress in studies on the biochemical mechanism of actin filament severing by AIP1 and in vivo functions of AIP1 in model organisms and human diseases.