Analysis of Clinical Characteristics, Radiological Predictors, Pathological Features, and Perioperative Outcomes Associated with Perinephric Fat Adhesion Degree

Personalized differential expression analysis in triple-negative breast cancer

Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.

Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning

OBJECTIVES

Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF.

MATERIALS AND METHODS

Patients with renal cell carcinoma who underwent surgery between April 2019 and February 2022 at Chonnam National University Hwasun Hospital were retrospectively screened, and 119 patients included. Twenty-one and seventeen patients were set aside for the internal and external test sets, respectively. Pre-operative T1-weighted MRI acquired at 60 s following a contrast injection (T1w-60) were collected. For each T1w-60 data, two regions of interest (ROIs) were manually drawn: the perinephric fat tissue and an aorta segment on the same level as the targeted kidney. Preprocessing steps included resizing voxels, N4 Bias Correction filtering, and aorta-based normalization. For each patient, 851 radiomics features were extracted from the ROI of perinephric fat tissue. Gender and BMI were added as clinical factors. Least Absolute Shrinkage and Selection Operator was adopted for feature selection. We trained and evaluated five models using a 4-fold cross validation. The final model was chosen based on the highest mean AUC across four folds. The performance of the final model was evaluated on the internal and external test sets.

RESULTS

A total of 15 features were selected in the final set. The final model achieved the accuracy, sensitivity, specificity, and AUC of 81% (95% confidence interval, 61.9-95.2%), 72.7% (42.9-100%), 90% (66.7-100%), and 0.855 (0.615-1.0), respectively on the internal test set, and 88.2% (70.6-100%), 100% (100-100%), 80% (50%-100%), 0.971 (0.871-1.0), respectively on the external test set.

CONCLUSIONS

Our study demonstrated the feasibility of machine learning algorithms trained with MRI-based radiomics features for APF prediction. Further studies with a multi-center approach are necessary to validate our findings.

Predictive factors and oncological outcomes of pathological T3a upstaging in patients with clinical T1 renal cell carcinoma undergoing partial nephrectomy

OBJECTIVES

To investigate predictive factors and oncological outcomes of pathological T3a upstaging in renal cell carcinoma patients who were initially diagnosed as clinical T1 and treated with partial nephrectomy.

METHODS AND MATERIALS

The clinical records and survival data of 1617 patients, who had undergone partial nephrectomy for clinical T1 renal cell carcinoma at Tokyo Women's Medical University, Tokyo, Japan between January 2011 and December 2020, were analyzed retrospectively.

RESULTS

Of 1617 clinical T1 renal cell carcinoma patients who underwent partial nephrectomy, 28 (1.73%) had pathological T3a upstaging. In the multivariable analysis for pathological T3a upstaging using logistic regression models, male sex and clinical T1b were significant factors associated with pathological T3a upstaging (male sex: odds ratio = 5.07, 95% confidence interval: 1.18-21.8, clinical T1b: odds ratio = 8.36, 95% confidence interval: 3.56-19.6). The Kaplan-Meier method of the recurrence-free survival showed shorter recurrence-free survival in patients with pathological T3a upstaging than in those with pathological T1 (P < 0.0001). In the multivariable analysis using Cox proportional hazards regression models, pathological T3a upstaging was no longer significantly associated with recurrence-free survival after adjustment for other pathological factors (hazard ratio = 1.59, 95% confidence interval: 0.58-4.36). In a sensitivity analysis that analyzed its components individually instead of whole pathological T3a, neither perinephric fat invasion, sinus fat invasion, nor renal vein invasion was associated with recurrence-free survival.

CONCLUSIONS

Male sex and clinical T1b were significant predictors for pathological T3a upstaging after partial nephrectomy in clinical T1 renal cell carcinoma patients. Although patients with pathological T3a upstaging had worse recurrence-free survival compared with those without upstaging, multivariable analyses revealed that pathological T3a upstaging was not an independent predictor for poor recurrence-free survival.

A therapeutical insight into the correlation between circRNAs and signaling pathways involved in cancer pathogenesis

Pre-messenger RNA molecules are back-spliced to create circular RNAs, which are non-coding RNA molecules. After a thorough investigation, it was discovered that these circRNAs have critical biological roles. CircRNAs have a variety of biological functions, including their ability to operate as microRNA sponges, interact with proteins to alter their stabilities and activities, and provide templates for the translation of proteins. Evidence supports a link between the emergence of numerous diseases, including various cancer types, and dysregulated circRNA expression. It is commonly known that a significant contributing element to cancer development is the disruption of numerous molecular pathways essential for preserving cellular and tissue homeostasis. The dysregulation of multiple biological processes is one of the hallmarks of cancer, and the molecular pathways linked to these processes are thought to be promising targets for therapeutic intervention. The biological and carcinogenic effects of circRNAs in the context of cancer are thoroughly reviewed in this article. Specifically, we highlight circRNAs' involvement in signal transduction pathways and their possible use as novel biomarkers for the early identification and prognosis of human cancer.