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Objective. To establish better diagnosis thinking and provide advanced understanding of MSK, the CT imaging features, clinical characteristics, and the expression of suspected genes in the kidney spatiotemporal immune zonation and fetal renal development were investigated. Methods. 17 patients with MSK hospitalized in our hospital were selected as our research subjects. Human Phenotype Ontology, MalaCards: The Human Disease Database, GeneCards: The Human Gene Database, Human Protein Atlas, and Single Cell Expression Atlas were used to analyze this disease. Results. In our 17 patients, the incidence of MSK tended to be the same in male and female, and the onset age of MSK was probably 31-50 years old. The top one related disease of MSK was nephrocalcinosis and the most frequent phenotype related to MSK was nephrolithiasis. In addition, the expression of HNF1B, CLCN5, GDNF, ATP6V0A4, ATP6V1B1, LAMA2, RET, ACAN, and ABCC8 has been implicated in both human kidney immune zonation and fetal kidney development. Conclusions. HNF1B, CLCN5, GDNF, ATP6V0A4, ATP6V1B1, LAMA2, RET, ACAN, and ABCC8 could be independent indicators for the diagnosis and preventive intervention of MSK patients, and abnormal kidney development due to mutations in key genes was the underlying cause of MSK.

BACKGROUND As the most aggressive breast cancer, inflammatory breast cancer (IBC) has a poor prognosis. However, analyzing the prognostic factors of IBC is challenging due to its rarity. We identified the prognostic factors to establish predictive tools for survival in nonmetastatic IBC patients who received tri-modality therapy. MATERIAL AND METHODS The data of 893 nonmetastatic IBC patients were acquired from the Surveillance, Epidemiology, and End Results (SEER) database. IBC was identified by "ICD-O-3=8530" or "AJCC T, 7th=T4d"). Patients were randomized to the training (n=668) and validation (n=225) cohorts. Prognostic factors were identified in the training cohort. Factors in the nomogram for overall survival (OS) were filtered by the least absolute shrinkage selection operator (LASSO) regression model. Factors selected by the competing-risk models were integrated to construct nomograms for breast cancer-specific survival (BCSS). Nomogram validation was performed in both cohorts. RESULTS The number of positive lymph nodes contributed the most to both nomograms. In the validation cohort, the C-indexes for OS and BCSS were 0.724 and 0.727, respectively. Calibration curves demonstrated acceptable agreement between predicted and actual survival. Risk scores were calculated from the nomograms and used to split patients into the low-risk and high-risk groups. Smooth hazard ratio (HR) curves and Kaplan-Meier curves showed a statistically significant difference in prognosis between the high-risk group and low-risk group (log-rank P