Identification of basement membrane-related prognostic signature for predicting prognosis, immune response and potential drug prediction in papillary renal cell carcinoma

Papillary renal cell carcinoma (PRCC) is a malignant neoplasm of the kidney and is highly interesting due to its increasing incidence. Many studies have shown that the basement membrane (BM) plays an important role in the development of cancer, and structural and functional changes in the BM can be observed in most renal lesions. However, the role of BM in the malignant progression of PRCC and its impact on prognosis has not been fully studied. Therefore, this study aimed to explore the functional and prognostic value of basement membrane-associated genes (BMs) in PRCC patients. We identified differentially expressed BMs between PRCC tumor samples and normal tissue and systematically explored the relevance of BMs to immune infiltration. Moreover, we constructed a risk signature based on these differentially expressed genes (DEGs) using Lasso regression analysis and demonstrated their independence using Cox regression analysis. Finally, we predicted 9 small molecule drugs with the potential to treat PRCC and compared the differences in sensitivity to commonly used chemotherapeutic agents between high and low-risk groups to better target patients for more precise treatment planning. Taken together, our study suggested that BMs might play a crucial role in the development of PRCC, and these results might provide new insights into the treatment of PRCC.

Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study

OBJECTIVE

To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients.

METHOD

We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set.

RESULT

For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P < 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P < 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608-0.760), P < 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P < 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set.

CONCLUSION

Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate.

Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm

Fetal weight is an important index to judge fetal development and ensure the safety of pregnant women. However, fetal weight cannot be directly measured. This study proposed a prediction model of fetal weight based on genetic algorithm to optimize back propagation (GA-BP) neural network. Using random number table method, 80 cases of pregnant women in our hospital from September 2018 to March 2019 were divided into control group and observation group, 40 cases in each group. The doctors in the control group predicted the fetal weight subjectively according to routine ultrasound and physical examination. In the observation group, the continuous weight change model of pregnant women was established by using the regression model and the historical physical examination data obtained by feature normalization pretreatment, and then the genetic algorithm (GA) was used to optimize the initial weights and thresholds of back propagation (BP) neural network to establish the fetal weight prediction model. The coincidence rate of fetal weight was compared between the two groups after birth. Results: The prediction error of GA-BPNN was controlled within 6%. And the accuracy of GA-BPNN was 76.3%, which were 14.5% higher than that of traditional methods. According to the error curve, GA-BP is more effective in predicting the actual fetal weight. Conclusion: The GA-BPNN model can accurately and quickly predict fetal weight.