Cervical cancer (CC) is a common malignant tumor of the female reproductive system. This study investigated the role of aplysia ras homolog I (ARHI) in resistance to CC in vitro and in patients' tissues. Hela cells were continuously treated with different concentrations of paclitaxel (1-10 nM) to construct paclitaxel-resistant cell model (Hela-TR). CC or CC-TR tissues were obtained from CC patients or CC patients who had developed paclitaxel resistance. The level of ARHI and multidrug resistance gene 1 (MDR1) in cells and tissues were detected by qRT-PCR and immunohistochemistry (IHC) staining. Cell viability, apoptosis and the number of colonies were assessed by MTT, flow cytometry and cell clone assay in Hela and Hela-TR cells after the ARHI plasmid or shARHI were transfected into cells. The autophagy and apoptosis signaling related proteins were analyzed by western blotting. The results revealed that the levels of ARHI mRNA and protein were down-regulated in CC tissues, and were further reduced in paclitaxel-resistant tissues and Hela cell model. High expression of ARHI inhibited the expression of MDR1 in Hela and Hela-TR cells. The cell viability and cell clone of Hela and Hela-TR cells were decreased by ARHI overexpression but increased by ARHI suppression. In addition, highly expressed ARHI promoted apoptosis and activated autophagy by increasing LC3-II/LC3-I through inactivating AKT/mTOR signaling pathway. In conclusion, overexpression of ARHI can increase the sensitivity of CC to paclitaxel through promoting apoptosis and autophagy in a AKT/mTOR inactivation dependent pathway.

Objective A case-control study was conducted to explore the application and clinical value of machine learning-based cervical cancer (CC) diagnosis and prediction model in adjuvant chemotherapy of CC. Methods From August 2019 to August 2021, 46 patients with stage IA CC (study group) and 55 patients with high-grade squamous intraepithelial lesions (HSIL) (control group) were retrospectively analyzed. All patients completed routine MRI examinations, the ADC values of diseased CC and normal cervix and cervical tissues in different stages were compared, and the changes of ADC values in CC tissues before and after chemotherapy were analyzed. The training set (IA = 37, HSIL = 44) and test set (IA = 9, HSIL = 11) are set in a ratio of 4 : 1. The preoperative MRI images were collected and uploaded to the radiomics cloud platform after preprocessing, and the cervix was manually delineated layer by layer on OSag-T2WI, OAx-T1WI, and OAx-T2FS, respectively, to obtain a three-dimensional volume of interest (VOI) of the cervix to extract omics features. Variance Threshold analysis, univariate feature selection (SelectKBest), and least absolute shrinkage and selection operator (LASSO) are adopted to reduce the dimension of data and enroll features. The arbitrary forest model was adopted for machine learning, the ROC curve was drawn, and the diagnostic performance of different sequence omics models was analyzed. Results Compared with ADC of stage A CC and HSIL, the ADC value of CC was remarkably lower than that of normal CC (P < 0.05). The ROC curve analysis of ADC value to differentiate CC and normal cervix indicated that the AUC was 0.838 and the 95% confidence interval was 0.721–0.955. According to the maximum Youden index of 0.848, the optimal critical value of ADC was 1.267 × 10−3 mm2/s and the sensitivity and specificity were 92.21% and 9.48%, respectively. All results are indicated in Table 2. After CC treatment, 12 patients were effective (CR + PR) and 4 patients were ineffective (PD + SD). When the b value was 1000 s/mm2, the ADC value of the effective patients after the second chemotherapy was significantly higher than that of the first chemotherapy and before treatment (P < 0.05). There was no significant difference between the ADC value after the first chemotherapy and before treatment, compared with before treatment (P > 0.05). There was no significant difference in ADC value between the ineffective patients before treatment and after the first and second chemotherapy (P > 0.05). A total of 8 omics features were extracted based on OSag-T2WI, all of which were wavelet features, including 7 texture features and 1 first-order feature. A total of 10 omics features were extracted based on OAx-T1WI, including 6 wavelet first-order features, 2 gradient first-order features, and 2 wavelet texture features. Based on OAx-T2FS, 6 omics features were extracted, including 3 wavelet texture features, 2 original shape features, and 1 logarithmic first-order feature. Based on OSag-T2WI&OAx-T2FS, 9 histological features were extracted, 4 from OSag-T2WI and 5 from OAx-T2FS. The diagnostic performance of the four arbitrary forest models is indicated in Table 1, and the ROC curve is indicated in Figure 6. The diagnostic performance of the omics model based on OSag-T2WI&OAx-T2FS was the best in both the training set and the test set. The AUC of the training set was 0.991 (95% CI (0.94, 1.00)), and the accuracy rate was 0.925. The AUC of the test set was 0.894 (95% CI (0.75, 1.00)), and the accuracy rate was 0.835. On the other hand, the diagnostic efficiency of the group model based on OAx-T1WI was the worst in both the training set and the test set. The AUC of the training set was 0.713 (95% CI (0.52, 0.92)), and the accuracy rate was 0.71. The AUC of test set is 0.513 (95% CI (0.24, 0.77)), and the accuracy rate was 0.56, which has no practical clinical significance. Conclusion A CC diagnosis and prediction model based on machine learning can better distinguish stage IA CC from HSIL in the absence of clear lesions, which is of great significance for reducing invasive examination before surgery, guiding surgical procedures and adjuvant chemotherapy for CC.

NRP1 is a transmembrane glycoprotein that is highly expressed in a variety of tumors. There is evidence that NRP1 can enhance the stem cell properties of tumor cells, which are thought to be resistant to radiotherapy. This study aims to elucidate the potential mechanism of NRP1 in radiation resistance. We transfected NRP1 siRNA and plasmid in breast cancer cells to detect the expression of cancer stem cell markers by western blot and qRT-PCR. The effect of NRP1 on radiotherapy resistance was assesses by immunofluorescence and flow cytometry. In vivo, we established xenograft tumor model treating with shRNA-NRP1 to assess radiotherapy sensitivity. We found that NRP1 could enhance the stem cell properties and confer radioresistance of breast cancer cells. Mechanistically, we proved that NRP1 reduced IR-induced apoptosis by downregulation of Bcl-2 via methyltransferase WTAP in m6A-depentent way. It is suggested that these molecules may be the therapeutic targets for improving the efficacy of radiotherapy for breast cancer.

Abstract Background Sorafenib is a kinase inhibitor that is used as a first-line therapy in advanced hepatocellular carcinoma (HCC) patients. However, the existence of sorafenib resistance has limited its therapeutic effect. Through RNA sequencing, we demonstrated that miR-138-1-3p was downregulated in sorafenib resistant HCC cell lines. This study aimed to investigate the role of miR-138-1-3p in sorafenib resistance of HCC. Methods In this study, quantitative real-time PCR (qPCR) and Western Blot were utilized to detect the levels of PAK5 in sorafenib-resistant HCC cells and parental cells. The biological functions of miR-138-1-3p and PAK5 in sorafenib-resistant cells and their parental cells were explored by cell viability assays and flow cytometric analyses. The mechanisms for the involvement of PAK5 were examined via co-immunoprecipitation (co-IP), immunofluorescence, dual luciferase reporter assay and chromatin immunoprecipitation (ChIP). The effects of miR-138-1-3p and PAK5 on HCC sorafenib resistant characteristics were investigated by a xenotransplantation model. Results We detected significant down-regulation of miR-138-1-3p and up-regulation of PAK5 in sorafenib-resistance HCC cell lines. Mechanistic studies revealed that miR-138-1-3p reduced the protein expression of PAK5 by directly targeting the 3′-UTR of PAK5 mRNA. In addition, we verified that PAK5 enhanced the phosphorylation and nuclear translocation of β-catenin that increased the transcriptional activity of a multidrug resistance protein ABCB1. Conclusions PAK5 contributed to the sorafenib resistant characteristics of HCC via β-catenin/ABCB1 signaling pathway. Our findings identified the correlation between miR-138-1-3p and PAK5 and the molecular mechanisms of PAK5-mediated sorafenib resistance in HCC, which provided a potential therapeutic target in advanced HCC patients.