Chemotherapy is an effective method to treat patients with advanced malignant tumors. However, tumor cells can develop resistance to multiple drugs during the therapy process, leading to treatment failure. Circular RNAs (circRNAs) are a new class of regulatory RNAs that can regulate endogenous gene expression. Previous studies revealed the diagnostic and prognostic value of circRNAs in malignant cancer and other diseases, but few reports have examined their association with clinical drug resistance. In this review, we summarize the up-to-date information regarding the role of circRNAs in the resistance of tumors to chemotherapy and discuss specific regulatory mechanisms. This analysis is expected to provide direction for the prevention and management of drug resistance in tumors.

Circular RNA (circRNA), as a covalently closed circular RNA molecule, is widely present, which is recognized as a competing endogenous RNA. A large number of differentially expressed circRNAs have been identified and are recognized as potential biomarkers for the diagnosis of tumors. CircRNAs play an important role in the regulation of cell signaling pathways. The main biological functions of circRNAs include acting as miRNA sponges, regulating the transcription of the parental genes, and acting as adapters to regulate the interactions between proteins and encoding proteins. Compared with normal tissues, there are differentially expressed circRNAs in lung cancer tissue, and the expression levels of circRNAs are correlated with clinicopathological features of lung cancer. Their roles in pathway regulation are described, and the diagnostic and prognostic values are further evaluated. In lung cancer, circRNAs participate in the proliferation, migration, and invasion, acting as a competitive endogenous RNA. Differentially expressed circRNAs may serve as non-invasive diagnostic markers for lung cancers. Further investigation of the roles of circRNAs in the pathogenesis and regulatory pathways is conducive to the development of novel approaches for the diagnosis and accurate treatment of lung cancers.

Introduction: Mannose, a major monosaccharide component of N-glycans, involves in the glycometabolism of human body. Recently, mannose has been shown to suppress tumor growth through enhancing chemosensitivity and reducing the activity of mannose phosphate isomerase (MPI). However, it is largely unknown whether mannose exerts effects on non-small cell lung cancer (NSCLC). Materials and Methods: First, a mannose IC50 assay was conducted to find a suitable concentration of mannose for cell experiments. Then, vitro studies including CCK-8 assay, scratch wound healing assay, and TUNEL assay were performed to evaluate the effects of mannose on A549 cells, and an animal model was established to evaluate the antitumoural effect of mannose on NSCLC in vivo. Finally, immunohistochemistry was done to detect the expression of MPI by Rabbit Anti-MPI. Results: In this study, a concentration of mannose, 15mM, was used to explore the suppressive effect of mannose on A549 cells. CCK-8 assay demonstrated that mannose significantly inhibited the proliferation of A549 cells and enhanced the anti-tumor efficacy of carboplatin. Wound healing assay showed that mannose inhibits the migration of A549 cells, and mannose-induced migration inhibition was more efficient in A549 cells treated with carboplatin. TUNEL assay demonstrated that mannose significantly enhanced the efficacy of carboplatin to promote apoptosis treated by mannose (15mM) or carboplatin. The results of animal experiments revealed that the size and weight of tumors derived from A549 cells treated with mannose were smaller than those derived from control cells, and co-treatment with mannose and carboplatin had most efficient inhibition on tumor growth. MPI expression detection showed that the expression level of MPI in the stage Tis (tumor in situ) was the highest, while the stage IV has the lowest. Discussion: Collectively, our findings suggest that mannose inhibited cell proliferation and migration, promoted cell apoptosis and enhanced the efficacy of carboplatin in lung adenocarcinoma. Preliminary results showed that mannose had less side effect on health. In the future, mannose may be a potential candidate drug for adjuvant therapy of lung adenocarcinoma.

Microinvasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) require distinct treatment strategies and are associated with different prognoses, underscoring the importance of accurate differentiation. This study aims to develop a predictive model that combines radiomics and deep learning to effectively distinguish between MIA and IAC. In this retrospective study, 252 pathologically confirmed cases of ground-glass nodules (GGNs) were included, with 177 allocated to the training set and 75 to the testing set. Radiomics, 2D deep learning, and 3D deep learning models were constructed based on CT images. In addition, two fusion strategies were employed to integrate these modalities: early fusion, which concatenates features from all modalities prior to classification, and late fusion, which ensembles the output probabilities of the individual models. The predictive performance of all five models was evaluated using the area under the receiver operating characteristic curve (AUC), and DeLong's test was performed to compare differences in AUC between models. The radiomics model achieved an AUC of 0.794 (95% CI: 0.684-0.898), while the 2D and 3D deep learning models achieved AUCs of 0.754 (95% CI: 0.594-0.882) and 0.847 (95% CI: 0.724-0.945), respectively, in the testing set. Among the fusion models, the late fusion strategy demonstrated the highest predictive performance, with an AUC of 0.898 (95% CI: 0.784-0.962), outperforming the early fusion model, which achieved an AUC of 0.857 (95% CI: 0.731-0.936). Although the differences were not statistically significant, the late fusion model yielded the highest numerical values for diagnostic accuracy, sensitivity, and specificity across all models. The fusion of radiomics and deep learning features shows potential in improving the differentiation of MIA and IAC in GGNs. The late fusion strategy demonstrated promising results, warranting further validation in larger, multicenter studies.

The reliable predictive markers to identify which patients with advanced non-small cell lung cancer tumors (NSCLC) will achieve durable clinical benefit (DCB) for chemo-immunotherapy are needed. In this retrospective study, we collected radiomics and clinical signatures from 94 patients with advanced NSCLC treated with anti-PD-1/PD-L1 combined with chemotherapy from January 1, 2018 to May 31, 2022. Radiomics variables were extracted from pretreatment CT and selected by Spearman correlation coefficients and clinical features by Logistics regression analysis. We performed effective diagnostic algorithms principal components analysis (PCA) and support vector machine (SVM) to develop an early classification model among DCB and non-durable benefit (NDB) groups. A total of 26 radiomics features and 6 clinical features were selected, and then principal component analysis was used to obtain 6 principal components for SVM building. RC-SVM achieved prediction accuracy with AUC of 0.91 (95% CI 0.87–0.94) in the training set, 0.73 (95% CI 0.61–0.85) in the cross-validation set, 0.84 (95% CI 0.80–0.89) in the external validation set. The new method of RC-SVM model based on radiomics-clinical signatures provides a significant additive value on response prediction in patients with NSCLC preceding chemo-immunotherapy.