Dysregulation of microRNAs (miRNAs) plays a key role during the pathogenesis of chemoresistance in lung cancer (LCa). Previous study suggests that miR-324-5p may serve as a unique miRNA signature for LCa, but its role and the corresponding molecular basis remain largely explored. Herein, we report that miR-324-5p expression was significantly increased in cisplatin (CDDP)-resistant LCa tissues and cells, and this upregulation predicted a poor post-chemotherapy prognosis in LCa patients. miR-324-5p was further shown to impact CDDP response: Ectopic miR-324-5p expression in drug-naïve LCa cells was sufficient to attenuate sensitivity to CDDP and to confer more robust tumour growth in CDDP-challenged nude mice. Conversely, ablation of miR-324-5p expression in resistant cells effectively potentiated CDDP-suppressed cell growth in vitro and in vivo. Using multiple approaches, we further identified the tumour suppressor FBXO11 as the direct down-stream target of miR-324-5p. Stable expression of FBXO11 could abrogate the pro-survival effects of miR-324-5p in CDDP-challenged LCa cells. Together, these findings suggest that miR-324-5p upregulation mediates, at least partially, the CDDP resistance by directly targeting FBXO11 signalling in LCa cells. In-depth elucidation of the molecular basis underpinning miR-324-5p action bears potential implications for mechanism-based strategies to improve CDDP responses in LCa.
Background Cuproptosis is a regulated cell death form associated with tumor progression, clinical outcomes, and immune response. However, the role of cuproptosis in pancreatic adenocarcinoma (PAAD) remains unclear. This study aims to investigate the implications of cuproptosis-related genes (CRGs) in PAAD by integrated bioinformatic methods and clinical validation. Methods Gene expression data and clinical information were downloaded from UCSC Xena platform. We analyzed the expression, mutation, methylation, and correlations of CRGs in PAAD. Then, based on the expression profiles of CRGs, patients were divided into 3 groups by consensus clustering algorithm. Dihydrolipoamide acetyltransferase (DLAT) was chosen for further exploration, including prognostic analysis, co-expression analysis, functional enrichment analysis, and immune landscape analysis. The DLAT-based risk model was established by Cox and LASSO regression analysis in the training cohort, and then verified in the validation cohort. Quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) assays were performed to examine the expression levels of DLAT in vitro and in vivo, respectively. Results Most CRGs were highly expressed in PAAD. Among these genes, increased DLAT could serve as an independent risk factor for survival. Co-expression network and functional enrichment analysis indicated that DLAT was engaged in multiple tumor-related pathways. Moreover, DLAT expression was positively correlated with diverse immunological characteristics, such as immune cell infiltration, cancer-immunity cycle, immunotherapy-predicted pathways, and inhibitory immune checkpoints. Submap analysis demonstrated that DLAT-high patients were more responsive to immunotherapeutic agents. Notably, the DLAT-based risk score model possessed high accuracy in predicting prognosis. Finally, the upregulated expression of DLAT was verified by RT-qPCR and IHC assays. Conclusions We developed a DLAT-based model to predict patients’ clinical outcomes and demonstrated that DLAT was a promising prognostic and immunological biomarker in PAAD, thereby providing a new possibility for tumor therapy.
Background Hepatocellular carcinoma (HCC) is one of the highly malignant and aggressive gastrointestinal tumors. Anoikis is a specific form of cell death that is closely related to malignant aggressive behavior of tumors. The role and significance of anoikis-related genes (ANRGs) in HCC deserve to be explored. Methods Here, transcriptome profiling and relevant clinical data needed for analysis were collected from public databases. Prognostic model of ANRGs was constructed by using Lasso regression algorithm. Then, patients were given a reasonable risk grouping, and survival analysis was conducted to compare the different survival rates in each risk group. Receiver operating characteristic (ROC) curve was employed to examine the predictive accuracy of the prognostic model. The single sample gene set enrichment (ssGSEA) was carried out to investigate important disease characteristics of each risk group, such as immune status profile and tumor microenvironment differences. The gene set enrichment analysis (GSEA) method was also implemented to complete functional and pathway enrichment analysis. In addition, drug sensitivity analysis and exploration of single cell data for HCC were completed with the aid of online analytical databases. Results We successfully created a prognostic model containing 14 ANRGs, namely: ANXA5, BSG, SKP2, BAK1, PHLDA2, CDKN3, SFN, EZH2, HMGA1, PBK, NRAS, SLC2A1, MAD2L1 and CASP2, and observed a lower overall survival in high-risk group. The ROC curve confirmed good performance of this new model in predicting prognosis. The ssGSEA revealed significant differences in tumor immune microenvironment between different risk groups, with higher activity about cancer related pathways in high-risk group. The expression level of immune checkpoint and m6A genes also differed between risk subgroups. These prognostic genes were also be related to chemotherapy susceptibility. Conclusion The novel prognostic model identified with ANRGs can be applied to prediction prognostic and assessment immune status profile, tumor microenvironment differences and chemosensitivity in HCC. Rational use of the prognostic new model may provide an important reference for individualized treatment of HCC.
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