FGFR inhibitors combined with nab-paclitaxel - A promising strategy to treat non-small cell lung cancer and overcome resistance.

Lung cancer has high morbidity and mortality rates worldwide, and NSCLC accounts for 85% of all lung cancer cases. Despite the development of targeted therapies and immunotherapy, many NSCLC patients do not effectively respond to treatment, and new treatment strategies are urgently needed. Aberrant activation of the FGFR signaling pathway is closely related to the initiation and progression of tumors. AZD4547, which is a selective inhibitor of FGFR 1-3, can suppress the growth of tumor cells with deregulated FGFR expression in vivo and in vitro. However, further exploration is needed to determine whether AZD4547 can play an antiproliferative role in tumor cells without deregulated FGFR expression. We investigated the antiproliferative effect of AZD4547 on NSCLC cells without deregulated FGFR expression. In vivo and in vitro experiments showed that AZD4547 exerted a weak antiproliferative effect on NSCLC cells without deregulated FGFR expression, but it significantly enhanced the sensitivity of NSCLC cells to nab-paclitaxel. We found that AZD4547 combined with nab-paclitaxel suppressed the phosphorylation of the MAPK signaling pathway, led to cell cycle arrest in the G2/M phase, promoted apoptosis, and inhibited cell proliferation more substantially than nab-paclitaxel alone. These findings provide insight into the rational use of FGFR inhibitors and personalized treatment of NSCLC patients.

A novel cuproptosis-related prognostic lncRNA signature and lncRNA MIR31HG/miR-193a-3p/TNFRSF21 regulatory axis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) remains the most common subtype of lung malignancy. Cuproptosis is a newly identified cell death which could regulate tumor cell proliferation and progression. Long non-coding RNAs (lncRNAs) are key molecules and potential biomarkers for diagnosing and treating various diseases. However, the effects of cuproptosis-related lncRNAs on LUAD are still unclear. In our study, 7 cuproptosis-related lncRNAs were selected to establish a prognostic model using univariate Cox regression analysis, LASSO algorithm, and multivariate analysis. Furthermore, we evaluated AC008764.2, AL022323.1, ELN-AS1, and LINC00578, which were identified as protective lncRNAs, while AL031667.3, AL606489.1, and MIR31HG were identified as risk lncRNAs. The risk score calculated by the prognostic model proved to be an effective independent factor compared with other clinical features by Cox regression analyses [univariate analysis: hazard ratio (HR) = 1.065, 95% confidence interval (CI) = 1.043-1.087, P < 0.001; multivariate analysis: HR = 1.067, 95% CI = 1.044-1.091, P < 0.001]. In addition, both analyses (ROC and nomogram) were used to corroborate the accuracy and reliability of this signature. The correlation between cuproptosis-related lncRNAs and immune microenvironment was elucidated, where 7 immune cells and 8 immune-correlated pathways were found to be differentially expressed between two risk groups. Furthermore, our results also identified and verified the ceRNA of cuproptosis-related lncRNA MIR31HG/miR-193a-3p/TNFRSF21 regulatory axis using bioinformatics tools. MIR31HG was highly expressed in LUAD specimens and some LUAD cell lines. Inhibition of MIR31HG clearly reduced the proliferation, migration, and invasion of the LUAD cells. MIR31HG showed oncogenic features via sponging miR-193a-3p and tended to positively regulate TNFRSF21 expression. In a word, lncRNA MIR31HG acts as an oncogene in LUAD by targeting miR-193a-3p to modulate TNFRSF21, which may be beneficial to the gene therapy of LUAD.

A novel pyroptosis-related prognostic lncRNAs signature, tumor immune microenvironment and the associated regulation axes in bladder cancer.

Bladder cancer (BC) is the most common malignancy of the urinary system. Pyroptosis is a host programmed cell death. However, the effects of pyroptosis-related lncRNAs (PRLs) on BC have not yet been completely elucidated. In this study, a prognostic PRLs model and two ceRNA networks were established using sufficient bioinformatics analysis and preliminary RT-qPCR validation in vitro. 6 PRLs were identified to construct a prognostic model. Then, the prognostic model risk score was verified to be an effective independent factor (Training cohort: Univariate analysis: HR = 1.786, 95% Cl = 1.416-2.252, p < 0.001; multivariate analysis: HR = 1.664, 95% Cl = 1.308-2.116, p < 0.001; testing cohort: Univariate analysis: HR = 1.268, 95% Cl = 1.144-1.405, p < 0.001; multivariate analysis: HR = 1.141, 95% Cl = 1.018-1.280, p = 0.024). Moreover, ROC and nomogram were performed to assess the accuracy of this signature (1-year-AUC = 0.764, 3-years-AUC = 0.769, 5-years-AUC = 0.738). Consequently, we evaluated the survival curves of these 6 lncRNAs using Kaplan-Meier survival analysis, demonstrating that MAFG-DT was risk lncRNA, while OCIAD1-AS1, SLC25A25-AS1, SNHG18, PSMB8-AS1 and TRM31-AS1 were protective lncRNAs. We found a strong correlation between PRLs and tumor immune microenvironment by Pearson's correlation analysis. As for sensitivity of anti-tumor drugs, the high-risk group was more sensitive to Sorafenib, Bicalutamide and Cisplatin, while the low-risk group was more sensitive to AKT.inhibitor.VIII, Salubrinal and Lenalidomide, etc. Meanwhile, we identified lncRNA OCIAD1-AS1/miR-141-3p/GPM6B and lncRNA OCIAD1-AS1/miR-200a-3p/AKAP11 regulatory axes, which may play a potential role in the progression of BC.

Development and Validation of a 7-Gene Inflammatory Signature Forecasts Prognosis and Diverse Immune Landscape in Lung Adenocarcinoma.

Background: Inflammatory responses are strongly linked with tumorigenesis and cancer development. This research aimed to construct and validate a novel inflammation response-related risk predictive signature for forecasting the prognosis of patients with LUAD. Methods: Differential expression analysis, univariate Cox, LASSO, and multivariate Cox regression analyses of 200 inflammatory response-related genes (IRRG) were performed to establish a risk predictive model in the TCGA training cohort. The performance of the IRRG model was verified in eight GEO datasets. GSEA analysis, ESTIMATE algorithms, and ssGSEA analysis were applied to elucidate the possible mechanisms. Furthermore, the relationship analysis between risk score, model genes, and chemosensitivity was performed. Last, we verified the protein expression of seven model genes by immunohistochemical staining or Western blotting. Results: We constructed a novel inflammatory response-related 7-gene signature (MMP14, BTG2, LAMP3, CCL20, TLR2, IL7R, and PCDH7). Patients in the high-risk group presented markedly decreased survival time in the TCGA cohort and eight GEO cohorts than the low-risk group. Interestingly, multiple pathways related to immune response were suppressed in high-risk groups. The low infiltration levels of B cell, dendritic cell, natural killer cell, and eosinophil can significantly affect the unsatisfactory prognosis of the high-risk group in LUAD. Moreover, the tumor cells' sensitivity to anticancer drugs was markedly related to risk scores and model genes. The protein expression of seven model genes was consistent with the mRNA expression. Conclusion: Our IRRG prognostic model can effectively forecast LUAD prognosis and is tightly related to immune infiltration.

DPYSL2 as potential diagnostic and prognostic biomarker linked to immune infiltration in lung adenocarcinoma.

BACKGROUND: Dihydropyrimidinase like 2 (DPYSL2) has been linked to tumor metastasis. However, the function of DPSY2L in lung adenocarcinoma (LUAD) is yet to be explored. METHODS: Herein, we assessed DPYSL2 expression in various tumor types via online databases such as Oncomine and Tumor Immune Estimation Resource (TIMER). Further, we verified the low protein and mRNA expressions of DPYSL2 in LUAD via the ULCAN, The TCGA and GEPIA databases. We applied the ROC curve to examine the role of DPYSL2 in diagnosis. The prognostic significance of DPYSL2 was established through the Kaplan-Meier plotter and the Cox analyses (univariate and multivariate). TIMER was used to explore DPYSL2 expression and its connection to immune infiltrated cells. Through Gene Set Enrichment Analysis, the possible mechanism of DPYSL2 in LUAD was investigated. RESULTS: In this study, database analysis revealed lower DPYSL2 expression in LUAD than in normal tissues. The ROC curve suggested that expression of DPYSL2 had high diagnostic efficiency in LUAD. The DPYSL2 expression had an association with the survival time of LUAD patients in the Kaplan-Meier plotter and the Cox analyses. The results from TIMER depicted a markedly positive correlation of DPYSL2 expression with immune cells infiltrated in LUAD, such as macrophages, dendritic cells, CD4+ T cells, and neutrophils. Additionally, many gene markers for the immune system had similar positive correlations in the TIMER analysis. In Gene Set Enrichment Analysis, six immune-related signaling pathways were associated with DPYSL2. CONCLUSIONS: In summary, DPYSL2 is a novel biomarker with diagnostic and prognostic potential for LUAD as well as an immunotherapy target. HIGHLIGHTS: 1. Expression of DPYSL2 was considerably lower in LUAD than in normal tissues. 2. Investigation of multiple databases showed a high diagnostic value of DPYSL2 in LUAD. 3. DPYSL2 can independently predict the LUAD outcomes. 4. Immune-related mechanisms may be potential ways for DPYSL2 to play a role in LUAD.

lncRNA/miR-29c-Mediated High Expression of LOX Can Influence the Immune Status and Chemosensitivity and Can Forecast the Poor Prognosis of Gastric Cancer.

Gastric carcinoma is the fourth most prevalent cause of cancer-related deaths worldwide because of dismal prognosis and few therapeutic options. Accumulated studies have indicated that targeting lysyl oxidase (LOX) family members may serve as an anticancer strategy. Nevertheless, the specific mechanisms of LOX in stomach carcinoma are still unclear. In this study, we demonstrated that LOX is significantly different in 13 types of cancers and may act as a potential therapeutic target, especially in stomach carcinoma. Moreover, overexpression of LOX in gastric carcinoma was validated by multiple databases and contributed to the poor overall survival (OS), progression-free survival (PFS) and post-progression survival (PPS) of stomach adenocarcinoma (STAD) patients. Next, based on the ceRNA hypothesis, the HIF1A-AS2/RP11-366L20.2-miR-29c axis was characterized as the upstream regulatory mechanism of LOX gene overexpression in gastric cancer by combining correlation analysis, expression analysis, and survival analysis. Finally, we illustrated that LOX gene overexpression leads to dismal prognosis of gastric cancer, perhaps through promoting M2 macrophage polarization and tumor immune escape and enhancing drug resistance of tumor cells to chemotherapeutic drugs. Our research demonstrate that LOX may be potentially applied as a novel prognostic marker and targeting inhibition of LOX holds promise as a treatment strategy for gastric cancer.