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Protective autophagy is a promising target for antitumor drug exploration. A hydroxychloroquine (HCQ) platinum(IV) complex with autophagy suppressing potency was developed, which displayed potent antitumor activities with a TGI rate of 44.2% against 4T1 tumors in vivo and exhibited a rather lower toxicity than cisplatin. Notably, it exhibited satisfactory antimetastatic activities toward lung pulmonary metastasis models with an inhibition rate of 49.6% and was obviously more potent than CDDP, which has an inhibition rate of 21.6%. Mechanism detection revealed that it caused serious DNA damage and upregulated the expression of γ-H2AX and p53. More importantly, the incorporation of an autophagy inhibitor HCQ endowed the platinum(IV) complex with potent autophagy impairing properties by perturbing the lysosomal function in tumor cells, which promoted apoptosis synergistically with DNA injury. Then, the impaired autophagy further led to the suppression of hypoxia and inflammation in the tumor microenvironment by downregulating ERK1/2, HIF-1α, iNOS, caspase1 and COX-2. Adaptive immune response was improved by inhibiting the immune checkpoint PD-L1 and further increasing CD4+ and CD8+ T cells in tumors. Then, tumor metastasis was effectively inhibited by restraining angiogenesis through inhibiting VEGFA, MMP-9, and CD34. [ABSTRACT FROM AUTHOR]

Background: Collagen (COL) genes, play a key role in tumor invasion and metastasis, are involved in tumor extracellular matrix (ECM)-receptor interactions and focal adhesion pathways. However, studies focusing on the diagnostic value of the COL4 family in stomach adenocarcinoma (STAD) are currently lacking. Methods: The TCGA database was employed to retrieve the clinical features and RNA sequencing expression profiles of patients with STAD. We conducted an investigation to examine the expression disparities between STAD and adjacent normal tissues. Kaplan-Meier survival analysis was utilized to assess their prognostic significance, while Spearman correlation analysis was employed to determine their association with immune checkpoint genes and immunomodulatory molecules. Furthermore, GO and KEGG analyses were performed on the COL4s-related genes, revealing potential biological pathways through gene set enrichment analysis (GSEA). Subsequently, we explored the extent of immune infiltration of the COL4 family in STAD using the TIMER database. Lastly, the expression levels of the COL4 family in STAD were further validated through quantitative PCR (qPCR) and western blot techniques. Results: The expression levels of COL4A1/2 were significantly upregulated, while COL4A5/6 were conspicuously downregulated in STAD. The survival analysis revealed that the upregulated COL4s indicated poorer overall survival, first progression and post-progression survival outcomes. Additionally, our findings demonstrated a positive correlation between the expressions of COL4A1/2/3/4 and the infiltration of immune cells, including CD8 + T cells, dendritic cells, macrophages, neutrophils and CD4 + T cells. Further correlation analysis uncovered a favorable association between the expression of COL4A1/2/3/4 and various crucial immunomodulatory molecules, immunological checkpoint molecules, and chemokines. Quantitative PCR analysis confirmed that the expression patterns of COL4A1/3/4/6 genes aligned with the finding from the TCGA database. However, gastric cancer cells exhibited downregulation of COL4A2. Consistently, the protein level of COL4A1 was elevated, whereas the protein level of COL4A2 was reduced in the gastric cancer cell lines. Conclusion: COL4s could potentially serve as biomarkers for diagnosing and predicting the prognosis of STAD. [ABSTRACT FROM AUTHOR]

BACKGROUND: Docetaxel is a yew compound antitumor agent with accurate antitumor efficacy, but its application is limited due to the high and serious adverse effects, and finding effective combination therapy options is a viable strategy. Immune checkpoint inhibitors have become hotspots in enhancing anti-tumor immunity by blocking immune checkpoint signaling pathways, but their response rate to monotherapy use is not high and the efficacy is minimal. OBJECTIVE: To explore the anti-tumor effects and mechanisms of the combination of PD-1 inhibitors and Docetaxel through in vivo experiments and develop a feasible combination treatment for the therapy of prostate cancer. METHODS: Tumor-bearing mice were subcutaneously injected with 0.1 ml RM-1 cells. Treatment were taken when the tumor growed up to 3 mm, after which the tumor and spleen were removed to test the antitumor effect with Flow cytometric (FACS) analysis, Immunohistochemistry, Western Blot. RESULTS: In this experiment, we found that PD-1 inhibitors combined with Docetaxel had a synergistic effect on mouse prostate cancer, inhibited the growth of prostate cancer, improved survival and reduced adverse reactions, increased spleen and tumor infiltrative CD4+ and CD8+ T cells, especially in group combination with low-dose Docetaxel, and were related to the PI3K/AKT/NFKB-P65/PD-L1 signaling pathway. CONCLUSION: Our study confirms that PD-1 inhibitors in combination with Docetaxel are a viable combination strategy and provide a safe and effective combination option for the clinical treatment of prostate cancer. [ABSTRACT FROM AUTHOR]

Objective: Polo‐like kinase 1 (PLK1), a serine/threonine‐protein kinase, functions as a potent oncogene in the initiation and progression of tumor. The aim of this study is to assess potential correlations between PLK1 expression and immune infiltration in breast cancer (BRCA) and construct a PLK1‐based immune risk model applicable for prognosis and treatment response prediction in BRCA. Methods: We collected data on PLK1 gene expression in BRCA patients from The Cancer Genome Atlas (TCGA) database. Thereafter, we analyzed the associations of PLK1 expression with immune cell infiltration and immunomodulators, and established a prognostic risk model based on seven PLK1‐associated immunomodulator genes and a nomogram for survival prediction. Results: BRCA prognosis, clinical stage progression, and tumor classification were all shown to be substantially correlated with PLK1 expression. The PLK1 gene was significantly enriched in T cell and B cell receptors and molecules of the chemokine signaling pathways. Specifically, PLK1 expression was positively correlated with the CD8+ T cell and regulatory T cell (Tregs) activation and negatively correlated with M2 macrophage infiltration. The seven‐genes‐based risk model could serve as an independent prognostic factor of BRCA. The risk model was markedly correlated with the expression of programmed cell death protein 1/programmed cell death ligand 1 (PD‐1/PD‐L1; both p < 0.001) immune checkpoints, and tumor mutation burden (TMB). High‐ and low‐risk BRCA patients identified by the risk model responded differently to anti‐PD‐1 and/or anti‐CTLA4 therapy, as well as common chemotherapy drugs, like cisplatin, paclitaxel, and gemcitabine. Conclusion: This PLK1‐based immune risk model can effectively predict the prognosis and tumor progression of BRCA, identify gene mutations, and evaluate patient's response toward immunotherapy and chemotherapy regimens. [ABSTRACT FROM AUTHOR]

BACKGROUND: Hepatocellular carcinoma (HCC) has a high degree of malignancy and poor prognosis. N6-methyladenosine (m6A) modifications and microRNAs (miRNAs) play pivotal roles in tumorigenesis and development. However, the role of m6A-related miRNAs in HCC has not been clarified yet. This study aimed to identify the role of m6A-miRNAs in HCC prognosis through bioinformatics analysis. METHODS: The clinicopathological information and RNA sequencing data of 369 HCC tumor tissues and 49 tumor-adjacent tissues were downloaded from the TCGA database. A total of 23 m6A regulators were extracted to evaluated the m6A-related miRNAs using Pearson's correlation analysis. Then, we selected prognosis-related m6A-miRNAs using a univariate Cox regression model and used the consensus cluster analysis to explore the characteristics of the m6A-miRNAs. The coefficient of the least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct a prognostic risk score model. The receiver operated characteristic (ROC) analysis was applied to evaluate the prognostic value of the signature. The biological functions of targeted genes were predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Then, to validate the potential predictive value for prognosis, the miRNA expression profiles from the GSE76903 and GSE6857 were used. Single sample Gene Set Enrichment Analysis (ssGSEA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) were applied to assess the immune microenvironment of HCC. Additionally, a meta-analysis was used to verify the prognostic value of the m6A-microRNAs. RT-PCR was applied to validated the expression of miRNAs in HCC tissues. Cell viability, transwell assay and RNA m6A dot blot assays of HCC cells was applied to access the function of miR-17-5p. RESULTS: The expression of 48 m6A-related miRNAs was identified and 17 prognostic m6A-miRNAs was discovered. The expression profile of those 17 miRNAs was divided into three clusters, and these clusters were associated with the tumor microenvironment (TME) and prognosis. The nine m6A-related miRNA signature was associated with the prognosis of HCC, the AUC of the ROC was 0.771(TCGA dataset), 0.788(GSE76903) and 0.646(GSE6857). The TME and the expression of immune checkpoint molecules were associated with the risk score. The meta-analysis also validated the prognostic value of the m6A-related miRNAs (miR182-5p (HR:1.58, 95%CI:1.04-2.40) and miR-17-5p (HR:1.58, 95%CI: 1.04–2.40)). The expression of miR-17-5p was upregulated in HCC tissues and miR-17-5p showed an oncogenic role in HCC cells. CONCLUSION: The clinical innovation is the use of m6A-miRNAs as biomarkers for predicting prognosis regarding immunotherapy response in HCC patients. [ABSTRACT FROM AUTHOR]

To establish a prognostic model based on immune-related microRNA (miRNA) for pancreatic carcinoma. Weighted correlation network analysis (WGCNA) was performed using the "WGCNA" package to find the key module genes involved in pancreatic carcinoma. Spearman correlation analysis was conducted to screen immune-related miRNAs. Uni- and multi-variate COX regression analyses were carried out to identify miRNAs prognostic for overall survival (OS) of pancreatic carcinoma, which were then combined to generate a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic (ROC) analysis, distribution plot of survival status in patients and regression analysis were collectively performed to study the accuracy of the model in prognosis. Target genes of the miRNAs in the model were intersected with the key module genes, and a miRNA–mRNA network was generated and visualized by Cytoscape3.8.0. TIMER analysis was conducted to study the abundance of immune infiltrates in tumor microenvironment of pancreatic carcinoma. Expression levels of immune checkpoint genes in subgroups stratified by the model were compared by Wilcoxon test. Gene Set Enrichment Analysis (GSEA) was performed to analyze the enriched signaling pathways between subgroups. Differential analysis revealed 1826 genes differentially up-regulated in pancreatic carcinoma and 1276 genes differentially down-regulated. A total of 700 immune-related miRNAs were obtained, of which 7 miRNAs were significantly associated with OS of patients and used to establish a prognostic model with accurate predictive performance. There were 99 mRNAs overlapped from the 318 target genes of the 7 miRNAs and the key modules genes analyzed by WGCNA. Patient samples were categorized as high or low risk according to the prognostic model, which were significantly associated with dendritic cell infiltration and expression of immune checkpoint genes (TNFSF9, TNFRSF9, KIR3DL1, HAVCR2, CD276 and CD80). GSEA showed remarkably enriched signaling pathways in the two subgroups. This study identified an immune-related 7-miRNA based prognostic model for pancreatic carcinoma, which could be used as a reliable tool for prognosis. [ABSTRACT FROM AUTHOR]