Characterization of anoikis-based molecular heterogeneity in pancreatic cancer and pancreatic neuroendocrine tumor and its association with tumor immune microenvironment and metabolic remodeling.

Background: Accumulating evidence suggests that anoikis plays a crucial role in the onset and progression of pancreatic cancer (PC) and pancreatic neuroendocrine tumors (PNETs); nevertheless, the prognostic value and molecular characteristics of anoikis in cancers are yet to be determined. Materials and methods: We gathered and collated the multi-omics data of several human malignancies using the TCGA pan-cancer cohorts. We thoroughly investigated the genomics and transcriptomics features of anoikis in pan-cancer. We then categorized a total of 930 patients with PC and 226 patients with PNETs into distinct clusters based on the anoikis scores computed through single-sample gene set enrichment analysis. We then delved deeper into the variations in drug sensitivity and immunological microenvironment between the various clusters. We constructed and validated a prognostic model founded on anoikis-related genes (ARGs). Finally, we conducted PCR experiments to explore and verify the expression levels of the model genes. Results: Initially, we identified 40 differentially expressed anoikis-related genes (DE-ARGs) between pancreatic cancer (PC) and adjacent normal tissues based on the TCGA, GSE28735, and GSE62452 datasets. We systematically explored the pan-cancer landscape of DE-ARGs. Most DE-ARGs also displayed differential expression trends in various tumors, which were strongly linked to favorable or unfavorable prognoses of patients with cancer, especially PC. Cluster analysis successfully identified three anoikis-associated subtypes for PC patients and two anoikis-associated subtypes for PNETs patients. The C1 subtype of PC patients showed a higher anoikis score, poorer prognosis, elevated expression of oncogenes, and lower level of immune cell infiltration, whereas the C2 subtype of PC patients had the exact opposite characteristics. We developed and validated a novel and accurate prognostic model for PC patients based on the expression traits of 13 DE-ARGs. In both training and test cohorts, the low-risk subpopulations had significantly longer overall survival than the high-risk subpopulations. Dysregulation of the tumor immune microenvironment could be responsible for the differences in clinical outcomes between low- and high-risk groups. Conclusions: These findings provide fresh insights into the significance of anoikis in PC and PNETs. The identification of subtypes and construction of models have accelerated the progress of precision oncology.

A Novel Ferroptosis-Related lncRNAs Signature Predicts Clinical Prognosis and Is Associated With Immune Landscape in Pancreatic Cancer.

Pancreatic cancer is one of the most lethal malignancies and currently therapies are severely lacking. In this study, we aimed to establish a novel ferroptosis-related lncRNAs signature to predict the prognosis of patients with pancreatic cancer and evaluate the predictive abilities of candidate lncRNAs. According to The Cancer Genome Atlas (TCGA) database, a total of 182 patients with pancreatic cancer were included in our study. Ferroptosis-related lncRNAs were screened by Pearson correlation analysis with 60 reported ferroptosis-related genes. Through univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate regression analyses, a novel signature based on five ferroptosis-related lncRNAs(ZNF236-DT, CASC8, PAN3-AS1, SH3PXD2A-AS1, LINP1) was constructed. Risk-related differentially expressed genes (DEGs) were subjected to enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The results revealed that immune cell infiltration, immune-related functions and checkpoints were factors to affect prognoisis of pancreatic cancer. In summary, we identified the prognostic ferroptosis-related lncRNAs(ZNF236-DT, CASC8, PAN3-AS1, SH3PXD2A-AS1, LINP1) in pancreatic cancer and these lncRNAs may serve as therapeutic targets for pancreatic cancer.

CXCL5/NF-κB Pathway as a Therapeutic Target in Hepatocellular Carcinoma Treatment.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common malignant cancer worldwide. CXCL5 has a role in inhibiting cell viability and metastasis in many tumors. In the present study, we investigated the role of CXCL5 in HCC and explored the underlying mechanism. Material and Methods. RT-qPCR and western blot were performed to evaluate the mRNA and protein levels of CXCL5. CCK-8 and transwell assay were applied to measure the proliferative and invasive abilities. Meanwhile, the Kaplan-Meier method was used to assess the survival of HCC patients. RESULTS: CXCL5 was upregulated in HCC tissues, which predicted a shorter overall survival in HCC. CXCL5 was a target gene of miR-577, and its expression was mediated by miR-577 in HCC. Knockdown of CXCL5 suppressed HuH-7 cell proliferation, invasion, and EMT and inhibited the NF-κB signaling pathway in cells. Moreover, knockdown of CXCL5 inhibited the xenograft growth of HuH-7 cells. CONCLUSION: Overexpression of CXCL5 predicts poor prognosis in HCC patients. Knockdown of CXCL5 inhibits cell proliferation and invasion through the NF-κB signaling pathway in HCC. The newly identified role of the CXCL5/miR-577/NF-κB axis provides novel insights into the targeted therapy of HCC.

Exponential amplification reaction and triplex DNA mediated aggregation of gold nanoparticles for sensitive colorimetric detection of microRNA.

Abnormal expression of specific microRNAs (miRNAs) is associated with the occurrence, development and prognosis of many diseases. In this study, a miRNA detection method based on exponential amplification reaction (EXPAR) and triplex DNA mediated aggregation of gold nanoparticles (AuNPs) was established. Specifically, one class of AuNPs is conjugated with an EXPAR probe, on which there is a complementary sequence of the target miRNA. The EXPAR reaction is triggered and duplex DNA is formed on the surface of AuNPs when the target miRNA exists. Then, single DNA probe on another class of AuNPs interacts with the duplex DNA to form triplex DNA, leading to the aggregation of the two classes of AuNPs, which could be quantified by UV-vis. The proposed method is highly selective and can afford a detection limit of 0.23 fM. Notably, all the ingredients needed for the analysis can pre-add to a tube and only 30 min is needed for the whole detection process. The method is simple, fast and with considerable selectivity and accuracy, so a great potential for this method is expected to meet the need of point-of-care testing of miRNA.