MiR-182-5p, as a member of miRNA family, can be detected in lung cancer and plays an important role in lung cancer. To explore the clinical value of miR-182-5p in lung squamous cell carcinoma (LUSC) and to unveil the molecular mechanism of LUSC. The clinical value of miR-182-5p in LUSC was investigated by collecting and calculating data from The Cancer Genome Atlas (TCGA) database, the Gene Expression Omnibus (GEO) database, and real-time quantitative polymerase chain reaction (RT-qPCR). Twelve prediction platforms were used to predict the target genes of miR-182-5p. Protein-protein interaction (PPI) networks and gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to explore the molecular mechanism of LUSC. The expression of miR-182-5p was significantly over-expressed in LUSC than in non-cancerous tissues, as evidenced by various approaches, including the TCGA database, GEO microarrays, RT-qPCR, and a comprehensive meta-analysis of 501 LUSC cases and 148 non-cancerous cases. Furthermore, a total of 81 potential target genes were chosen from the union of predicted genes and the TCGA database. GO and KEGG analyses demonstrated that the target genes are involved in pathways related to biological processes. PPIs revealed the relationships between these genes, with EPAS1, PRKCE, NR3C1, and RHOB being located in the center of the PPI network. MiR-182-5p upregulation greatly contributes to LUSC and may serve as a biomarker in LUSC.

The present study investigated the sensitization of 5‑fluorouracil (5‑FU)‑resistant colon cancer cells in vitro, using oxymatrine, a Chinese herb, and a quinolizidine alkaloid compound extracted from the root of Sophora flavescens. The HCT‑8 colon cancer cell line and its 5‑FU‑resistant subline HCT‑8/5‑FU were treated with 5‑FU and oxymatrine, alone or in combination, at various doses. The cells were subsequently assessed for changes in cell viability, apoptosis and morphology and analyzed by fluorescence microscopy and western blotting. The data demonstrated that HCT‑8/5‑FU markedly increased the dose of 5‑FU required for the suppression of tumor cell viability (78.77±1.90 µg/ml vs. 9.20±0.96 µg/ml in parental HCT‑8 cells), whereas HCT‑8/5‑FU induced the tumor cell epithelial‑mesenchymal transition (EMT). By contrast, oxymatrine alone and in combination with 5‑FU altered HCT‑8/5‑FU cell morphology, apoptosis and EMT phenotypes. The combination of oxymatrine and 5‑FU reduced the protein expression of snail family transcriptional repressor 2 and vimentin, phosphorylated p65 and induced the expression of E‑cadherin, by inhibiting the nuclear factor κB (NF‑κB) signaling pathway. In conclusion, the data from the present study demonstrated that EMT was associated with 5‑FU chemoresistance in HCT‑8/5‑FU colon cancer cells, and that oxymatrine treatment was able to reverse such resistance. Oxymatrine may regulate tumor cell EMT and inactivate the NF‑κB signaling pathway, and may therefore serve as a potential therapeutic drug to reverse 5‑FU resistance in colon cancer cells.

Background: Machine learning is now well-developed in non-small cell lung cancer (NSCLC) radiotherapy. But the research trend and hotspots are still unclear. To investigate the progress in machine learning in radiotherapy NSCLC, we performed a bibliometric analysis of associated research and discuss the current research hotspots and potential hot areas in the future.Methods: The involved researches were obtained from the Web of Science Core Collection database (WoSCC). We used R-studio software, the Bibliometrix package and VOSviewer (Version 1.6.18) software to perform bibliometric analysis.Results: We found 197 publications about machine learning in radiotherapy for NSCLC in the WoSCC, and the journal Medical Physics contributed the most articles. The University of Texas MD Anderson Cancer Center was the most frequent publishing institution, and the United States contributed most of the publications. In our bibliometric analysis, “radiomics” was the most frequent keyword, and we found that machine learning is mainly applied to analyze medical images in the radiotherapy of NSCLC.Conclusion: The research we identified about machine learning in NSCLC radiotherapy was mainly related to the radiotherapy planning of NSCLC and the prediction of treatment effects and adverse events in NSCLC patients who were under radiotherapy. Our research has added new insights into machine learning in NSCLC radiotherapy and could help researchers better identify hot research areas in the future.

Background and Aim: The biological functions and clinical implications of lysophosphatidylcholine acyltransferase 1 (LPCAT1) remain unclarified in gastric cancer (GC). The aim of the current study was to explore the possible clinicopathological significance of LPCAT1 and its perspective mechanism in GC tissues.Materials and Methods: The protein expression and mRNA levels of LPCAT1 were detected from in‐house immunohistochemistry and public high‐throughput RNA arrays and RNA sequencing. To have a comprehensive understanding of the clinical value of LPCAT1 in GC, all enrolled data were integrated to calculate the expression difference and standard mean difference (SMD). The biological mechanism of LPCAT1 in GC was confirmed by computational biology and in vitro experiments. Migration and invasion assays were also conducted to confirm the effect of LPCAT1 in GC.Results: Both protein and mRNA expression levels of LPCAT1 in GC were remarkably higher than those in noncancerous controls. Comprehensively, the SMD of LPCAT1 mRNA was 1.11 (95% CI = 0.86–1.36) in GC, and the summarized AUC was 0.85 based on 15 datasets containing 1727 cases of GC and 940 cases of non‐GC controls. Moreover, LPCAT1 could accelerate the invasion and migration of GC by boosting the neutrophil degranulation pathway and disturbing the immune microenvironment.Conclusion: An increased level of LPCAT1 may promote the progression of GC.

Incorrect Funding In the published article, there was an error in the Funding statement. The funding from Guangxi key research and development program was omitted. The correct Funding statement appears below.FUNDINGThe authors declare that this study received funding from Guangxi key research and development program No. (GK) AB18221080, The Basic Ability Enhancement Program for Young and Middle-aged Teachers of Guangxi (2020KY03036), Guangxi Medical University Foundation (GXMUYSF201918), Foundation of Guangxi Health and Family Planning Commission (Z20190581), Foundation of the Second Affiliated Hospital of Guangxi Medical University (hbrc202104). The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.