Ferulic acid (4-hydroxy-3-methoxycinnamic acid, FA) is a hydroxycinnamic acid derived from a rich polyphenolic compound. This study aimed to investigate the effect of ferulic acid (4-hydroxy-3-methoxycinnamic acid; FA) on cell proliferation, invasion, apoptosis, and autophagy in Hela and Caski cervical carcinoma cell lines. The cell proliferation of FA in Hela and Caski cells were detected by MTT assay. The cell invasion of FA in Hela and Caski cells were detected by Transwell assay. Subsequently, MMP-9 mRNA expression for cell invasion was detected by RT-PCR. Additionally, cell cycle and apoptosis were assayed using flow cytometry. Expression levels of 7 proteins for both cell cycle and autophagy were measured by Western blot analysis. After treated with FA (2.0 mM) for 48 h, the inhibition rates of FA in Hela and Caski cells were 88.3 and 85.4%, respectively. In addition, FA inhibited cell invasion through reducing MMP-9 mRNA expression. FA induced arrest in G0/G1 phase of the cell cycle in Hela and Caski cells with dose dependent (P < 0.05). Meanwhile, FA induced the cell cycle-related proteins expression such as p53 and p21, and reduced Cyclin D1 and Cyclin E levels. Moreover, FA decreased the autophagy-related proteins such as LC3-II, Beclin1 and Atg12-Atg5 in a dose-dependent manner. FA can significantly inhibit cell proliferation and invasion in Hela and Caski cells. It might be acted as an anti-cancer drug through inhibiting the autophagy and inducing cell cycle arrest in human cervical carcinoma cells.

Cisplatin (DDP) resistance poses a significant challenge in the treatment of ovarian cancer. Studies have shown that the combination of certain polysaccharides derived from plants with DDP is an effective approach to overcoming drug resistance in some cancers. Angelica sinensis (Oliv.) Diels has been used for centuries in China to treat gynecological ailments. Numerous studies indicate that Angelica sinensis polysaccharide (ASP), an extract from Angelica sinensis, can inhibit various forms of cancer. However, the impact of ASP on ovarian cancer remains unexplored. Through both in vitro and in vivo experiments, our study revealed the capability of ASP to effectively reversing DDP resistance in cisplatin-resistant ovarian cancer cells, while exhibiting acceptable safety profiles in vivo. To elucidate the mechanism underlying drug resistance reversal, we employed RNA-seq analysis and identified GPX4 as a key gene. Considering the role of GPX4 in ferroptosis, we conducted additional research to explore the effects of combining ASP with DDP on SKOV3/DDP cells. In summary, our findings demonstrate that the combination of ASP and DDP effectively suppresses GPX4 expression in SKOV3/DDP cells, thereby reversing their resistance to DDP.

Background: Hyperoside (Hy) is a plant-derived quercetin 3-d-galactoside that exhibits inhibitory activities on various tumor types. The objective of the current study was to explore Hy effects on cervical cancer cell proliferation, and to perform a transcriptome analysis of differentially expressed genes.Methods: Cervical cancer HeLa and C-33A cells were cultured and the effect of Hy treatment was determined using the Cell Counting Kit-8 (CCK-8) assay. After calculating the IC50 of Hy in HeLa and C-33A cells, the more sensitive to Hy treatment cell type was selected for RNA-Seq. Differentially expressed genes (DEGs) were identified by comparing gene expression between the Hy and control groups. Candidate genes were determined through DEG analysis, protein interaction network (PPI) construction, PPI module analysis, transcription factor (TF) prediction, TF-target network construction, and survival analysis. Finally, the key candidate genes were verified by RT-qPCR and western blot.Results: Hy inhibited HeLa and C33A cell proliferation in a dose- and time-dependent manner, as determined by the CCK-8 assay. Treatment of C-33A cells with 2 mM Hy was selected for the subsequent experiments. Compared with the control group, 754 upregulated and 509 downregulated genes were identified after RNA-Seq. After functional enrichment, 74 gene ontology biological processes and 43 Kyoto Encyclopedia of Genes and Genomes pathways were obtained. According to the protein interaction network (PPI), PPI module analysis, TF-target network construction, and survival analysis, the key genesMYC,CNKN1A,PAX2,TFRC,ACOX2,UNC5B,APBA1,PRKACA,PEAR1,COL12A1,CACNA1G,PEAR1, andCCNA2were detected. RT-qPCR was performed on the key genes, and Western blot was used to verifyC-MYCandTFRC.C-MYCandTFRCexpressions were lower and higher than the corresponding values in the control group, respectively, in accordance with the results from the RNA-Seq analysis.Conclusion: Hy inhibited HeLa and C-33A cell proliferation throughC-MYCgene expression reduction in C-33A cells andTFRCregulation. The results of the current study provide a theoretical basis for Hy treatment of cervical cancer.

Purpose: The variation in inflammation in chronic obstructive pulmonary disease (COPD) between individuals is genetically determined. This study aimed to identify gene signatures of COPD through bioinformatics analysis based on multiple gene sets and explore their immune characteristics and transcriptional regulation mechanisms. Methods: Data from four microarrays were downloaded from the Gene Expression Omnibus database to screen differentially expressed genes (DEGs) between COPD patients and controls. Weighted gene co-expression network analysis was applied to identify trait-related modules and then select key module-related DEGs. The optimized gene set of signatures was obtained using the least absolute shrinkage and selection operator (LASSO) regression analysis. The CIBERSORT algorithm and Pearson correlation test were used to analyze the relationship between gene signatures and immune cells. Finally, public databases were used to predict the transcription factors (TFs) and upstream miRNAs. Results: A total of 127 DEGs in COPD were identified from the combined dataset. By considering the intersection of DEGs and genes in two trait-related modules, 83 key module-related DEGs were identified, which were mainly enriched in interleukin-related pathways. Seven-gene signatures, including MTHFD2, KANK3, GFPT2, PHLDA1, HS3ST2, FGG, and RPS4Y1, were further selected using the LASSO algorithm. These gene signatures showed the predictive potential for COPD risks and were significantly correlated with 18 types of immune cells. Finally, nine miRNAs and three TFs were predicted to target MTHFD2, GFPT2, PHLDA1, and FGG. Conclusion: We proposed the seven-gene-signature to predict COPD risk and explored its potential immune characteristics and regulatory mechanisms.