Chaenomeles speciosa Nakai is commonly used in traditional Chinese medicine for a variety of health-promoting effects. The present study aimed to investigate the antitumor effects of Chaenomeles speciosa Nakai. The tumor-inhibitory activity of the ethanol extract of Chaenomeles speciosa Nakai (EEC) was evaluated by in vitro growth assays of tumor cells and in vivo H22 tumor formation assays in mice. Mitochondrial membrane potential and DNA ladder assays were used to detect tumor cell apoptosis in the presence of EEC. To investigate the cellular targets of EEC, the immunomodulatory genes PD-L1, Foxp3 and TGF-β were detected in the tumor tissue using reverse transcription polymerase chain reaction (RT-PCR). Immune responses were determined by hemolysis and lymphocyte proliferation assays. EEC markedly inhibited the proliferation of the H22 cells in a dose-dependent manner. Moreover, it induced DNA fragmentation and decreased the mitochondrial membrane potential. In vivo, EEC inhibited tumor growth and enhanced the immune responses in mice, while the expression of PD-L1, Foxp3 and TGF-β was inhibited in the tumor tissue. These results provide the first evidence that EEC may inhibit tumor growth by directly killing tumor cells and enhancing immune function. Thus, it is a natural source for safe anticancer medicine.

Purpose: Stomach adenocarcinoma (STAD) is one of the common cancers globally. Cuproptosis is a newly identified cell death pattern. The role of cuproptosis-associated lncRNAs in STAD is unknown.Methods: STAD patient data from TCGA were used to identify prognostic lncRNAs by Cox regression and LASSO. A nomogram was constructed to predict patient survival. The biological profiles were evaluated through GO and KEGG.Results: We identified 298 cuproptosis-related lncRNAs and 13 survival-related lncRNAs. Patients could be categorized into either high risk group or low risk group with 9-lncRNA risk model with significantly different survival time (p < 0.001). ROC curve and nomogram confirmed the 9-lncRNA risk mode had good prediction capability. Patients in the lower risk score had high gene mutation burden. We also found that patients in the two groups might respond differently to immune checkpoint inhibitors and some anti-tumor compounds.Conclusion: The nomogram with 9-lncRNA may help guide treatment of STAD. Future clinical studies are necessary to verify the nomogram.

Platelets, macrophages, endothelial cells, and smooth muscle cells can all contribute to atherosclerosis. To investigate the molecular events leading to atherosclerosis involving platelets, macrophages, endothelial cells, and smooth muscle cells, we carried out suppression subtractive hybridization (SSH) to generate a profile of genes overexpressed in the aorta and blood cells in high fat diet rats. From 85 random SSH-cDNA clones, we have screened 23 clones by Northern blotting, which were scored as overexpressed in the aorta and blood cells in high fat diet rats compared to normal diet rats. Sequencing showed that the gene corresponded to the known gene in the public databases, previously shown to be overexpressed in atherosclerosis, heparin-binding epidermal growth factor (EGF)-like growth factor (HB-EGF), and local production was seen in vascular smooth muscle and endothelial cells by RT-PCR and Western blotting. These results show that SSH provides a very efficient means to produce a profile of differentially expressed genes in atherosclerosis. The identified gene may provide novel points of therapeutic intervention and pathophysiological mechanisms in atherosclerosis.

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Background: Head and neck squamous cell carcinoma (HNSCC) arises from squamous cells in the oral cavity, pharynx and larynx. Although HNSCC is sensitive to radiotherapy, patient prognosis is poor. Necroptosis is a novel programmed form of necrotic cell death. The prognostic value of necroptosis-associated gene expression in HNSCC has not been explored.Material and Methods: We downloaded mRNA expression data of HNSCC patients from TCGA databases and Gene Expression Omnibus (GEO) databases, and compared gene expression between tumor tissues and adjacent normal tissues to identify differentially expressed genes (DEGs) and necroptosis-related prognostic genes. A model with necroptosis-related genes was established to predict patient prognosis via LASSO method and Kaplan-Meier analysis. GSE65858 data set (n = 270) from GEO was used to verify the model’s predictive ability. Gene set enrichment analyses, immune microenvironment analysis, principal component analysis, and anti-tumor compound IC50 prediction were also performed.Results: We identified 49 DEGs and found 10 DEGs were associated with patient survival (p < 0.05). A risk model of 6-gene signature was constructed using the TCGA training data set and further validated with the GEO data set. Patients in the low-risk group survived longer than those in the high-risk group (p < 0.05) in the GEO validation sets. Functional analysis showed the two patient groups were associated with distinct immunity conditions and IC50.Conclusion: We constructed a prognostic model with 6 necroptosis-associated genes for HNSCC. The model has potential usage to guide treatment because survival was different between the two groups.