Cryptotanshinone (IUPAC name: (R)-1,2,6,7,8,9-hexahydro-1,6,6-trimethyl-phenanthro(1,2-b)furan-10,11-dione), a biologically active constituent extracted from the roots and rhizomes of the plant Salvia miltiorrhiza, has been studied in depth as a medicinally active compound and shown to have efficacy in the treatment of numerous diseases and disorders. In this review, we describe in detail the current status of cryptotanshinone research, including findings relating to the structure, pharmacokinetics, pharmacological activity, and derivatives of this compound. Cryptotanshinoneh as a diverse range of pharmacological effects, including anti-cancer, anti-inflammatory, immune regulatory, neuroprotective, and anti-fibrosis activities. Studies on the molecular mechanisms underlying the activities of cryptotanshinone have established that the JAK2/STAT3, PI3K/AKT, NF-κB, AMPK, and cell cycle pathways are involved in the inhibitory and pro-apoptotic effects of cryptotanshinone on different tumor cell lines, these molecular pathways interact in a coordinated manner to inhibit cell proliferation, migration and invasion,and induce transformation, autophagy, necrosis, and cellular immunity. The anti-inflammatory mechanisms of cryptotanshinone have been found to be associated with the TLR4-MyD88/PI3K/Nrf2 and TLR4-MyD88/NF-κB/MAPK pathways, whereasthe Hedgehog, NF-κB, and Nrf-2/HO-1 pathways are regulated by cryptotanshinone to reduce organ fibrosis, and its inhibitory effects on the PI3K/AKT-eNOS pathway have been linked to neuroprotective effects. Given the potential medicinal utility of cryptotanshinone, further research is needed to verify the efficacy and safety of this compound in clinical use, evaluate its pharmacological activity, and identify molecular targets.

As one of the most common malignant tumors in humans, lung cancer has experienced a gradual increase in morbidity and mortality. This study examined prognosis-related methylation-driven genes specific to lung adenocarcinoma (LUAD) to provide a basis for prognosis prediction and personalized targeted therapy for LUAD patients. The methylation and survival time data from LUAD patients in the TCGA database were downloaded. The MethylMix algorithm was used to identify the differential methylation status of LUAD and adjacent tissues based on the β-mixture model to obtain disease-related methylation-driven genes. A COX regression model was then used to screen for LUAD prognosis-related methylation-driven genes, and a linear risk model based on five methylation-driven gene expression profiles was constructed. A methylation and gene expression combined survival analysis was performed to further explore the prognostic value of 5 genes independently. There were 118 differentially expressed methylation-driven genes in the LUAD tissues and adjacent tissues. Five of the genes, CCDC181, PLAU, S1PR1, ELF3, and KLHDC9, were used to construct a prognostic risk model. Overall, the survival time was significantly lower in the high-risk group compared with that in the low-risk group (P < 0.05). In addition, the methylation and gene expression combined survival analysis found that the combined expression levels of the genes CCDC181, PLAU, and S1PR1 as well as KLHDC9 alone can be used as independent prognostic markers or drug targets. Our findings provide an important bioinformatic basis and relevant theoretical basis for guiding subsequent LUAD early diagnosis and prognosis assessments.

Danshen (Salvia miltiorrhiza Bunge), a natural powerful drug for various conditions treatment, has traditionally been used in Asian countries for centuries as anticancer agent, anti‐inflammatory agent, and antioxidant. More recently, it is explored in combination with other herbs for skeletal diseases therapy; bone‐targeting compounds with pharmacological activities have been isolated from various sources of traditional Chinese medicine (TCM), including Danshen. In this case, some evidence supports that Danshen may treat myelofibrosis (MF) by exerting its antitumor effect. To study the specific mechanism of Danshen in the treatment of MF, we used bioinformatics databases to determine its active ingredients. Then, identification of target proteins related to MF was made using a network pharmacology analysis platform. In our results, 20 key active compounds and 457 key targets of Danshen were identified. In‐depth network analysis of the top diseases, functions, and pathways suggested that a common underlying mechanism linked Danshen involvement with MF. Finally, 5 potential targets were confirmed by the analysis; these 5 targets, as well as 20 previously identified compounds, were subjected to molecular docking experiments. The results indicated that cryptotanshinone of Danshen may affect MF by acting on the key genes in the JAK‐STAT signalling pathway and the TGF‐β signalling pathway.

Background. Long noncoding RNAs (lncRNAs) act as competing endogenous RNAs for microRNAs in cancer metastasis. However, the roles of lncRNA‐mediated competing endogenous RNA (ceRNA) networks for breast cancer (BC) are still unclear. Material and Methods. The expression profiles of mRNAs, lncRNAs, and miRNAs with BC were extracted from The Cancer Genome Atlas database. Weighted gene coexpression network analysis was conducted to extract differentially expressed mRNAs (DEmRNAs) that might be core genes. Through miRWalk, TargetScan, and miRDB to predict the target genes, an abnormal lncRNA‐miRNA‐mRNA ceRNA network with BC was constructed. The survival possibilities of mRNAs, miRNAs, and lncRNAs for patients with BC were determined by Kaplan‐Meier survival curves and Oncomine. Results. We identified 2134 DEmRNAs, 1059 differentially expressed lncRNAs (DElncRNAs), and 86 differentially expressed miRNAs (DEmiRNAs). We then compose a ceRNA network for BC, including 72 DElncRNAs, 8 DEmiRNAs, and 12 DEmRNAs. After verification, 2 lncRNAs (LINC00466, LINC00460), 1 miRNA (Hsa‐mir‐204), and 5 mRNAs (TGFBR2, CDH2, CHRDL1, FGF2, and CHL1) were meaningful as prognostic biomarkers for BC patients. In the ceRNA network, we found that three axes were present in 10 RNAs related to the prognosis of BC, namely, LINC00466‐Hsa‐mir‐204‐TGFBR2, LINC00466‐Hsa‐mir‐204‐CDH2, and LINC00466‐Hsa‐mir‐204‐CHRDL1. Conclusion. This study highlighted lncRNA‐miRNA‐mRNA ceRNA related to the pathogenesis of BC, which might be used for latent diagnostic biomarkers and therapeutic targets for BC.

Background. The incidence of squamous lung cancer (LUSC) has substantially increased. Systematic studies of metabolic genomic patterns are fundamental for the treatment and prediction of LUSC. Because cancer metabolism and immune cell metabolism have been studied in depth, metabolism and the state and function of immune cells have become key factors in tumor development. This also indicates that metabolic genes and the tumor immune microenvironment (TME) are crucial in tumor treatment. This study is aimed at dissecting the connection between TME and LUSC digestion‐related qualities. Methods. The information used in this study was obtained from The Cancer Genome Atlas dataset. Metabolism‐related genes in patients with LUSC were screened, and relevant clinical data were collated. Next, genes associated with prognosis were screened using univariate COX regression and LASSO regression analyses. Finally, a timer database study was conducted to analyze the molecular mechanisms of immune cell infiltration of LUSC prognosis‐related metabolic genes at the immune cell level. Results. Nine metabolism‐related genes were identified: ADCY7, ALDH3B1, CHIA, CYP2C18, ENTPD6, GGCT, HPRT1, PLA2G1B, and PTGIS. A clinical prediction model for LUSC based on metabolism‐related genes was constructed. In addition, 22 subpopulations of tumor‐infiltrating immune cells (TIIC) in the TME were analyzed using the CIBERSORT algorithm. Finally, we used the TIMER database to analyze the immune infiltration of LUSC and the relationship between metabolism‐related genes and immune cells. Conclusion. Our study identified metabolic genes associated with the prognosis of LUSC, which are important markers for its diagnosis, clinically relevant assessments, and prognosis. The relationship between metabolic genes with prognostic impact and immune infiltration was also analyzed, and a metabolic gene‐based clinical prediction model was identified, providing a new perspective for LUSC treatment.