Investigating the effect of Icaritin on hepatocellular carcinoma based on network pharmacology.

Hepatocellular carcinoma is one of the cancers that kill people in the global population. Icaritin, a small molecule drug approved by NMPA, has demonstrated potential anti-HCC effects. However, its underlying molecular mechanisms remain unclear. We employed a multi-omics approach in this study, including pharmaco-omics and proteomics, to look into the Icaritin's possible molecular targets and workings in the therapy of HCC. Through pharmaco-omics analysis, we identified ten putative target genes of Icaritin, including FYN. The relationship between Icaritin and these target genes, particularly FYN, was further validated through in vitro and in vivo experiments. The outcomes revealed that Icaritin may exert its anti-HCC effects through modulating the FYN gene, highlighting the importance of multi-omics approaches in drug discovery research. This research gives valuable insights regarding the therapeutic potential of Icaritin against HCC and its possible molecular mechanisms.

Combination of the platelet-to-lymphocyte ratio and fibrinogen may predict 5-year overall survival of patient in non-small cell lung cancer treated with surgery.

Background: Non-small cell lung cancer (NSCLC) is a major type of lung cancer with high incidence and mortality. Systemic inflammatory response (SIR) and an imbalance of the coagulation system are both associated with the tumor progression. However, few studies have investigated the prognostic utility of a combination of inflammation and the coagulation system in NSCLC. The combination of platelet-to-lymphocyte ratio (PLR) and fibrinogen (FIB) (PLR-FIB; defined as PLR × FIB) is an indicator reflecting SIR and coagulation concurrently, which have potentiality to predict prognosis of NSCLC. Methods: This retrospective, single-center study included 314 NSCLC patients with surgery. According to a cutoff value for the PLR-FIB, we divided participants into a low-PLR-FIB group and a high-PLR-FIB group. We retrospectively collected the data on 314 patients and used univariate and multivariate analyses to investigate the relationship between the PLR-FIB and survival. Results: Univariate analysis showed that adenosquamous carcinoma (ASC) (P=0.002), high PLR-FIB (P=0.023), and tumor-node-metastasis (TNM) stage III-IV (P<0.001) were associated with a poor outcome. On multivariate analysis, low PLR-FIB [hazard ratio (HR), 0.587; 95% confidence interval (CI): 0.359-0.985; P=0.044], and TNM stage I-II (HR, 0.380; 95% CI: 0.245-0.590; P<0.001) were independent factors of a better prognosis. ASC type was an independent prognostic factor of poor outcome (HR, 5.513; 95% CI: 1.895-16.034; P=0.002). There were no significant differences in patient demographics or clinical characteristics between the two PLR-FIB groups (P>0.05). The 5-year overall survival (OS) rates were 80.8% and 67.9% for the low-PLR-FIB group and high-PLR-FIB group, respectively (P=0.02). Conclusions: Preoperative PLR-FIB was found to be an independent prognostic factor for 5-year overall survival in patients with NSCLC treated with surgery.

Small Nucleolar RNA Host Gene 1 (SNHG1) and Chromosome 2 Open Reading Frame 48 (C2orf48) as Potential Prognostic Signatures for Liver Cancer by Constructing Regulatory Networks.

BACKGROUND Liver cancer is a common malignant tumor with poor prognosis. The present study sought to identify potential signatures that can predict the prognosis of patients with liver cancer. MATERIAL AND METHODS The RNA sequencing (RNA-seq) and clinical information of liver cancer patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) were identified between liver cancer and adjacent normal tissues. After predicting lncRNA-miRNA and miRNA-mRNA pairs using online databases, the competing endogenous RNA (ceRNA) networks were constructed. Furthermore, the prognostic value of these differentially expressed genes was evaluated using univariate and multivariate Cox regression analyses. RESULTS After constructing the ceRNA network, 2 lncRNAs small nucleolar RNA host gene 1 (SNHG1) and chromosome 2 open reading frame 48 (C2orf48) with the most nodes were identified. Correlation analysis revealed that SNHG1 was correlated with miR-195 and C2orf48 was correlated with miR-195 and miR-93. High expression of SNHG1, C2orf48, and miR-93 can contribute to poorer clinical outcomes compared to low expression. Furthermore, low miR-195 expression was correlated with shorter survival time than was high expression. SNHG1 and C2orf48 were closely associated with histology grade. Univariate and multivariate Cox regression analyses confirmed that SNHG1 and C2orf48 are risk factors for liver cancer. CONCLUSIONS Our findings revealed that SNHG1 and C2orf48 possess potential prognostic value and should be considered as possible biomarkers for predicting clinical outcomes for patients with liver cancer.

Concordance of hospital-based cancer registry data with a clinicians' database for breast cancer.

OBJECTIVE: Reliable information is essential to both clinical and policy decision making. We aimed to shed lights on the similarity and differences between a hospital-based cancer registry with a clinicians' database for breast cancer by comparing the registered data on the same year. METHODS: We performed a head-to-head comparison of breast cancer cases extracted from the hospital-based cancer registry and the clinicians' database maintained by the Division of Breast Surgery at the National Cancer Center Hospital in 2004. RESULTS: The hospital-based cancer registry reported 827 cases of newly diagnosed breast cancer patients in 2004, while the clinicians' database contained 366 surgically treated cases from 2004. Of these, 276 cases overlapped. Presence or absence of treatment modality was discordant in 15% for radiation therapy, 19% for chemotherapy, and 24% for hormone therapy between the two data sets. Furthermore, the recorded disease pathology was discordant in 13% for pathology and 28% for staging, with 22% for T-stage, 7% for N-stage, 7% for M-stage. CONCLUSIONS: Although information contained in hospital-based cancer registry and clinicians' database are generally accurate, some important differences were revealed as a result of varying interpretations of clinical information. Analyses of these data sets must be made with attention to details such as eligible patients, registered treatment, and timing of registration.