Preoperative platelet distribution width-to-platelet ratio combined with serum thyroglobulin may be objective and popularizable indicators in predicting papillary thyroid carcinoma.

OBJECTIVES: The incidence of papillary thyroid carcinoma (PTC) has increased more rapidly than that of any other cancer type in China. Early indicators with high sensitivity and specificity during diagnosis are required. To date, there has been a paucity of studies investigating the relationship between preoperative platelet distribution width-to-platelet count ratio (PPR) and PTC. This study thus aimed to assess the diagnostic value of PPR combined with serum thyroglobulin (Tg) in patients with PTC. METHODS: A total of 1001 participants were included in our study. 876 patients who underwent surgery for nodular goiter were divided into the PTC group or benign thyroid nodule (BTN) group according to pathology reports, and 125 healthy controls (HCs) were included. Preoperative hemogram parameters and serum Tg levels were compared among three groups. Receiver operating characteristic (ROC) curve was used to evaluate the value of PPR combined with serum Tg for diagnosing PTC. RESULTS: Platelet distribution width (PDW) and PPR levels were higher in the PTC group than in the BTN and HC groups (both p < 0.05) but did not significantly differ between the BTN and HC groups. PDW and PPR levels significantly differed in the presence/absence of lymph node metastasis, the presence/absence of capsule invasion (p = 0.005), and TNM stages (p < 0.001). Multivariable analyses indicated that high serum Tg levels [adjusted odds ratio (OR), 1.007; 95% confidence interval (CI), 1.004-1.009; p < 0.001], high neutrophil-to-lymphocyte ratio (NLR,adjusted OR, 1.928; 95% CI, 1.619-2.295; p < 0.001), and high PPR (adjusted OR, 1.378; 95% CI, 1.268-1.497; p < 0.001) were independent risk factors for PTC. In ROC analysis, the areas under the curves (AUCs) of serum Tg, PDW, PPR, and NLR for predicting PTC were 0.603, 0.610, 0.706, and 0.685, respectively. PPR combined with serum Tg (PPR + Tg) had a higher diagnostic value (AUC, 0.738; sensitivity, 60%; specificity, 74.7%) compared with PDW + Tg (AUC, 0.656; sensitivity, 64.4%; specificity, 59.9%) and NLR + Tg (AUC, 0.714; sensitivity, 61.6%; specificity, 71.1%). CONCLUSIONS: Preoperative PPR combined with serum Tg may be objective and popularizable indicators for effective predicting PTC.

[Value of Head and Neck CT Angiography in the Clinical Evaluation of Intraoperative Bleeding Volume of Carotid Body Tumours].

Objective To investigate the value of head and neck CT angiography(CTA)in the evaluation of intraoperative hemorrhage of carotid body tumours. Methods Head and neck CTA images of 36 patients with carotid body tumours confirmed by pathology were retrospectively analyzed.Patients were divided into two groups based on the intraoperative bleeding volume:<500 ml and≥500 ml groups.The patient's age,sex,Shamblin classification,size of the lesion,number of blood supply arteries,course of the disease,plain scan,and enhanced CT value between two groups were compared and analyzed.Logistics regression equation was established based on the CTA parameters with significant differences between the two intraoperative bleeding volume groups,and combined parameter was acquired.The receiver operating characteristic curve was established based on CTA single and combined parameters. Results The bleeding volume during the operation of carotid body tumors was significantly correlated with the age of patients(P=0.019),the maximum diameter of tumours on axial images(P=0.003),the maximum upper and lower diameters(P=0.004),Shamblin classification(P=0.012),and number of blood supply arteries(P<0.001).The area under the receiver operating characteristic curve of the number of feeding arteries,the maximum diameter of axial images,maximum upper and lower diameters,Shamblin classification,and combined parameters were 0.865,0.781,0.806,0.766,and 0.927,respectively.When the optimal critical value was 0.408,the Youden index was 0.794,and the corresponding accuracy,sensitivity,and specificity were 0.919,0.909,and 0.923,respectively. Conclusions Preoperative head and neck CTA can be used to evaluate the intraoperative blood loss.Combined parameters has the best diagnostic performance compared with single parameters.

Limonin inhibits the stemness of cancer stem-like cells derived from colorectal carcinoma cells potentially via blocking STAT3 signaling.

BACKGROUND: Limonin is one of the most abundant active ingredients of Tetradium ruticarpum. It exerts antitumor effects on several kinds of cancer cells. However, whether limonin exerts antitumor effects on colorectal cancer (CRC) cells and cancer stem-like cells (CSCs), a subpopulation responsible for a poor prognosis, is unclear. AIM: To evaluate the effects of limonin on CSCs derived from CRC cells. METHODS: CSCs were collected by culturing CRC cells in serum-free medium. The cytotoxicity of limonin against CSCs and parental cells (PCs) was determined by cholecystokinin octapeptide-8 assay. The effects of limonin on stemness were detected by measuring stemness hallmarks and sphere formation ability. RESULTS: As expected, limonin exerted inhibitory effects on CRC cell behaviors, including cell proliferation, migration, invasion, colony formation and tumor formation in soft agar. A relatively low concentration of limonin decreased the expression stemness hallmarks, including Nanog and ß-catenin, the proportion of aldehyde dehydrogenase 1-positive CSCs, and the sphere formation rate, indicating that limonin inhibits stemness without presenting cytotoxicity. Additionally, limonin treatment inhibited invasion and tumor formation in soft agar and in nude mice. Moreover, limonin treatment significantly inhibited the phosphorylation of STAT3 at Y705 but not S727 and did not affect total STAT3 expression. Inhibition of Nanog and ß-catenin expression and sphere formation by limonin was obviously reversed by pretreatment with 2 µmol/L colievlin. CONCLUSION: Taken together, these results indicate that limonin is a promising compound that targets CSCs and could be used to combat CRC recurrence and metastasis.

CD36: an emerging therapeutic target for cancer and its molecular mechanisms.

Tumor cells need to rewire their metabolic pathways to regulate the nutrient uptake and metabolism to sustain the energy production. Lipids are important components of energy sources for tumor metabolism. Tumor cells rely on various transporters to mediate the trafficking of lipids for oxidation or activate oncogenic signaling pathways. CD36, a membrane glycoprotein presenting on the surface of cells, binds fatty acids to facilitate their transport for lipid utilization. Upregulated CD36 expression has been observed in multiple cancer types including acute myeloid leukemia, breast cancer, colorectal cancer, gastric cancer, etc. Moreover, CD36 is correlated with poor clinical outcomes and adverse clinicopathological features in various cancer types. In vitro and vivo studies have confirmed that CD36 participates in the regulation of tumor growth, metastasis, drug resistance through diverse molecular mechanisms. Thus, we firstly discussed the role of CD36 in the regulation of metabolic phenotypes, especially in glucose and fatty acid metabolism. Furthermore, we specifically focused on the molecular mechanisms of CD36 in the occurrence and development of multiple tumor types. Collectively, we explored the connection between CD36 and tumors, providing new insights for developing potential therapeutic strategies and tumor stratification targeting CD36.

Survival stratification for colorectal cancer via multi-omics integration using an autoencoder-based model.

Prognosis stratification in colorectal cancer helps to address cancer heterogeneity and contributes to the improvement of tailored treatments for colorectal cancer patients. In this study, an autoencoder-based model was implemented to predict the prognosis of colorectal cancer via the integration of multi-omics data. DNA methylation, RNA-seq, and miRNA-seq data from The Cancer Genome Atlas (TCGA) database were integrated as input for the autoencoder, and 175 transformed features were produced. The survival-related features were used to cluster the samples using k-means clustering. The autoencoder-based strategy was compared to the principal component analysis (PCA)-, t-distributed random neighbor embedded (t-SNE)-, non-negative matrix factorization (NMF)-, or individual Cox proportional hazards (Cox-PH)-based strategies. Using the 175 transformed features, tumor samples were clustered into two groups (G1 and G2) with significantly different survival rates. The autoencoder-based strategy performed better at identifying survival-related features than the other transformation strategies. Further, the two survival groups were robustly validated using "hold-out" validation and five validation cohorts. Gene expression profiles, miRNA profiles, DNA methylation, and signaling pathway profiles varied from the poor prognosis group (G2) to the good prognosis group (G1). miRNA-mRNA networks were constructed using six differentially expressed miRNAs (let-7c, mir-34c, mir-133b, let-7e, mir-144, and mir-106a) and 19 predicted target genes. The autoencoder-based computational framework could distinguish good prognosis samples from bad prognosis samples and facilitate a better understanding of the molecular biology of colorectal cancer.