Circulating tumor cells (CTCs) provide a new approach for auxiliary diagnosis, therapeutic effect evaluation, and prognosis prediction for cancer patients. The epithelial cell adhesion molecule (EpCAM)-based separation method (CellSearch) showed good clinical use in multiple types of cancer. Nevertheless, some non-small cell lung cancer (NSCLC) tumor cells have a lower expression of EpCAM and are less frequently detected by CellSearch. Here, we present a highly sensitive immunomagnetic separation method to capture CTCs based on two cell surface markers for NSCLC, EpCAM and Folate receptor alpha (FRα). Our method has been demonstrated to be more efficient in capturing NSCLC cells (P < 0.01) by enriching three types of CTCs: EpCAM+/FRα−/low, EpCAM−/low/FRα+, and EPCAM+/FRα+. In 41 NSCLC patients, a significantly higher CTC capture rate (48.78% vs. 73.17%) was obtained, and by using a cutoff value of 0 CTC per 2 ml of blood, the sensitivities were 53.66% and 75.61% and the specificities were 100% and 90% for anti-EpCAM-MNs or a combination of anti-EpCAM-MNs and anti-FRα-MNs, respectively. Compared with the tumor-specific LT-PCR based on FRα, our method can isolate intact FRα+ CTCs, and it is advantageous for additional CTC-related downstream analysis. Our results provide a new method to increase the CTC capture efficiency of NSCLC.

Pancreatic cancer is known as “the king of cancer,” and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.

Purpose: Circulating tumor cell (CTC) detection methods based on epithelial cell adhesion molecule (EpCAM) have low detection rates in epithelial ovarian cancer (EOC). Meanwhile, folate receptor alpha (FRα) has high expression in EOC cells. We explored the feasibility of combining FRα and EpCAM as CTC capture targets in EOC. Patients and methods: EpCAM and FRα antibodies were linked to magnetic nanospheres (MNs) using the principle of carbodiimide chemistry. Blood samples from healthy donor spiked with A2780 ovarian cancer cells were used for detecting the capture rate. Ninety-five blood samples from 30 patients with EOC were used for comparing the positive rate of detection when using anti-EpCAM-MNs alone with that when using combination of anti-EpCAM-MNs and anti-FRα-MNs. Samples from 28 patients initially diagnosed with EOC and 20 patients with ovarian benign disease were used for evaluating the sensitivity and specificity of combination of anti-EpCAM-MNs and anti-FRα-MNs. Results: Regression analysis between the number of recovered and that of spiked A2780 cells revealed y EpCAM = 0.535x (R 2 = 0.99), y FRα = 0.901x (R 2 = 0.99), and y EpCAM+FRα = 0.928x (R 2 = 0.99). In mixtures of A2780 and MCF7 cells, the capture rate was 92% using the combination of anti-EpCAM-MNs and anti-FRα-MNs, exceeding the rate when using anti-EpCAM-MNs or anti-FRα-MNs alone by approximately 20% (P < 0.01). The combination of anti-EpCAM-MNs and anti-FRα-MNs showed a significantly increased positive rate of CTC detection in EOC patients compared with anti-EpCAM-MNs alone (χ 2 = 14.45, P < 0.001). Sensitivity values were 0.536 and 0.75 and specificity values were 0.9 and 0.85 when using anti-EpCAM-MNs alone and when using the combination of anti-EpCAM-MNs and anti-FRα-MNs, respectively. Conclusion: The combination of FRα and EpCAM is feasible as a CTC capture target of CTC detection in patients with EOC.

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Background: Melanoma is the third most common skin malignant tumor in the clinic, with high morbidity and mortality. Autophagy plays an important role in the development and progression of melanoma. We aimed to establish an autophagy-related genes(ARGs) expression based risk model for individualized prognosis prediction in patients with melanoma.Methods: Differentially expressed autophagy-related genes (DEARGs) in melanoma and normal skin samples were screened using TCGA and GTEx database. These DEARGs were used to perform KEGG functional enrichment analysis and GO analysis. Univariate and multivariate Cox regression analyses were performed on DEARGs to identify the optimal prognosis-related genes. These prognosis-related DEARGs were used to construct a risk score model, and the predictive effect of this risk model on the prognosis of melanoma patients was tested by the Kaplan-Meier curve, log-rank test, and ROC curve. Method of univariate and multivariate analysis were used to confirmed that the risk model of independent predictive value relative to other clinical variables, and build a nomogram based on the independent prognostic factors in the univariate analysis to predict overall survival(OS) in patients with melanoma, we used internal validation and calculation of concordance index (C-index) to test prediction effect of the nomogram. We also used the t-test to analyze the relationship between risk factors (risk genes and risk score) and clinical variables in the risk model.Results: We screened and finally obtained 6 optimal DEARGs (risk gene) through univariate and multivariate Cox analysis to construct the risk model: EIF2AK2(HR=0.403, P=0.007), IFNG(HR=0.659, P=0.003), DAPK2(HR=0.441, P=0.022), PTK6(HR=1.609, P=6.04E-05), BIRC5(HR=2.479, P=0.001), and EGFR(HR=1.474, P=0.004) were selected to establish the prognostic risk score model and validated in the entire melanoma cohort. The results of GO enrichment analysis showed that the gene function of the DEARGs was concentrated in the functions of gland morphogenesis, protein insertion into membrane, and autophagy. The results of KEGG enrichment analysis showed that the function of the DEARGs was concentrated in the autophagy–animal, p53 signaling pathway, and platinum drug resistance. Kaplan-Meier survival analysis demonstrated that patients with high risk scores had significantly poorer overall survival (OS, log-rank P=6.402E−11). The model was identified as an independent prognostic factor. Finally, a prognostic nomogram including the risk model, T-stage, N-stage, and radiotherapy was constructed, and the calibration plots indicated its excellent predictive performance.Conclusion: The autophagy-related six-gene risk score model could be a prognostic biomarker and suggest therapeutic targets for melanoma. The prognostic nomogram could help individualized survival prediction and improve treatment strategies.

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