Significance: The poor prognosis of epithelial ovarian cancer (EOC) has not improved for several decades because of drug resistance to current anticancer drugs. Furthermore, few molecularly targeted agents are effective for EOC, likely because EOC has high tumor heterogeneity. Discovering new drug targets and mechanisms involved in the progression of EOC is therefore sorely needed. Our multiple CRISPR and RNAi-based in vivo loss-of-function screens have identified multiple new EOC candidate drug targets, including the druggable oncogene KPNB1, whose inhibition caused multiphased cell cycle arrest and induced apoptosis. Ivermectin, a Food and Drug Administration-approved antiparasitic drug, exerts KPNB1-dependent antitumor effects and synergistically inhibits tumor growth in combination with paclitaxel, and therefore represents a new potential combinatorial therapy for EOC through drug repositioning.

Pancreatic cancer is one of the most lethal tumors, and reliable detection of early-stage pancreatic cancer and risk diseases for pancreatic cancer is essential to improve the prognosis. As 260 genes were previously reported to be upregulated in invasive ductal adenocarcinoma of pancreas (IDACP) cells, quantification of the corresponding proteins in plasma might be useful for IDACP diagnosis. Therefore, the purpose of the present study was to identify plasma biomarkers for early detection of IDACP by using two proteomics strategies: antibody-based proteomics and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics. Among the 260 genes, we focused on 130 encoded proteins with known function for which antibodies were available. Twenty-three proteins showed values of the area under the curve (AUC) of more than 0.8 in receiver operating characteristic (ROC) analysis of reverse-phase protein array (RPPA) data of IDACP patients compared with healthy controls, and these proteins were selected as biomarker candidates. We then used our high-throughput selected reaction monitoring or multiple reaction monitoring (SRM/MRM) methodology, together with an automated sample preparation system, micro LC and auto analysis system, to quantify these candidate proteins in plasma from healthy controls and IDACP patients on a large scale. The results revealed that insulin-like growth factor-binding protein (IGFBP)2 and IGFBP3 have the ability to discriminate IDACP patients at an early stage from healthy controls, and IGFBP2 appeared to be increased in risk diseases of pancreatic malignancy, such as intraductal papillary mucinous neoplasms (IPMNs). Furthermore, diagnosis of IDACP using the combination of carbohydrate antigen 19–9 (CA19-9), IGFBP2 and IGFBP3 is significantly more effective than CA19-9 alone. This suggests that IGFBP2 and IGFBP3 may serve as compensatory biomarkers for CA19-9. Early diagnosis with this marker combination may improve the prognosis of IDACP patients.

To investigate the relationships between the expression of MUC5B and clinicopathological parameters, the expression of MUC5B was immunohistochemically studied. MUC5B expression was observed in 129 of 198 (65.2%) adenocarcinomas and in 4 of 49 (8.2%) squamous cell carcinomas (P < 0.00001). MUC5B expression was significantly associated with poorer differentiation (P = 0.0303), higher pathological TNM stage (p = 0.0153) and poorer prognosis of adenocarcinoma patients (P = 0.0017). Multivariable analysis with Cox proportional hazards models confirmed that MUC5B expression increased the hazard of death after adjusting for other clinicopathological factors (HR = 2.66; 95%CI, 1.26–5.61). We also immunohistochemically evaluated TTF-1 expression and found that the combination of MUC5B with TTF-1 is a useful marker for adenocarcinomas. The diagnostic accuracies of TTF-1 and MUC5B for adenocarcinoma were 83.8% and 70.4%, respectively. The accuracy increased to 94.3% when the two factors were combined. In survival analysis, the MUC5B(High)/TTF-1(−) group was significantly associated with a poorer outcome compared with the MUC5B(Low)/TTF-1(+) group (p < 0.0001). The present study suggested that the combination of MUC5B and TTF-1 expression is useful for discriminating adenocarcinomas from squamous cell carcinomas, yielding prognostic significance in patients with lung adenocarcinoma.

Myosin-9 (MYH9) belongs to the myosin superfamily of actin-binding motor protein. Recently, MYH9 has been thought to be associated with cancer cell migration, invasion, and metastasis. The aims of this study were to immunohistochemically examine MYH9 expression in surgically resected non-small cell lung cancer (NSCLC), and evaluate its correlations with clinicopathological parameters and the prognosis of patients. MYH9 expression was immunohistochemically studied in 266 consecutive resected NSCLCs, and its associations with clinicopathological parameters were evaluated. Kaplan-Meier survival analysis and Cox proportional hazards models were used to estimate the effect of MYH9 expression on survival. MYH9 expression was detected in 102 of 266 (38.3%) NSCLCs. MYH9 expression was significantly correlated with the adenocarcinoma histology (P = 0.014), poorer differentiation ((P = 0.033), intratumoral vascular invasion and lymphatic invasion ((P = 0.013 and P = 0.045 respectively), and a poorer prognosis ((P = 0.032). In addition, multivariable analysis revealed that MYH9 expression independently predicted a poorer survival (HR, 2.15; 95%CI, 1.17-3.92; (P = 0.01). The present study revealed that MYH9 is expressed in a subset of NSCLC with a more malignant nature, and its expression is an indicator of a poorer survival probability.

To improve prognosis of pancreatic cancer (PC) patients, the discovery of more reliable biomarkers for the early detection is desired. Blood samples were collected by 2 independent groups. The 1st set was included 55 early PC and 58 healthy volunteers (HV), and the 2nd set was included 16 PC and 16HV. The 16 targeted metabolites were quantitatively analyzed by gas chromatography/tandem mass spectrometry together with their corresponding stable isotopes. In the 1st set, the levels of these metabolites were evaluated, and diagnostic models were constructed via multivariate logistic regression analysis, leading to validation using the 2nd set. In the 1st set, model X consisting of 4 candidates based on our previous report possessed higher sensitivity (74.1%) than carbohydrate antigen 19-9 (CA19-9). Model Y, consisting of 2 metabolites newly selected from 16 metabolites via stepwise method possessed higher sensitivity (70.4%) than CA19-9. Furthermore, combining model Y with CA19-9 increased its sensitivity (90.7%) and specificity (89.5%). In the 2nd set, combining model Y with CA19-9 displayed high sensitivity (81.3%) and specificity (93.8%). In particular, it displayed very high sensitivity (100%) for resectable PC. Quantitative analysis confirmed that metabolomics-based diagnostic methods are useful for detecting PC early.