Four and a half LIM domains 2 (FHL2) attenuates tumorigenesis of gastrointestinal stromal tumors (GISTs) by negatively regulating KIT signaling.

Gastrointestinal stromal tumors (GISTs) are predominately induced by KIT mutants. In this study, we found that four and a half LIM domains 2 (FHL2) was highly expressed in GISTs and KIT signaling dramatically increased FHL2 transcription while FHL2 inhibited KIT transcription. In addition, our results showed that FHL2 associated with KIT and increased the ubiquitination of both wild-type KIT and primary KIT mutants in GISTs, leading to decreased expression and activation of KIT although primary KIT mutants were less inhibited by FHL2 than wild-type KIT. In the animal experiments, loss of FHL2 expression in mice carrying germline KIT/V558A mutation which can develop GISTs resulted in increased tumor growth, but increased sensitivity of GISTs to imatinib treatment which is used as the first-line targeted therapy of GISTs, suggesting that FHL2 plays a role in the response of GISTs to KIT inhibitor. Unlike wild-type KIT and primary KIT mutants, we further found that FHL2 didn't alter the expression and activation of drug-resistant secondary KIT mutants. Taken together, our results indicated that FHL2 acts as the negative feedback of KIT signaling in GISTs while primary KIT mutants are less sensitive and secondary KIT mutants are resistant to the inhibition of FHL2.

Harnessing artificial intelligence for prostate cancer management.

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

Automated pancreatic segmentation and fat fraction evaluation based on a self-supervised transfer learning network.

Accurate segmentation of the pancreas from abdominal computed tomography (CT) images is challenging but essential for the diagnosis and treatment of pancreatic disorders such as tumours and diabetes. In this study, a dataset with 229 sets of high-resolution CT images was generated and annotated. We proposed a novel 3D segmentation model named nnTransfer (nonisomorphic transfer learning) net, which employs generative model structure for self-supervision to facilitate the network's learning of image attributes from unlabelled data. The effectiveness for pancreas segmentation of nnTransfer was assessed using the Hausdorff distance (HD) and Dice similarity coefficient (DSC) on the dataset. Additionally, a histogram analysis with local thresholding was used to achieve automated whole-volume measurement of pancreatic fat (fat volume fraction, FVF). The proposed technique performed admirably on the dataset, with DSC: 0.937 ± 0.019 and HD: 2.655 ± 1.479. The mean pancreas volume and FVF of the pancreas were 91.95 ± 23.90 cm3 and 12.67 % ± 9.84 %, respectively. The nnTransfer functioned flawlessly and autonomously, facilitating the use of the FVF to evaluate pancreatic disease, particularly in patients with diabetes.

Integrative transcriptome and proteome analyses of clear cell renal cell carcinoma develop a prognostic classifier associated with thrombus.

Clear cell renal cell carcinoma (ccRCC) with venous tumor thrombus (VTT) is associated with poor prognosis. Our integrative analyses of transcriptome and proteome reveal distinctive molecular features of ccRCC with VTT, and yield the development of a prognostic classifier to facilitate ccRCC molecular subtyping and treatment. The RNA sequencing and mass spectrometry were performed in normal-tumor-thrombus tissue triples of five ccRCC patients. Statistical analysis, GO and KEGG enrichment analysis, and protein-protein interaction network construction were used to interpret the transcriptomic and proteomic data. A six-gene-based classifier was developed to predict patients' survival using Cox regression, which was validated in an independent cohort. Transcriptomic analysis identified 1131 tumorigenesis-associated differentially expressed genes (DEGs) and 856 invasion-associated DEGs. Overexpression of transcription factor EGR2 in VTT indicated its important role in tumor invasion. Furthermore, proteomic analysis showed 597 tumorigenesis-associated differentially expressed proteins (DEPs) and 452 invasion-associated DEPs. The invasion-associated DEPs showed unique enrichment in DNA replication, lysine degradation, and PPAR signaling pathway. Integration of transcriptome and proteome reveals 142 tumorigenesis-associated proteins and 84 invasion-associated proteins displaying changes consistent with corresponding genes in transcriptomic profiling. Based on their different expression patterns among normal-tumor-thrombus triples, RAB25 and GGT5 were supposed to play a consistent role in both tumorigenesis and invasion processes, while SHMT2 and CADM4 might play the opposite roles in tumorigenesis and thrombus invasion. A prognostic classifier consisting of six DEGs (DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5, SUSD2) performed satisfactorily in predicting survival of ccRCC patients (HR = 4.41, P < 0.001), which was further validated in an independent cohort of 40 cases (HR = 5.52, P = 0.026). Our study revealed the transcriptomic and proteomic profiles of ccRCC patients with VTT, and identified the distinctive molecular features associated with VTT. The six-gene-based prognostic classifier developed by integrative analyses may facilitate ccRCC molecular subtyping and treatment.

[Prediction of epitope region and preparation of mouse polyclonal antibody of human Shisa-like protein 1(SHISAL1)].

Objective To investigate antigen optimization of Shisa like protein 1 (SHISAL1) for preparing mouse anti-human SHISAL1 polyclonal antibody and to identify the specificity of the prepared antibody. Methods Bioinformatics was employed to predict the antigenic epitope region of SHISAL1 protein, and then a polypeptide composed of amino acid residues from the site of 28 to 97 of SHISAL1, termed SHISAL1-N, was selected as the antigen. The coding region of SHISAL1-N was cloned by molecular cloning technique, and then it was inserted into pET-28a to generate pET28a-SHISAL1-N recombinant plasmid. The two recombinant plasmids pET28a-SHISAL1-N and pET28a-SHISAL1 were transformed into BL21 (DE3) bacteria and induced to express by IPTG. The two proteins were purified and immunized to female Kunming mice, respectively. The specificities and sensitivities of the acquired antibodies were detected by Western blot analysis, immunoprecipitation and immunofluorescent cytochemical staining. Results pET28a-SHISAL1-N recombinant plasmid was successfully constructed, and the two fused proteins, SHISAL1 and SHISAL1-N, were induced to express. Moreover, two types of SHISAL1 mouse polyclonal antibodies, derived from SHISAL1-N and SHISAL1 antigens, were obtained. Western blot results showed that the antibody prepared from SHISAL1 antigen was less specific and sensitive compared with the antibody prepared from SHISAL1-N antigen which could specifically identify different endogenous SHISAL1 protein. Immunoprecipitation results showed that SHISAL1-N antibody could specifically pull down SHIISAL1 protein in hepatocellular carcinoma cells and immunofluorescence results demonstrated that SHISAL1-N antibody could specifically bind to SHISAL1 protein in the cytoplasm. Conclusion We have optimized the SHISAL1 antigen and prepared the mouse anti-human SHISAL1 polyclonal antibodies successfully, which can be used for Western blot analysis, immunoprecipitation and immunofluorescence cytochemical staining.

Tumor size measurements of pancreatic cancer with neoadjuvant therapy based on RECIST guidelines: is MRI as effective as CT?

OBJECTIVES: To compare tumor size measurements using CT and MRI in pancreatic cancer (PC) patients with neoadjuvant therapy (NAT). METHODS: This study included 125 histologically confirmed PC patients who underwent NAT. The tumor sizes from CT and MRI before and after NAT were compared by using Bland-Altman analyses and intraclass correlation coefficients (ICCs). Variations in tumor size estimates between MRI and CT in relationship to different factors, including NAT methods (chemotherapy, chemoradiotherapy), tumor locations (head/neck, body/tail), tumor regression grade (TRG) levels (0-2, 3), N stages (N0, N1/N2) and tumor resection margin status (R0, R1), were further analysed. The McNemar test was used to compare the efficacy of NAT evaluations based on the CT and MRI measurements according to RECIST 1.1 criteria. RESULTS: There was no significant difference between the median tumor sizes from CT and MRI before and after NAT (P = 0.44 and 0.39, respectively). There was excellent agreement in tumor size between MRI and CT, with mean size differences and limits of agreement (LOAs) of 1.5 [-9.6 to 12.7] mm and 0.9 [-12.6 to 14.5] mm before NAT (ICC, 0.93) and after NAT (ICC, 0.91), respectively. For all the investigated factors, there was good or excellent correlation (ICC, 0.76 to 0.95) for tumor sizes between CT and MRI. There was no significant difference in the efficacy evaluation of NAT between CT and MRI measurements (P = 1.0). CONCLUSION: MRI and CT have similar performance in assessing PC tumor size before and after NAT.

A morphology-based nephrometry score to predict pathological upstaging to T3 renal cell carcinoma.

Background: Patients with clinical T1-2 renal cell carcinoma (RCC) upstaging to pathological T3 showed worse survival prognosis than those without upstaging. We aimed to develop and validate a morphology-based nephrometry scoring system for predicting pathological upstaging to T3 of RCC. Methods: We retrospectively reviewed 200 patients with clinical T1-2 RCC who underwent surgical treatment. The nephrometry scores were measured through preoperative computed tomography images. The risk factors of pathological upstaging were identified by logistic regression models. The predictive accuracy of a novel morphology-based nephrometry scoring system (M-Index), was compared with R.E.N.A.L (radius, exophytic/endophytic, nearness, anterior/posterior, location), PADUA (preoperative aspects and dimensions used for an anatomic classification), DAP (diameter, axial, polar) and C-Index scores. Results: The upstaging rate of the population was 17% (34 out of 200 patients). The upstaging and non-upstaging groups were comparable in terms of age, gender ratio, body mass index, tumor laterality, and pathological type, while the upstaging group tended to have large tumor diameter, irregular tumor morphology, inner tumor location, and short polar and axial distance. Large tumor diameter refers to larger than 5 cm, while irregular tumor morphology refers to not regular shapes such as round, oval, or lobular. Univariate and multivariate logistic regression analyses showed that tumor morphology [odds ratio (OR) 3.26, 95% confidence interval (CI): 1.79-5.97] and tumor rim location (OR 2.95, 95% CI: 1.16-7.46) were independent risk factors for pathological upstaging. The receiver operating characteristic curve and decision curve analysis (DCA) demonstrated the novel M-Index based on tumor morphology and rim location outperformed R.E.N.A.L, PADUA, DAP, and C-Index in the prediction of pathological upstaging (area under curve 0.756 vs. 0.728 vs. 0.641 vs. 0.661 vs. 0.743). Conclusions: Consisting of fewer non-complex parameters, the M-Index is an intuitive and practical tool with satisfactory predictive power for pathological upstaging to T3 in RCC patients undergoing surgery.

Sites of Distant Metastases and Cancer-Specific Survival in Intraductal Papillary Mucinous Neoplasm With Associated Invasive Carcinoma: A Study of 1,178 Patients.

BACKGROUND: To explore the impact of distant metastases on cancer-specific survival in patients with intraductal papillary mucinous neoplasm (IPMN) with associated invasive carcinoma and identify the risk factor of distant metastases in IPMN with associated invasive carcinoma. METHODS: Patients with IPMN with associated invasive carcinoma between 2010 and 2015 were retrospectively selected from the Surveillance, Epidemiology, and End Results (SEER) database. The survival analyses were assessed by Kaplan-Meier analyses and log-rank test. The impact of distant metastases was evaluated by Cox regression model and the risk factors of distant metastases were identified by logistic regression analyses, respectively. RESULTS: The median cancer-specific survival time of patients with no metastases, isolated liver, isolated lung, and multiple site metastases were 19 months, 4 months, 7 months, and 3 months, respectively. In patients with isolated liver metastases, multivariate analysis after adjustment indicated that chemotherapy (Hazard Ratio [HR]=0.351, 95% confidence interval [CI]=0.256-0.481, P<0.001) was a protective prognostic factor for cancer-specific survival (CSS) in patients with isolated liver metastases. In isolated lung metastases subgroup, old age (HR=1.715, 95% CI=1.037-2.838, P=0.036) and chemotherapy (HR=0.242, 95% CI=0.134-0.435, P<0.001) were related to CSS in multivariable Cox regression analysis(P<0.05). Tumor located in the pancreatic body/tail (HR=2.239, 95% CI=1.140-4.400, P=0.019) and chemotherapy (HR=0.191, 95% CI=0.108-0.340, P<0.001) were independent prognostic factors for CSS in patients with multiple metastases. Finally, a nomogram was constructed for cancer-specific survival and the predicted C-index was 0.780 (95% CI=0.762-0.798). CONCLUSION: The liver is the most common site of distant metastases in IPMN with associated invasive carcinoma. Tumor located in the pancreatic body/tail and chemotherapy are independent prognostic factors for CSS in patients with multiple metastases. Further, tumor located in body/tail is identified as a risk factor of distant metastases.

miRNA-1290 Promotes Aggressiveness in Pancreatic Ductal Adenocarcinoma by Targeting IKK1.

BACKGROUND/AIMS: MicroRNAs (miRNAs) are a group of non-coding RNAs that play diverse roles in pancreatic carcinogenesis. In pancreatic ductal adenocarcinoma (PDAC), NF-kB is constitutively activated in most patients and is linked to a mutation in KRAS via IkB kinase complex 1 (IKK1, also known as IKKa). We investigated the link between PDAC aggressiveness and miR-1290. METHODS: We used miRCURYTM LNA Array and in situ hybridization to investigate candidate miRNAs and validated the findings with PCR. The malignant behavior of cell lines was assessed with Cell Counting Kit-8, colony formation, and Transwell assays. A dual-luciferase reporter assay was used to evaluate the interaction between miR-1290 and IKK1. Protein expression was observed by western blotting. RESULTS: In this study, 36 miRNAs were dysregulated in high-grade pancreatic intraepithelial neoplasia (PanIN) and PDAC tissues compared with low-grade PanIN tissues. The area under the curve values of miR-1290 and miR-31-5p were 0.829 and 0.848, respectively (95% confidence interval, 0.722-0.936 and 0.749-0.948, both P < 0.001). There was a significant correlation between miR-1290 and histological differentiation (P = 0.029), pT stage (P = 0.006), and lymph node metastasis (P = 0.001). In addition, the in vitro work showed that miR-1290 promoted PDAC cell proliferation, invasion, and migration. Western blotting and the dual-luciferase reporter assay showed that miR-1290 promoted cancer aggressiveness by directly targeting IKK1. The synergist effect of miR-1290 on the proliferation and metastasis of PDAC cells was attenuated and enhanced by IKK1 overexpression and knockdown, respectively. Consistent with the in vitro results, a subcutaneous tumor mouse model showed that miR-1290 functioned as a potent promoter of PDAC in vivo. CONCLUSION: MiR-1290 may act as an oncogene by directly targeting the 3'-untranslated region of IKK1, and the miR-1290/IKK1 pathway may prove to be a novel diagnostic and therapeutic target for PDAC.

MEG3 Activated by Vitamin D Inhibits Colorectal Cancer Cells Proliferation and Migration via Regulating Clusterin.

The long non-coding RNA maternally expressed gene 3 (MEG3) is frequently dysregulated in human cancers; however, its roles in colorectal cancer (CRC) development are largely unknown. Here, we reported that MEG3 was down-regulated in CRC tissues and CRC patients with lower MEG3 showed poorer overall survival and disease-free survival than those with higher MEG3 level. MEG3 over-expression represses CRC cells proliferation and migration in vivo and in vitro, while MEG3 knockdown leads to the enhanced proliferation and metastasis of CRC cells. In CRC cells, MEG3 over-expression is related to decreased Clusterin mRNA and the corresponding protein levels, and it also directly binds to Clusterin protein through its 732-1174 region. In further, Clusterin over-expression rescues the compromised abilities of proliferation and metastasis induced by MEG3 over-expression, suggesting that MEG3 inhibits the CRC progression through regulating the Clusterin activities. Additionally, we found that 1α,25-(OH)2D and vitamin D receptor (VDR) stimulate MEG3 expression in CRC cells through directly binding to its promoter. These results suggested that MEG3 functions as a tumor suppressor in CRC via regulating the Clusterin activities and may underlie the anticancer activities of vitamin D on CRC cells. The VDR/MEG3/Clusterin signaling pathway may serve as potential therapeutic targets and prognosis biomarkers for CRC patients in future.

Microarray Analysis of the Expression Profile of Long Non-Coding RNAs Indicates lncRNA RP11-263F15.1 as a Biomarker for Diagnosis and Prognostic Prediction of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy with poor prognostic outcomes. Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) play an important role in the development and progression of carcinogenesis. Nevertheless, little is known about the role of lncRNAs in PDAC. The aim of the current study was to find differentially expressed lncRNAs and related mRNAs in human PDAC tissues and adjacent normal tissues by microarray analysis, and investigate the relationship between lncRNA RP11-263F15.1 levels and the clinicaopathological features of PDAC patients. It was found that 4364 lncRNAs and 4862 related mRNAs were significantly dysregulated in PDAC tissues as compared with adjacent normal tissues with a fold change ≥2.0 (P<0.05). GO and pathway analyses showed that the up-regulated gene profiles were related to several pathways associated with carcinogenesis, while the down-regulated gene profiles were closely correlated with nutrient metabolism. RP11-263F15.1 levels were associated with histologic differentiation (P=0.001). Besides, Kaplan-Meier analysis showed that high expression of RP11-263F15.1 was associated with poor outcomes, but multivariate analysis suggested that RP11-263F15.1 was not an independent factor for predicting prognosis of PDAC. In conclusion, these data indicate that differentially expressed lncRNAs and mRNAs were involved in the carcinogenesis of PDAC, and RP11-263F15.1 may prove to be a potential biomarker for the diagnosis and prognostic prediction of PDAC.

Association between vitamin A, retinol and carotenoid intake and pancreatic cancer risk: Evidence from epidemiologic studies.

Pancreatic cancer is a devastating disease with poor prognosis. The association between vitamin A, retinol and carotenoid intake and the risk of pancreatic cancer occurrence remains controversial, and therefore it is necessary to make a meta-analysis to clarify the association between vitamin A, retinol and carotenoid intake and pancreatic cancer risk. In the present study, PubMed and EMBASE databases were used to identify qualified studies. The association between dietary vitamin A, retinol and carotenoids was estimated by pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs). It was found that there was an inverse correlation between vitamin A, beta-carotene and lycopene intake and the risk of pancreatic cancer (for vitamin A, pooled OR = 0.85, 95%CI = 0.74-0.97, P = 0.015; for beta-carotene, pooled OR = 0.78, 95%CI = 0.66-0.92, P = 0.003; for lycopene, pooled OR = 0.84, 95%CI = 0.73-0.97, P = 0.020), which was more prominent in case-control study subgroup. In conclusion, dietary vitamin A, beta-carotene and lycopene might inversely correlate with pancreatic cancer.

LncRNAs in pancreatic cancer.

Pancreatic cancer (PC) is one of the most common causes of cancer-related death. The underlying mechanism of PC is not completely understood at present. Studies in recent years have demonstrated that long non-coding RNAs (lncRNAs) have multiple biological functions in cell growth, differentiation and proliferation. Notably, expressions of some lncRNAs undergo significant changes in the initiation and progression of cancers. In addition, lncRNAs are reported to be involved in various steps of PC development and have a potential value in the diagnosis, treatment and prognostic prediction of PC. In this review, we highlight recent evidence related to the molecular mechanism of lncRNAs in growth, survival, invasion, metastasis, angiogenesis and apoptosis of PC cells, and discuss the potential clinical application of lncRNAs to the diagnosis, treatment and prognostic prediction of PC.

The association between RFC1 G80A polymorphism and cancer susceptibility: Evidence from 33 studies.

Aberrant folate metabolism is closely related to tumorigenesis. Genetic variations in the Reduced folate carrier 1 (RFC1) may alter the progress of folate metabolism, and thereby cause the initiation and progress of the cancer. Considerable studies have performed to investigate the association between RFC1 G80A (rs1051266) polymorphism and cancer susceptibility, but the conclusions were conflicting. Therefore, we conducted a meta-analysis to reevaluate the association of RFC1 G80A polymorphism with cancer risk. PubMed and EMBASE were searched for eligible studies. The association of RFC1 G80A polymorphism and cancer risk was evaluated by the pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs). The significant association was found between RFC1 G80A polymorphism and hematological malignance susceptibility (A vs. G: OR=1.11, 95%CI=1.003-1.23, P=0.045; GA vs. GG: OR=1.18, 95%CI=1.06-1.31, P=0.002; AA+GA vs. GG: OR=1.18, 95%CI=1.07-1.29, P=0.001). Stratified analysis by ethnicity indicated that the association became more prominent among Caucasians (GA vs. GG: OR=1.28, 95%CI=1.12-1.45, P<0.001; AA+GA vs. GG: OR=1.21, 95%CI=1.08-1.36, P=0.001). In term of the cancer type, this polymorphism significantly increased the risk of acute lymphoblast leukemia (GA vs. GG: OR=1.13, 95%CI=1.001-1.28, P=0.048; AA+GA vs. GG: OR=1.28, 95%CI=1.13-1.46, P<0.001) and acute myeloid leukemia (GA vs. GG: OR=2.57, 95%CI=1.37-4.85, P=0.003). No significant association between RFC1 G80A polymorphism and overall solid cancer risk was observed, but a protective association with digestive cancer risk was found (GA vs. GG: OR=0.89, 95%CI= 0.81-0.99, P=0.030). The comprehensive meta-analysis encouraged the notion that RFC1 G80A polymorphism may play an important role in hematopoietic system malignance. These findings need further validation in the large multicenter investigations.