Antidepressant exposure and long-term dementia risk in a nationwide retrospective study on US veterans with midlife major depressive disorder.

INTRODUCTION: The use of antidepressants in major depressive disorder (MDD) has been reported to influence long-term risk of Alzheimer's disease (AD) and AD-related dementias (AD/ADRD), but studies are conflicting. METHODS: We used inverse probability weighted (IPW) Cox models with time-varying covariates in a retrospective cohort study among midlife veterans with MDD within the US Veterans Affairs healthcare system from January 1, 2000 to June 1, 2022. RESULTS: A total of 35,200 patients with MDD were identified. No associations were seen regarding the effect of being exposed to any antidepressant versus no exposure on AD/ADRD risk (events = 1,056, hazard ratio = 0.94, 95% confidence interval: 0.81 to 1.09) or the exposure to specific antidepressant classes versus no exposure. A risk reduction was observed for female patients in a stratified analysis; however, the number of cases was small. DISCUSSION: Our study suggests that antidepressant exposure has no effect on AD/ADRD risk. The association in female patients should be interpreted with caution and requires further attention. HIGHLIGHTS: We studied whether antidepressant use was associated with future dementia risk. We specifically focused on patients after their first-ever diagnosis of depression. We used IPW Cox models with time-varying covariates and a large observation window. Our study did not identify an effect of antidepressant use on dementia risk. A risk reduction was observed in female patients, but the number of cases was small.

Self-prediction of relations in GO facilitates its quality auditing.

As applications of the gene ontology (GO) increase rapidly in the biomedical field, quality auditing of it is becoming more and more important. Existing auditing methods are mostly based on rules, observed patterns or hypotheses. In this study, we propose a machine-learning-based framework for GO to audit itself: we first predict the IS-A relations among concepts in GO, then use differences between predicted results and existing relations to uncover potential errors. Specifically, we transfer the taxonomy of GO 2020 January release into a dataset with concept pairs as items and relations between them as labels(pairs with no direct IS-A relation are labeled as ndrs). To fully obtain the representation of each pair, we integrate the embeddings for the concept name, concept definition, as well as concept node in a substring-based topological graph. We divide the dataset into 10 parts, and rotate over all the parts by choosing one part as the testing set and the remaining as the training set each time. After 10 rotations, the prediction model predicted 4,640 existing IS-A pairs as ndrs. In the GO 2022 March release, 340 of these predictions were validated, demonstrating significance with a p-value of 1.60e-46 when compared to the results of randomly selected pairs. On the other hand, the model predicted 2,840 out of 17,079 selected ndrs in GO to be IS-A's relations. After deleting those that caused redundancies and circles, 924 predicted IS-A's relations remained. Among 200 pairs randomly selected, 30 were validated as missing IS-A's by domain experts. In conclusion, this study investigates a novel way of auditing biomedical ontologies by predicting the relations in it, which was shown to be useful for discovering potential errors.

GEMIN4, a potential therapeutic targets for patients with basal-like subtype breast cancer.

BACKGROUND: Basal-like breast cancer (BLBC) takes up about 10-20% of all breast cancer(BC), what's more, BLBC has the lowest survival rate among all BC subtypes because of lacks of efficient treatment methods. We aimed to explore the molecules that can be used as diagnostic maker for BLBC at early stage and provide optimized treatment strategies for BLBC patients in this study. METHODS: Apply weighted gene co-expression network analysis (WGCNA) to identify gene modules related to BLBC;The functional enrichment of candidate genes related to BLBC in the red module of Go data package and KEGG analysis;Overlapping cross analysis of URGs and WGCNA to identify candidate genes in each BC subtype;Divide BCBL patients into high-risk and low-risk groups, and analyze the two groups of overall survival (OS) and relapse free survival (RFS);Screening of GEMIN4 dependent cell lines; QRT PCR was used to verify the expression of GEMIN4 transfected with siRNA; CCK8 was used to determine the effect of GEMIN4 on cell viability; Positive cell count detected by BrdU staining;GO and KEGG enrichment analysis of GEMIN4. RESULTS: The "red module" has the highest correlation with BLBC, with 913 promising candidate genes identified from the red module;913 red module candidate genes related to BLBC participated in multiple GO terms, and KEGG enrichment analysis results mainly enriched in estrogen signaling pathways and pathways in cancer;There are 386 overlapping candidate genes among the 913 "red module" genes identified by 1893 common URG and WGCNA;In BLBC patients, 9 highly expressed genes are associated with OS. Five highly expressed genes are associated with RFS. Kaplan Meier survival analysis suggests that high GEMIN4 expression levels are associated with poor prognosis in BLBC patients;The GEMIN4 gene dependency score in HCC1143 and CAL120 cell lines is negative and low; Si-GEMIN4-1 can significantly reduce the mRNA expression of GEMIN4; Si-GEMIN4 can inhibit cell viability; Si-GEMIN4 can reduce the number of positive cells;GO enrichment analysis showed that GEMIN4 is associated with DNA metabolism processes and adenylate binding; KEGG pathway enrichment analysis shows that GEMIN4 is related to ribosome biogenesis in eukaryotes. CONCLUSION: We hypothesized that GEMIN4 may be the potential target for the treatment of BLBC.

LMAN1-MCFD2 complex is a cargo receptor for the ER-Golgi transport of α1-antitrypsin.

α1-antitrypsin (AAT) is a serine protease inhibitor synthesized in hepatocytes and protects the lung from damage by neutrophil elastase. AAT gene mutations result in AAT deficiency (AATD), which leads to lung and liver diseases. The AAT Z variant forms polymer within the endoplasmic reticulum (ER) of hepatocytes and results in reduction in AAT secretion and severe disease. Previous studies demonstrated a secretion defect of AAT in LMAN1 deficient cells, and mild decreases in AAT levels in male LMAN1 and MCFD2 deficient mice. LMAN1 is a transmembrane lectin that forms a complex with a small soluble protein MCFD2. The LMAN1-MCFD2 protein complex cycles between the ER and the Golgi. Here, we report that LMAN1 and MCFD2 knockout (KO) HepG2 and HEK293T cells display reduced AAT secretion and elevated intracellular AAT levels due to a delayed ER-to-Golgi transport of AAT. Secretion defects in KO cells were rescued by wild-type LMAN1 or MCFD2, but not by mutant proteins. Elimination of the second glycosylation site of AAT abolished LMAN1 dependent secretion. Co-immunoprecipitation experiment in MCFD2 KO cells suggested that AAT interaction with LMAN1 is independent of MCFD2. Furthermore, our results suggest that secretion of the Z variant, both monomers and polymers, is also LMAN1-dependent. Results provide direct evidence supporting that the LMAN1-MCFD2 complex is a cargo receptor for the ER-to-Golgi transport of AAT and that interactions of LMAN1 with an N-glycan of AAT is critical for this process. These results have implications in production of recombinant AAT and in developing treatments for AATD patients.

Analysis of Long Noncoding RNAs-Related Regulatory Mechanisms in Duchenne Muscular Dystrophy Using a Disease-Related lncRNA-mRNA Pathway Network.

Objective: This study aimed to investigate the molecular regulatory mechanisms underpinning Duchenne muscular dystrophy (DMD). Methods: Using microarray data, differentially expressed long noncoding RNAs (DELs) and DMD-related differentially expressed mRNAs (DEMs) were screened based on the comparative toxicogenomics database, using a cutoff of |log2 fold change| > 1 and false discovery rate (FDR) < 0.05. Then, protein-protein interaction (PPI), coexpression network of lncRNA-mRNA, and DMD-related lncRNA-mRNA pathway networks were constructed, and functional analyses of the genes in the network were performed. Finally, the proportions of immune cells infiltrating the muscle tissues in DMD were analyzed, and the correlation between the immune cells and expression of the DELs/DEMs was studied. Results: A total of 46 DELs and 313 DMD-related DEMs were identified. The PPI network revealed STAT1, VEGFA, and CCL2 to be the top three hub genes. The DMD-related lncRNA-mRNA pathway network comprising two pathways, nine DELs, and nine DMD-related DEMs showed that PYCARD, RIPK2, and CASP1 were significantly enriched in the NOD-like receptor signaling pathway, whereas MAP2K2, LUM, RPS6, PDCD4, TWIST1, and HIF1A were significantly enriched with proteoglycans in cancers. The nine DELs in this network were DBET, MBNL1-AS1, MIR29B2CHG, CCDC18-AS1, FAM111A-DT, GAS5, LINC01290, ATP2B1-AS1, and PSMB8-AS1. Conclusion: The nine DMD-related DEMs and DELs identified in this study may play important roles in the occurrence and progression of DMD through the two pathways of the NOD-like receptor signaling pathway and proteoglycans in cancers.

Drug repurposing for opioid use disorders: integration of computational prediction, clinical corroboration, and mechanism of action analyses.

Morbidity and mortality from opioid use disorders (OUD) and other substance use disorders (SUD) is a major public health crisis, yet there are few medications to treat them. There is an urgency to accelerate SUD medication development. We present an integrated drug repurposing strategy that combines computational prediction, clinical corroboration using electronic health records (EHRs) of over 72.9 million patients and mechanisms of action analysis. Among top-ranked repurposed candidate drugs, tramadol, olanzapine, mirtazapine, bupropion, and atomoxetine were associated with increased odds of OUD remission (adjusted odds ratio: 1.51 [1.38-1.66], 1.90 [1.66-2.18], 1.38 [1.31-1.46], 1.37 [1.29-1.46], 1.48 [1.25-1.76], p value < 0.001, respectively). Genetic and functional analyses showed these five candidate drugs directly target multiple OUD-associated genes including BDNF, CYP2D6, OPRD1, OPRK1, OPRM1, HTR1B, POMC, SLC6A4 and OUD-associated pathways, including opioid signaling, G-protein activation, serotonin receptors, and GPCR signaling. In summary, we developed an integrated drug repurposing approach and identified five repurposed candidate drugs that might be of value for treating OUD patients, including those suffering from comorbid conditions.

lncRNA MALAT1 promotes HCC metastasis through the peripheral vascular infiltration via miRNA-613: a primary study using contrast ultrasound.

BACKGROUND: This study aimed to explore the specific pathogenesis of lncRNA MALAT1 promoting the invasion and metastasis of hepatocellular carcinoma (HCC) through peripheral blood vessels by regulating the expression of miRNA-613 molecule. METHODS: The data of 60 HCC metastatic patients and 60 HCC non-metastatic patients detected by the contrast-enhanced ultrasound (CEUS) in the Second Affiliated Hospital of Qiqihar Medical College from January 2020 to June 2021 were collected, as well as postoperatively retained HCC tissues and paired paracancer tissues (5 cm laterally from the edge of the cancer area), to study the changes of microangiogenesis in HCC tissues with CEUS. The correlation between CEUS grading and lncRNA MALAT1 in patients with HCC was analyzed through Pearson correlation analysis, lncRNA MALAT1 and miRNA-613 in HCC tissues of patients with HCC were detected by qRT-PCR, followed by the bioinformatic analysis for the relationship between lncRNA MALAT1 and miRNA-613. The Log-growing human HCC cell strain, HepG2, was selected for experiments. Adenovirus transfection knocked down lncRNA MALAT1 in HCC cells, which was divided into two groups (inhibitor-NC group and lncR-inhibitor group), followed by knocking down miRNA-613 on the basis of knocking down lncRNA MALAT1, which was divided into three groups (inhibitor-NC group, lncR-inhibitor groups, and lncR/miR613-inhibitor group). The expression of miRNA-613 and lncRNA MALAT1 in each group was detected by qRT-PCR. The migration and invasiveness of cells in each group were detected by Transwell assay. RESULTS: CEUS of HCC and Pearson correlation analysis showed that CEUS grading and lncRNA MALAT1 were positively correlated in patients with HCC. In HCC tissues of patients with HCC, lncRNA MALAT1 expressed high and miRNA-613 expressed low. The results of bioinformatic analysis showed the targeting of lncRNA MALAT1 and miRNA-613. Knocking down lncRNA MALAT1 could increase miRNA-613 expression significantly, and reduce the migration of HCC cells. Inhibiting miRNA-613 based on knocking down lncRNA MALAT1 could increase the survival and migration of HCC cells. CONCLUSIONS: lncRNA MALAT1 can promote HCC metastasis through the peripheral vascular infiltration by inhibiting the level of MiRNA-613, which can, therefore, be used as a potential target for the treatment of HCC.

Potential long-term effect of tumor necrosis factor inhibitors on dementia risk: A propensity score matched retrospective cohort study in US veterans.

INTRODUCTION: Tumor necrosis factor (TNF) inhibitors are widely used to treat rheumatoid arthritis (RA) and their potential to retard Alzheimer's disease (AD) progression has been reported. However, their long-term effects on the dementia/AD risk remain unknown. METHODS: A propensity scored matched retrospective cohort study was conducted among 40,207 patients with RA within the US Veterans Affairs health-care system from 2000 to 2020. RESULTS: A total of 2510 patients with RA prescribed TNF inhibitors were 1:2 matched to control patients. TNF inhibitor use was associated with reduced dementia risk (hazard ratio [HR]: 0.64, 95% confidence interval [CI]: 0.52-0.80), which was consistent as the study period increased from 5 to 20 years after RA diagnosis. TNF inhibitor use also showed a long-term effect in reducing the risk of AD (HR: 0.57, 95% CI: 0.39-0.83) during the 20 years of follow-up. CONCLUSION: TNF inhibitor use is associated with lower long-term risk of dementia/AD among US veterans with RA.

Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery.

MOTIVATION: Predicting drug-target interactions (DTIs) using human phenotypic data have the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinical corroboration. RESULTS: We developed a network-based DTI prediction system (TargetPredict) by modeling 855 904 phenotypic and genetic relationships among 1430 drugs, 4251 side effects, 1059 diseases and 17 860 genes. We systematically evaluated TargetPredict in de novo cross-validation and compared it to a state-of-the-art phenome-driven DTI prediction approach. We applied TargetPredict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD), a disease affecting over 5.8 million people in the United States. We evaluated the clinical efficiency of top repositioned drug candidates using EHRs of over 72 million patients. The area under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs. TargetPredict outperformed a state-of-the-art phenome-driven DTI prediction system as measured by precision-recall curves [measured by average precision (MAP): 0.28 versus 0.23, P-value < 0.0001]. The EHR-based case-control studies identified that the prescriptions top-ranked repositioned drugs are significantly associated with lower odds of AD diagnosis. For example, we showed that the prescription of liraglutide, a type 2 diabetes drug, is significantly associated with decreased risk of AD diagnosis [adjusted odds ratios (AORs): 0.76; 95% confidence intervals (CI) (0.70, 0.82), P-value < 0.0001]. In summary, our integrated approach that seamlessly combines computational DTI prediction and large-scale patients' EHRs-based clinical corroboration has high potential in rapidly identifying novel drug targets and drug candidates for complex diseases. AVAILABILITY AND IMPLEMENTATION: nlp.case.edu/public/data/TargetPredict.

Suppressed expression of Cbl-b by NF-κB mediates icotinib resistance in EGFR-mutant non-small-cell lung cancer.

Although epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) could greatly improve the prognosis of NSCLC patients harboring activating EGFR mutations, drug resistance still remains a major obstacle to successful treatment. Our previous study found that the EGFR-TKI icotinib could upregulate the expression of Casitas-B-lineage lymphoma protein-B (Cbl-b), an E3 ubiquitin ligase. In the present study, we aimed to clarify the potential role of Cbl-b in the resistance to icotinib, and the underlying mechanisms using EGFR-mutant cell lines. We found that icotinib inhibited the proliferation of mutant-EGFR NSCLC cells (PC9 and HCC827), and upregulated the expression of Cbl-b at both the protein and mRNA levels. Cbl-b knockdown decreased the sensitivity of PC9 and HCC827 cells to icotinib, and partially restored icotinib-inhibited AKT activation in PC9 cells. On the contrary, Cbl-b overexpression could partly reverse the drug resistance in PC9 icotinib-resistant cells (PC9/IcoR). Moreover, overexpressing p65, the main member of transcription factor NF-κB family, reversed the icotinib-mediated upregulation of Cbl-b. Collectively, these data suggest that icotinib could upregulate Cbl-b mediated by NF-κB inhibition, and Cbl-b contribute to the icotinib sensitivity in EGFR-mutant NSCLC cells. This study highlights that low expression of Cbl-b might be the key obstacles in the efficacy of icotinib therapy.

Analysis of disease organ as a novel phenotype towards disease genetics understanding.

Discerning the modular nature of human diseases through computational approaches calls for diverse data. The finding sites of diseases, like other disease phenotypes, possess rich information in understanding disease genetics. Yet, analysis of the rich knowledge of disease finding sites has not been comprehensively investigated. In this study, we built a large-scale disease organ network (DON) based on 76,561 disease-organ associations (for 37,615 diseases and 3492 organs) extracted from the United Medical Language System (UMLS) Metathesaurus. We investigated how phenotypic organ similarity among diseases in DON reflects disease gene sharing. We constructed a disease genetic network (DGN) using curated disease-gene associations and demonstrated that disease pairs with higher organ similarities not only are more likely to share genes, but also tend to share more genes. Based on community detection algorithm, we showed that phenotypic disease clusters on DON significantly correlated with genetic disease clusters on DGN. We compared DON with a state-of-art disease phenotype network, disease manifestation network (DMN), that we have recently constructed, and demonstrated that DON contains complementary knowledge for disease genetics understanding.

Cancer-associated fibroblasts-stimulated interleukin-11 promotes metastasis of gastric cancer cells mediated by upregulation of MUC1.

Cancer-associated fibroblasts (CAFs) are major components of the tumor stroma and regulators of tumor progression. However, the molecular mechanism by which CAFs promote gastric cancer progression should be further explored. In our study, we found that interleukin-11 (IL-11) secretion was significantly increased when CAFs were co-cultured with gastric cancer cells. Co-culture system-derived IL-11 promoted the migration and invasion of gastric cancer cells, whereas the increase of migration and invasion was attenuated by a neutralizing antibody of IL-11 or inhibition of JAK/STAT3 and MAPK/ERK pathways with specific inhibitors. Taken together, these results revealed that CAFs play a significant role in the gastric cancer progression in the tumor microenvironment through IL-11-STAT3/ERK signaling by upregulating MUC1. Also, IL-11 targeted therapy can achieve an effective treatment against gastric cancer indirectly by exerting their action on stromal fibroblasts.

Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data.

BACKGROUND: Systems approaches in studying disease relationship have wide applications in biomedical discovery, such as disease mechanism understanding and drug discovery. The FDA Adverse Event Reporting System (FAERS) contains rich information about patient diseases, medications, drug adverse events and demographics of 17 million case reports. Here, we explored this data resource to mine disease comorbidity relationships using association rule mining algorithm and constructed a disease comorbidity network. RESULTS: We constructed a disease comorbidity network with 1059 disease nodes and 12,608 edges using association rule mining of FAERS (14,157 rules). We evaluated the performance of comorbidity mining from FAERS using known disease comorbidities of multiple sclerosis (MS), psoriasis and obesity that represent rare, moderate and common disease respectively. Comorbidities of MS, obesity and psoriasis obtained from our network achieved precisions of 58.6%, 73.7%, 56.2% and recalls 87.5%, 69.2% and 72.7% separately. We performed comparative analysis of the disease comorbidity network with disease semantic network, disease genetic network and disease treatment network. We showed that (1) disease comorbidity clusters exhibit significantly higher semantic similarity than random network (0.18 vs 0.10); (2) disease comorbidity clusters share significantly more genes (0.46 vs 0.06); and (3) disease comorbidity clusters share significantly more drugs (0.64 vs 0.17). Finally, we demonstrated that the disease comorbidity network has potential in uncovering novel disease relationships using asthma as a case study. CONCLUSIONS: Our study presented the first comprehensive attempt to build a disease comorbidity network from FDA Adverse Event Reporting System. This network shows well correlated with disease semantic similarity, disease genetics and disease treatment, which has great potential in disease genetics prediction and drug discovery.

Breast cancer and synchronous multiple primary lung adenocarcinomas with heterogeneous mutations: a case report.

BACKGROUND: Multiple primary malignant tumors (MPMT) refers to the presence of two or more primary cancers of different organs in the same patient. MPMT is a sparse disease in the past, but there has been a gradual increase in the morbidity. Since multiple primary malignant tumors treatment methods differ, it is essential for clinicians to be able to distinguish between separate primary lesions and metastasis. CASE PRESENTATION: We present the case of a 57-year-old woman with MPMT presenting with cancer in the left breast and synchronous double primary lung adenocarcinomas. We used IHC and epidermal growth factor receptor(EGFR)mutation to analyze genomic alteration profiles in the patient to validate the difference among the pathological assessments and the clinical differences between double primary lesions of lung and breast. EGFR gene analysis of breast cancer lesion revealed no mutations. The left and right lower lobe lung adenocarcinomas contained EGFR gene mutations: an L858R point mutation in exon 21 in the left lesion and a deletion mutation in exon 19 in the right lesion. The breast cancer and both lung adenocarcinomas were surgically resected. To date, the patient has remained disease-free. CONCLUSIONS: Both pathological and molecular assessment adapted in the current study appeared necessary. Mutational analysis of the EGFR gene provided important information not only in the diagnosis and but also in the treatment of MPMT.

4-Phenybutyric acid promotes gastric cancer cell migration via histone deacetylase inhibition-mediated HER3/HER4 up-regulation.

Dysregulation of histone acetylation plays an important role in tumor development. Histone acetylation regulates gene transcription and expression, which is reversibly regulated by histone acetyltransferase (HAT) and histone deacetylase (HDAC). As an HDAC inhibitor, 4-phenylbutyric acid (4-PBA) can increase histone acetylation levels by inhibiting HDAC activity. While 4-PBA inhibits proliferation of tumor cells in vitro, clinical trials have failed to show benefits of 4-PBA for refractory solid tumors. Here, we found that 4-PBA could enhance the migration capacity of gastric cancer cells. Upregulation of HER3/HER4 and activation of HER3/HER4-ERK pathway was shown to be involved in 4-PBA-induced gastric cancer cell migration. Knockdown of HER3/HER4 blocked HER3/HER4-ERK activation and partially prevented 4-PBA-induced cell migration. Consistently, the ERK inhibitor PD98059 also partially prevented 4-PBA-induced cell migration. Moreover, enhanced levels of acetyl-histones were detected following 4-PBA-treatment, and histone3 acetylation in promoter regions of HER3 and HER4 were confirmed by ChIP. These results demonstrate that 4-PBA promotes gastric cancer cells migration through upregulation of HER3/HER4 subsequent to increased levels of acetyl-histone and activation of ERK signaling. These novel findings provide important considerations for the use of 4-PBA in cancer therapeutics.

Tyrosine kinase inhibitor-induced IL-6/STAT3 activation decreases sensitivity of EGFR-mutant non-small cell lung cancer to icotinib.

Tyrosine kinase Inhibitors (TKIs) of epidermal growth factor receptor (EGFR) has considerably benefited for non-small cell lung carcinomas (NSCLC) harbor mutations in EGFR. However, the factors attenuating EGFR-TKI efficiency are obstacles to inhibit the proliferation of EGFR-mutant NSCLC cells successfully. Clarifying the insensitivity mechanisms of EGFR-TKI would help to develop new treatment strategy. In this study, the sensitivity of EGFR-mutant NSCLC cell lines, PC9 and HCC827, to icotinib was detected. Similar with other EGFR-TKIs such as gefitinib and erlortinib in previous research, the proliferation of two cell lines was apparently inhibited. However, we surprisingly found that contrast with the suppression of EGFR-AKT/ERK pathway, STAT3 was significantly activated in PC9 cells with the treatment of icotinib, but not in HCC827 cells. Further study confirmed that icotinib concomitantly induced IL-6 secretion and src activation in PC9 cells. Moreover, with the treatment of IL-6 neutralizing antibody or src inhibitor, dasatinib, icotinib-induced phosphorylation of STAT3 was reduced, as well as the sensitivity of PC9 to icotinib was also partially increased. Our results suggest that Src/IL-6/STAT3 bypass pathway is activated to maintain cell survival when the EGFR pathway was inhibited by TKIs, even in some EGFR-mutant NSCLC cells sensitive to TKIs. This finding provides a groundwork for potential combinatorial treatment with TKIs and Src or STAT3 inhibitor to improve icotinib sensitivity.

Structural characterization of carbohydrate binding by LMAN1 protein provides new insight into the endoplasmic reticulum export of factors V (FV) and VIII (FVIII).

LMAN1 (ERGIC-53) is a key mammalian cargo receptor responsible for the export of a subset of glycoproteins from the endoplasmic reticulum. Together with its soluble coreceptor MCFD2, LMAN1 transports coagulation factors V (FV) and VIII (FVIII). Mutations in LMAN1 or MCFD2 cause the genetic bleeding disorder combined deficiency of FV and FVIII (F5F8D). The LMAN1 carbohydrate recognition domain (CRD) binds to both glycoprotein cargo and MCFD2 in a Ca(2+)-dependent manner. To understand the biochemical basis and regulation of LMAN1 binding to glycoprotein cargo, we solved crystal structures of the LMAN1-CRD bound to Man-α-1,2-Man, the terminal carbohydrate moiety of high mannose glycans. Our structural data, combined with mutagenesis and in vitro binding assays, define the central mannose-binding site on LMAN1 and pinpoint histidine 178 and glycines 251/252 as critical residues for FV/FVIII binding. We also show that mannobiose binding is relatively independent of pH in the range relevant for endoplasmic reticulum-to-Golgi traffic, but is sensitive to lowered Ca(2+) concentrations. The distinct LMAN1/MCFD2 interaction is maintained at these lowered Ca(2+) concentrations. Our results suggest that compartmental changes in Ca(2+) concentration regulate glycoprotein cargo binding and release from the LMAN1·MCFD2 complex in the early secretory pathway.

Phosphorylated VEGFR2 and hypertension: potential biomarkers to indicate VEGF-dependency of advanced breast cancer in anti-angiogenic therapy.

The efficacy of anti-VEGF agents probably lies on VEGF-dependency. Apatinib, a specific tyrosine kinase inhibitor that targets VEGF receptor 2, was assessed in patients with advanced breast cancer (ABC) (ClinicalTrials.gov NCT01176669 and NCT01653561). This substudy was to explore the potential biomarkers for VEGF-dependency in apatinib-treated breast cancer. Eighty pretreated patients received apatinib 750 or 500 mg/day orally in 4-week cycles. Circulating biomarkers were measured using a multiplex assay, and tissue biomarkers were identified with immunostaining. Baseline characteristics and adverse events (AEs) were included in the analysis. Statistical confirmation of independent predictive factors for anti-tumor efficacy was performed using Cox and Logistic regression models. Median progression-free survival (PFS) was 3.8 months, and overall survival (OS) was 10.6 months, with 17.5 % of objective response rate. Prominent AEs (≥60 %) were hypertension, hand-foot skin reaction (HFSR), and proteinuria. Higher tumor phosphorylated VEGFR2 (p-VEGFR2) expressions (P = 0.001), higher baseline serum soluble VEGFR2 (P = 0.031), hypertension (P = 0.011), and HFSR (P = 0.018) were significantly related to longer PFS, whereas hypertension (P = 0.002) and HFSR (P = 0.001) were also related to OS. Based on multivariate analysis, only p-VEGFR2 (adjusted HR, 0.40; P = 0.013) and hypertension (adjusted HR, 0.58; P = 0.038) were independent predictive factors for both PFS and clinical benefit rate. Apatinib had substantial antitumor activity in ABC and manageable toxicity. p-VEGFR2 and hypertension may be surrogate predictors of VEGF-dependency of breast cancer, which may identify an anti-angiogenesis sensitive population.

Clinical characteristics and prognosis analysis of postoperative patients with stage I-III colon cancer based on SEER database.

PURPOSE: To identify the relevant factors affecting the prognosis and survival time of colon cancer and construct a survival prediction model. METHODS: Data on postoperative stage I-III colon cancer patients were obtained from the Surveillance, Epidemiology, and End Results database. We used R project to analyze the data. Univariate and multivariate Cox regression analyses were performed for independent factors correlated with overall survival from colon cancer. The C-index was used to screen the factors that had the greatest influence in overall survival after surgery in colon cancer patients. Receiver operating characteristic (ROC) curve was made according to the Risk score and calculated to validate the predictive accuracy of the model. In addition, we used decision curve analysis (DCA) to evaluate the clinical benefits and utility of the nomogram. We created a model survival curve to determine the difference in prognosis between patients in the low-risk group and those in the high-risk group. RESULTS: Univariate and multifactor COX analyses showed that the race, Grade, tumor size, N-stage and T-stage were independent risk factors affecting survival time of patients. The analysis of ROC and DCA showed the nomogram prediction model constructed based on the above indicators has good predictive effects. CONCLUSION: Overall, the nomogram constructed in this study has good predictive effects. It can provide a reference for future clinicians to evaluate the prognosis of colon cancer patients.

Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow.

Clinical trial enrollment is impeded by the significant time burden placed on research coordinators screening eligible patients. With 50,000 new cancer cases every year, the Veterans Health Administration (VHA) has made increased access for Veterans to high-quality clinical trials a priority. To aid in this effort, we worked with research coordinators to build the MPACT (Matching Patients to Accelerate Clinical Trials) platform with a goal of improving efficiency in the screening process. MPACT supports both a trial prescreening workflow and a screening workflow, employing Natural Language Processing and Data Science methods to produce reliable phenotypes of trial eligibility criteria. MPACT also has a functionality to track a patient's eligibility status over time. Qualitative feedback has been promising with users reporting a reduction in time spent on identifying eligible patients.