Systematic analysis reveals distinct roles of USF family proteins in various cancer types.

BACKGROUND: Upstream stimulatory factors (USFs) are members of the basic helix-loop-helix leucine zipper transcription factor family, including USF1, USF2, and USF3. The first two members have been well studied compared to the third member, USF3, which has received scarce attention in cancer research to date. Despite a recently reported association of its alteration with thyroid carcinoma, its expression has not been previously analyzed. METHODS: We comprehensively analyzed differential levels of USFs expression, genomic alteration, DNA methylation, and their prognostic value across different cancer types and the possible correlation with tumor-infiltrating immune cells and drug response by using different bioinformatics tools. RESULTS: Our findings established that USFs play an important role in cancers related to the urinary system and justify the necessity for further investigation. We implemented and offer a useful ShinyApp to facilitate researchers' efforts to inquire about any other gene of interest and to perform the analysis of drug response in a user-friendly fashion at http://zzdlab.com:3838/Drugdiscovery/.

Exploring the molecular characteristics of the malignant potential of gastric adenocarcinoma with enteroblastic differentiation.

AIMS: Gastric adenocarcinoma with enteroblastic differentiation (GAED) is a rare subset of alpha-fetoprotein (AFP)-producing carcinomas with poor prognosis. However, the molecular features associated with the malignant potential of GEAD remain partially elucidated. METHODS AND RESULTS: In this study, the relationship between clinicopathological parameters and aggressive biological behaviour was analysed in 37 patients with GAED. The results showed that GAED tended to infiltrate the deep layer of the gastric wall and possessed more frequent vascular invasion than conventional gastric adenocarcinoma (CGA) (P < 0.001). All distant metastases were observed in the GAED group, not the CGA group (P < 0.001). High HER2 expression was found in nearly 24.32% of the informative cases, and none showed EBV-encoded RNA positivity or deficient mismatch repair. The most frequently mutated gene in GAED was p53. Programmed cell death-ligand 1 (PD-L1) immunostaining revealed 13 patients with a combined positive score (CPS) ≥ 5 (65%, 13 of 20). Thus, based on these molecular markers (immunostaining, in situ hybridisation and mutation analysis), GAED may be classified as a unique subgroup of the chromosomal instability subtype with HER2+ /EBV- /MSS/TP53+ /PD-L1+ . Next-generation sequencing analyses showed that mutations in the TOPI, ELOA and NOTCH3 genes were found only in GAED, and abnormally expressed genes in GAED were significantly enriched in hepatocellular carcinoma-, gland development-, and gastric cancer-related pathways. CONCLUSION: The HER2+ /EBV- /MSS/TP53+ /PD-L1+ profile and hepatocellular carcinoma-related pathways may be significant in the malignant potential of GAED. In addition to anti-HER2 therapy, immune check-point inhibitors may be an effective treatment option for patients with GAED.

Development of a nomogram based on serum cytokine-related riskscore in breast cancer.

Background: Cytokines are involved in many inflammatory diseases and thus play an important role in tumor immune regulation. In recent years, researchers have found that breast cancer is not only related to genetic and environmental factors, but also to the chronic inflammation and immunity. However, the correlation between serum cytokines and blood tests indicators remain unclear. Methods: A total of 84 serum samples and clinicopathological data of breast cancer patients from Tianjin Cancer Institute & Hospital, Tianjin Medical University, Tianjin, P. R. China were collected. The expression levels of the 12 cytokines were detected by immunofluorescence method. Blood tests results were obtained from medical records. By stepwise Cox regression analysis, a cytokine-related gene signature was generated. Univariate and multivariate Cox regression were used to analyze the influence on the prognosis of patients. A nomogram was constructed to illustrate the cytokine-related riskscore predicting 5-year OS, which was further evaluated and validated by C-index and ROC curve. The correlation between the expression of cytokines in serum and other blood indicators was studied by using Spearman's test. Results: The riskscore was calculated as IL-4×0.99069 + TNF-α×0.03683. Patients were divided into high and low risk groups according to the median riskscore, with the high-risk group has a shorter survival time by log-rank test (training set, P=0.017; validation set, P=0.013). Combined with the clinical characteristics, the riskscore was found to be an independent factor for predicting the OS of breast cancer patients in both training cohort (HR=1.2, P<0.01) and validation cohort (HR=1.6, P=0.023). The 5-year C-index and AUC of the nomogram were 0.78 and 0.68, respectively. IL-4 was further found to be negatively correlated with ALB. Conclusion: In summary, we have developed a nomogram based on two cytokines including IL-4 and TNF-α to predict OS of breast cancer and investigated their correlation with blood test indicators.

Optimal therapy for concomitant EGFR and TP53 mutated non-small cell lung cancer: a real-world study.

BACKGROUND: Non-small cell cancer (NSCLC) patients with concomitant epidermal growth factor receptor (EGFR) and TP53 mutations have a poor prognosis with the treatment of tyrosine kinase inhibitors (TKIs), and may benefit from a combination regimen preferentially. The present study aims to compare the benefits of EGFR-TKIs and its combination with antiangiogenic drugs or chemotherapy in patients with NSCLC harboring EGFR and TP53 co-mutation in a real-life setting. METHODS: This retrospective analysis included 124 patients with advanced NSCLC having concomitant EGFR and TP53 mutations, who underwent next-generation sequencing prior to treatment. Patients were classified into the EGFR-TKI group and combination therapy group. The primary end point of this study was progression-free survival (PFS). The Kaplan-Meier (KM) curve was drawn to analyze PFS, and the differences between the groups were compared using the logarithmic rank test. Univariate and multivariate cox regression analysis was performed on the risk factors associated with survival. RESULTS: The combination group included 72 patients who received the regimen of EGFR-TKIs combined with antiangiogenic drugs or chemotherapy, while the EGFR-TKI monotherapy group included 52 patients treated with TKI only. The median PFS was significantly longer in the combination group than in the EGFR-TKI group (18.0 months; 95% confidence interval [CI]: 12.1-23.9 vs. 7.0 months; 95% CI: 6.1-7.9; p < 0.001) with greater PFS benefit in TP53 exon 4 or 7 mutations subgroup. Subgroup analysis showed a similar trend. The median duration of response was significantly longer in the combination group than in the EGFR-TKI group. Patients with 19 deletions or L858R mutations both achieved a significant PFS benefit with combination therapy versus EGFR-TKI alone. CONCLUSION: Combination therapy had a higher efficacy than EGFR-TKI alone for patients with NSCLC having concomitant EGFR and TP53 mutations. Future prospective clinical trials are needed to determine the role of combination therapy for this patient population.

Water channel protein AQP1 in cytoplasm is a critical factor in breast cancer local invasion.

BACKGROUND: Metastasis of breast cancer grows from the local invasion to the distant colonization. Blocking the local invasion step would be promising for breast cancer treatment. Our present study demonstrated AQP1 was a crucial target in breast cancer local invasion. METHODS: Mass spectrometry combined with bioinformatics analysis was used to identify AQP1 associated proteins ANXA2 and Rab1b. Co-immunoprecipitation, immunofluorescence assays and cell functional experiments were carried out to define the relationship among AQP1, ANXA2 and Rab1b and their re-localization in breast cancer cells. The Cox proportional hazards regression model was performed toward the identification of relevant prognostic factors. Survival curves were plotted by the Kaplan-Meier method and compared by the log-rank test. RESULTS: Here, we show that the cytoplasmic water channel protein AQP1, a crucial target in breast cancer local invasion, recruited ANXA2 from the cellular membrane to the Golgi apparatus, promoted Golgi apparatus extension, and induced breast cancer cell migration and invasion. In addition, cytoplasmic AQP1 recruited cytosolic free Rab1b to the Golgi apparatus to form a ternary complex containing AQP1, ANXA2, and Rab1b, which induced cellular secretion of the pro-metastatic proteins ICAM1 and CTSS. Cellular secretion of ICAM1 and CTSS led to the migration and invasion of breast cancer cells. Both in vivo assay and clinical analysis data confirmed above results. CONCLUSIONS: Our findings suggested a novel mechanism for AQP1-induced breast cancer local invasion. Therefore, targeting AQP1 offers promises in breast cancer treatment.

Screening for Lipid-Metabolism-Related Genes and Identifying the Diagnostic Potential of ANGPTL6 for HBV-Related Early-Stage Hepatocellular Carcinoma.

Lipid metabolic reprogramming is one of the hallmarks of hepatocarcinogenesis and development. Therefore, lipid-metabolism-related genes may be used as potential biomarkers for hepatocellular carcinoma (HCC). This study aimed to screen for genes with dysregulated expression related to lipid metabolism in HCC and explored the clinical value of these genes. We screened differentially expressed proteins between tumorous and adjacent nontumorous tissues of hepatitis B virus (HBV)-related HCC patients using a Nanoscale Liquid Chromatography-Tandem Mass Spectrometry platform and combined it with transcriptomic data of lipid-metabolism-related genes from the GEO and HPA databases to identify dysregulated genes that may be involved in lipid metabolic processes. The potential clinical values of these genes were explored by bioinformatics online analysis tools (GEPIA, cBioPortal, SurvivalMeth, and TIMER). The expression levels of the secreted protein (angiopoietin-like protein 6, ANGPTL6) in serum were analyzed by ELISA. The ability of serum ANGPTL6 to diagnose early HCC was assessed by ROC curves. The results showed that serum ANGPTL6 could effectively differentiate between HBV-related early HCC patients with normal serum alpha-fetoprotein (AFP) levels and the noncancer group (healthy participants and chronic hepatitis B patients) (AUC = 0.717, 95% CI: from 0.614 to 0.805). Serum ANGPTL6 can be used as a potential second-line biomarker to supplement serum AFP in the early diagnosis of HBV-related HCC.

Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer.

Objective: To identify CT imaging biomarkers based on radiomic features for predicting brain metastases (BM) in patients with ALK-rearranged non-small cell lung cancer (NSCLC). Methods: NSCLC patients with pathologically confirmed ALK rearrangement from January 2014 to December 2020 in our hospital were enrolled retrospectively in this study. Finally, 77 patients were included according to the inclusion and exclusion criteria. Patients were divided into two groups: BM+ were those patients who were diagnosed with BM at baseline examination (n = 16) or within 1 year's follow-up (n = 14), and BM- were those without BM followed up for at least 1 year (n = 47). Radiomic features were extracted from the pretreatment thoracic CT images. Sequential univariate logistic regression, LASSO regression, and backward stepwise logistic regression were used to select radiomic features and develop a BM-predicting model. Results: Five robust radiomic features were found to be independent predictors of BM. AUC for radiomics model was 0.828 (95% CI: 0.736-0.921), and when combined with clinical features, the AUC was increased (p = 0.017) to 0.909 (95% CI: 0.845-0.972). The individualized BM-predicting model incorporated with clinical features was visualized by the nomogram. Conclusion: Radiomic features extracted from pretreatment thoracic CT images have the potential to predict BM within 1 year after detection of the primary tumor in patients with ALK-rearranged NSCLC. The radiomics model incorporated with clinical features shows improved risk stratification for such patients.

Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer.

OBJECTIVES: To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC). METHODS: A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model. RESULTS: Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively. CONCLUSIONS: Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment. KEY POINTS: • Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.

Tolerance and Pharmacokinetics of Recombinant Human Endostatin Administered as Single-Dose or Multiple-Dose Infusions in Patients With Advanced Solid Tumors: A Phase I Clinical Trial.

Objective: This study aimed to investigate the tolerance and pharmacokinetic characteristics of recombinant human endostatin (rh-endostatin) administered as single-dose or multiple-dose infusions in patients with advanced solid tumors. Methods: This phase I trial was designed as a single-center, single-arm, nonrandomized, open-label, dose-escalation study. The trial consisted of 2 parts: a single-dose part and a multiple-dose part, each with 3 dose comparison groups. Rh-endostatin was administered as an intravenous injection only once at a dose of 5 mg/m2, 7.5 mg/m2, or 10 mg/m2 in the single-dose part and as a daily intravenous injection for 14 days at the same doses in the multiple-dose part. The serum pharmacokinetics, toxicity and immunogenicity of rh-endostatin were evaluated. Results: Dose-limiting toxicity (DLT) was not observed in any group. A few patients developed cardiotoxicity, such as QT prolongation or narrow arrhythmia. Other adverse events were slight coagulation abnormalities and haematological abnormalities. For rh-endostatin doses of 5 mg/m2, 7.5 mg/m2, and 10 mg/m2, the mean Cmax values in the single-dose part were 344 ± 38.7 ng/mL, 524 ± 157 ng/mL, and 800 ± 201 ng/mL, respectively, and the average AUC0-t values were 3290 ± 3790 ng•h/mL, 4940 ± 4380 ng•h/mL, and 5050 ± 3980 ng•h/mL, respectively. The Cmax ss values of the 3 doses in the multiple-dose part were 575 ± 270 ng/mL, 531 ± 106 ng/mL, and 864 ± 166 ng/mL, respectively, and the AUC0-τ values were 3610 ± 1040 ng•h/mL, 3290 ± 1090 ng•h/mL, and 5180 ± 1210 ng•h/mL, respectively. The Cmax of a single-dose regimen showed linear kinetic characteristics. The patients in the single-dose group were negative for serum antibodies against rh-endostatin, while one patient in the multiple-dose group was positive. Conclusions: Rh-endostatin as a daily intravenous injection for 14 days in patients with advanced solid tumors is safe and well tolerated, without DLT, at doses of 5 mg/m2, 7.5 mg/m2, and 10 mg/m2. Serum antibodies against rh-endostatin were very low after multiple infusions. For phase II trials, the recommended rh-endostatin dose is 10 mg/m2 as a daily intravenous injection for 14 days.

Endocytic protein intersectin1-S shuttles into nucleus to suppress the DNA replication in breast cancer.

Breast cancer is the most common type of cancer worldwide. However, the well-known molecular biomarkers are not enough to meet the needs of precision medicine. In search for novel targets in this regard, we reported ITSN1 (intersectin1) as one of the candidates through mRNA microarray analysis. In the present study, we reported that endocytic protein ITSN1-S exists not only in the cytoplasm but also in nuclei of breast cancer cells. ITSN1-S' functional nuclear localization signal is within its residues 306-312. Its nuclear export signal (NES) resides within its SH3 domains. We also found, the interaction between the CC domain of nuclear ITSN1-S and the NT domain of nuclear DNA helicase II (NDH II) directly suppressed the DNA replication and nascent DNA synthesis by inhibiting the R-loops resolution in breast cancer cells. Furthermore, the interaction between the EH domains of cytoplasmic ITSN1-S and PI3KC2α inhibit cell migration and invasion by inactivating the PI3KC2α-AKT pathway. Our results were confirmed in both ITSN1 gene knockout cells and in vivo assays. Finally, our clinical data showed a potential application of the combined consideration of the cytoplasmic and nuclear ITSN1-S as an independent prognosis factor. In conclusion, our study revealed ITSN1-S' novel positioning in the nuclei of breast cancer cells, its function in suppressing DNA replication, and its potential application in improved breast cancer prognosis.

Identification of hub genes and their novel diagnostic and prognostic significance in pancreatic adenocarcinoma.

OBJECTIVE: The main reasons for the poor prognoses of pancreatic adenocarcinoma (PA) patients are rapid early-stage progression, advanced stage metastasis, and chemotherapy resistance. Identification of novel diagnostic and prognostic biomarkers of PA is therefore urgently needed. METHODS: Three mRNA microarray datasets were obtained from the Gene Expression Omnibus database to select differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses for hub genes were performed using DAVID. Correlations between expression levels of hub genes and cancer-infiltrating immune cells were investigated by TIMER. Cox proportional hazard regression analyses were also performed. Serum hub genes were screened using the HPA platform and verified for diagnostic value using ELISAs. RESULTS: We identified 59 hub genes among 752 DEGs. GO analysis indicated that these 59 hub genes were mainly involved in the defense response to viruses and the type I interferon signaling pathway. We also discovered that RSAD2 and SMC4 were associated with immune cell infiltration in the PA microenvironment. Additionally, DLGAP5 mRNA might be used as an independent risk factor for the prognoses of PA patients. Furthermore, the protein encoded by ISG15, which exists in peripheral blood, was validated as a potential diagnostic biomarker that distinguished PA patients from healthy controls (area under the curve: 0.902, 95% confidence interval: 0.819-0.961). CONCLUSIONS: Our study suggested that RSAD2 and SMC4 were associated with immune cell infiltration in the PA microenvironment, while DLGAP5 mRNA expression might be an independent risk factor for the survival prognoses of PA patients. Moreover, ELISAs indicated that serum ISG15 could be a potential novel diagnostic biomarker for PA.

nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.

Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identification of Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and expensive when compared with computational approaches. To date, several predictors for Kcr site prediction have been developed, most of which are capable of predicting crotonylation sites on either histones alone or mixed histone and nonhistone proteins together. These methods exhibit high diversity in their algorithms, encoding schemes, feature selection techniques and performance assessment strategies. However, none of them were designed for predicting Kcr sites on nonhistone proteins. Therefore, it is desirable to develop an effective predictor for identifying Kcr sites from the large amount of nonhistone sequence data. For this purpose, we first provide a comprehensive review on six methods for predicting crotonylation sites. Second, we develop a novel deep learning-based computational framework termed as CNNrgb for Kcr site prediction on nonhistone proteins by integrating different types of features. We benchmark its performance against multiple commonly used machine learning classifiers (including random forest, logitboost, naïve Bayes and logistic regression) by performing both 10-fold cross-validation and independent test. The results show that the proposed CNNrgb framework achieves the best performance with high computational efficiency on large datasets. Moreover, to facilitate users' efforts to investigate Kcr sites on human nonhistone proteins, we implement an online server called nhKcr and compare it with other existing tools to illustrate the utility and robustness of our method. The nhKcr web server and all the datasets utilized in this study are freely accessible at http://nhKcr.erc.monash.edu/.

iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.

Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development of new predictive models, which depends on the availability of effective tools that support these efforts. We introduce iLearnPlus, the first machine-learning platform with graphical- and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences. iLearnPlus provides a comprehensive set of algorithms and automates sequence-based feature extraction and analysis, construction and deployment of models, assessment of predictive performance, statistical analysis, and data visualization; all without programming. iLearnPlus includes a wide range of feature sets which encode information from the input sequences and over twenty machine-learning algorithms that cover several deep-learning approaches, outnumbering the current solutions by a wide margin. Our solution caters to experienced bioinformaticians, given the broad range of options, and biologists with no programming background, given the point-and-click interface and easy-to-follow design process. We showcase iLearnPlus with two case studies concerning prediction of long noncoding RNAs (lncRNAs) from RNA transcripts and prediction of crotonylation sites in protein chains. iLearnPlus is an open-source platform available at https://github.com/Superzchen/iLearnPlus/ with the webserver at http://ilearnplus.erc.monash.edu/.

Computational identification of ubiquitination sites in Arabidopsis thaliana using convolutional neural networks.

KEY MESSAGE: We developed two CNNs for predicting ubiquitination sites in Arabidopsis thaliana, demonstrated their competitive performance, analyzed amino acid physicochemical properties and the CNN structures, and predicted ubiquitination sites in Arabidopsis. As an important posttranslational protein modification, ubiquitination plays critical roles in plant physiology, including plant growth and development, biotic and abiotic stress, metabolism, and so on. A lot of ubiquitination site prediction models have been developed for human, mouse and yeast. However, there are few models to predict ubiquitination sites for the plant Arabidopsis thaliana. Based on this context, we proposed two convolutional neural network (CNN) based models for predicting ubiquitination sites in A. thaliana. The two models reach AUC (area under the ROC curve) values of 0.924 and 0.913 respectively in five-fold cross-validation, and 0.921 and 0.914 respectively in independent test, which outperform other models and demonstrate the competitive edge of them. We in-depth analyze the amino acid physicochemical properties in the neighboring sequence regions of the ubiquitination sites, and study the influence of the CNN structure to the prediction performance. Potential ubiquitination sites in the global Arbidopsis proteome are predicted using the two CNN models. To facilitate the community, the source code, training and test dataset, predicted ubiquitination sites in the Arbidopsis proteome are available at GitHub ( http://github.com/nongdaxiaofeng/CNNAthUbi ) for interest users.

Th1 cytokine interferon gamma improves response in HER2 breast cancer by modulating the ubiquitin proteasomal pathway.

HER2 breast cancer (BC) remains a significant problem in patients with locally advanced or metastatic BC. We investigated the relationship between T helper 1 (Th1) immune response and the proteasomal degradation pathway (PDP), in HER2-sensitive and -resistant cells. HER2 overexpression is partially maintained because E3 ubiquitin ligase Cullin5 (CUL5), which degrades HER2, is frequently mutated or underexpressed, while the client-protective co-chaperones cell division cycle 37 (Cdc37) and heat shock protein 90 (Hsp90) are increased translating to diminished survival. The Th1 cytokine interferon (IFN)-γ caused increased CUL5 expression and marked dissociation of both Cdc37 and Hsp90 from HER2, causing significant surface loss of HER2, diminished growth, and induction of tumor senescence. In HER2-resistant mammary carcinoma, either IFN-γ or Th1-polarizing anti-HER2 vaccination, when administered with anti-HER2 antibodies, demonstrated increased intratumor CUL5 expression, decreased surface HER2, and tumor senescence with significant therapeutic activity. IFN-γ synergized with multiple HER2-targeted agents to decrease surface HER2 expression, resulting in decreased tumor growth. These data suggest a novel function of IFN-γ that regulates HER2 through the PDP pathway and provides an opportunity to impact HER2 responses through anti-tumor immunity.

Angiotensin-converting enzyme inhibitors have adverse effects in anti-angiogenesis therapy for hepatocellular carcinoma.

At present, anti-angiogenic drugs (AADs) are widely used in the systemic treatment of hepatocellular carcinoma (HCC) or other types of cancer, and have achieved good anti-cancer effect, whereas treatment-related proteinuria can affect the routine use of AADs, which in turn abates the overall efficacy. Currently, most clinicians prescribe angiotensin-converting enzyme inhibitors (ACEIs) to alleviate proteinuria according to diabetic nephropathy guidelines or expert recommendations. However, the efficacy of ACEIs in reducing AAD-related proteinuria and its effect on the anticancer effect of AADs is unknown. Our clinical data showed that some HCC patients experienced tumor progression by ACEIs administration for the treatment of proteinuria caused by AADs. Here, we confirmed that in different tumor-bearing mouse models, ACEIs did not delay the appearance of proteinuria or alleviate proteinuria caused by AADs but compromised the anticancer efficacy of AADs. This effect is unrelated to the change in the VEGF signaling pathway. Our data showed that the combination of ACEIs and AADs flared the production of kidney-derived erythropoietin (EPO). In turn, EPO compromises the anti-angiogenic effects of AADs and decreases antitumor activity. In conclusion, for the treatment of proteinuria caused by AADs, ACEIs have no efficacy while also promoting AADs resistance. This finding is of great significance to guide clinical standardized management of side effects of anti-angiogenic therapy for cancer patients.

Bioinformatics Analysis of Expression and Alterations of BARD1 in Breast Cancer.

BACKGROUND: Breast cancer is one of the most common malignant tumor type in women worldwide. BARD1 could impact function of BRCA1 as its interaction partner. In the current study, we aimed to investigate the prognostic role of BARD1 expression as well as its alterations in breast cancer using different online tools. METHODS: We performed a bioinformatics analysis for BARD1 in patients with breast cancer using several online databases, including Oncomine, bc-GenExMiner, PrognoScan, Search Tool for the Retrieval of Interacting Genes, Cytoscape, and cBioPortal. RESULTS: We found that BARD1 was highly expressed in basal-like, HER2-E, and luminal B compared with normal-like subtype. Forest plot showed that BARD1 overexpression was correlated with worse distant metastasis-free survival (hazard ratio: 2.72, 95% confidence interval: 1.02-2.21; P = .0448), disease-specific survival (hazard ratio: 2.65, 95% confidence interval: 1.37-5.12; P = .0037), and disease-free survival (hazard ratio: 1.98, 95% confidence interval: 1.22-3.24; P = .0062) but positively correlated with overall survival (hazard ratio: 0.66, 95% confidence interval: 0.50-0.85; P = .0017). Multivariate analysis indicated that BARD1 expression was significantly associated with distant metastasis-free survival (hazard ratio: 4.60, 95% confidence interval: 1.22-17.28; P = .0239) whereas marginally significant for disease-free survival (hazard ratio: 1.00, 95% confidence interval: 1.00-1.01, P = .0630) and disease-specific survival (hazard ratio: 1.96, 95% confidence interval: 0.97-3.96; P = .0602). Meanwhile, alterations in BARD1 interaction network were associated with worse overall survival instead of BARD1 alteration alone. CONCLUSIONS: Bioinformatics analysis revealed that BARD1 may be a predictive biomarker for prognosis of breast cancer. However, future research is required to validate our findings.

Multiple MicroRNAs synergistically promote tolerance to epidermal growth factor receptor-targeted drugs in smoked lung cancer therapies.

AIMS: Lung cancer is one of the leading causes of cancer-related mortality. Tobacco usage is considered as associated with the carcinogenesis, progression, and prognosis of lung cancer. Previous studies have demonstrated that the smoking inhibited medical therapy results from K-Ras gene mutation through suppressing the epidermal growth factor receptor (EGFR) pathway. However, recent clinical trials have revealed that few smoked lung cancer patients present K-Ras gene mutation; yet, the majority of smoked lung cancer patients remain K-Ras nonmutation. The chemo-resistant mechanism remains unclear. Recently, microRNA (miRNA) interaction has been found to play an important role in drug resistance process. We hypothesized that miRNA may exert medicine resistance during the processes of lung cancer therapy. METHODS: Here, we analyzed miRNA array data from the GEO database. RESULTS: Our results showed that the interaction network among hsa-miR-30d-3p, hsa-miR-184, hsa-miR-500a, hsa-miR-542-3p, among others, inhibited EGFR-targeted medicine therapy. CONCLUSION: The research will provide evidence that promotes novel therapy of lung cancers.

Single drug biomarker prediction for ER- breast cancer outcome from chemotherapy.

ER-negative breast cancer includes most aggressive subtypes of breast cancer such as triple negative (TN) breast cancer. Excluded from hormonal and targeted therapies effectively used for other subtypes of breast cancer, standard chemotherapy is one of the primary treatment options for these patients. However, as ER- patients have shown highly heterogeneous responses to different chemotherapies, it has been difficult to select most beneficial chemotherapy treatments for them. In this study, we have simultaneously developed single drug biomarker models for four standard chemotherapy agents: paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) to predict responses and survival of ER- breast cancer patients treated with combination chemotherapies. We then flexibly combined these individual drug biomarkers for predicting patient outcomes of two independent cohorts of ER- breast cancer patients who were treated with different drug combinations of neoadjuvant chemotherapy. These individual and combined drug biomarker models significantly predicted chemotherapy response for 197 ER- patients in the Hatzis cohort (AUC = 0.637, P = 0.002) and 69 ER- patients in the Hess cohort (AUC = 0.635, P = 0.056). The prediction was also significant for the TN subgroup of both cohorts (AUC = 0.60, 0.72, P = 0.043, 0.009). In survival analysis, our predicted responder patients showed significantly improved survival with a >17 months longer median PFS than the predicted non-responder patients for both ER- and TN subgroups (log-rank test P-value = 0.018 and 0.044). This flexible prediction capability based on single drug biomarkers may allow us to even select new drug combinations most beneficial to individual patients with ER- breast cancer.