High Expression Levels of AGGF1 and MFAP4 Predict Primary Platinum-Based Chemoresistance and are Associated with Adverse Prognosis in Patients with Serous Ovarian Cancer

Spatial transcriptomics reveals discrete tumour microenvironments and autocrine loops within ovarian cancer subclones

High-grade serous ovarian carcinoma (HGSOC) is genetically unstable and characterised by the presence of subclones with distinct genotypes. Intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and poor prognosis. Here, we use spatial transcriptomics to identify HGSOC subclones and study their association with infiltrating cell populations. Visium spatial transcriptomics reveals multiple tumour subclones with different copy number alterations present within individual tumour sections. These subclones differentially express various ligands and receptors and are predicted to differentially associate with different stromal and immune cell populations. In one sample, CosMx single molecule imaging reveals subclones differentially associating with immune cell populations, fibroblasts, and endothelial cells. Cell-to-cell communication analysis identifies subclone-specific signalling to stromal and immune cells and multiple subclone-specific autocrine loops. Our study highlights the high degree of subclonal heterogeneity in HGSOC and suggests that subclone-specific ligand and receptor expression patterns likely modulate how HGSOC cells interact with their local microenvironment.

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

BACKGROUND

Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate.

OBJECTIVE

This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer.

RESULTS

A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort.

CONCLUSIONS

Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

Prediction of Chemoresistance-How Preclinical Data Could Help to Modify Therapeutic Strategy in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is one of the most lethal tumors generally and the most fatal cancer of the female genital tract. The approved standard therapy consists of surgical cytoreduction and platinum/taxane-based chemotherapy, and of targeted therapy in selected patients. The main therapeutic problem is chemoresistance of recurrent and metastatic HGSOC tumors which results in low survival in the group of FIGO III/IV. Therefore, the prediction and monitoring of chemoresistance seems to be of utmost importance for the improvement of HGSOC management. This type of cancer has genetic heterogeneity with several subtypes being characterized by diverse gene signatures and disturbed peculiar epigenetic regulation. HGSOC develops and metastasizes preferentially in the specific intraperitoneal environment composed mainly of fibroblasts, adipocytes, and immune cells. Different HGSOC subtypes could be sensitive to distinct sets of drugs. Moreover, primary, metastatic, and recurrent tumors are characterized by an individual biology, and thus diverse drug responsibility. Without a precise identification of the tumor and its microenvironment, effective treatment seems to be elusive. This paper reviews tumor-derived genomic, mutational, cellular, and epigenetic biomarkers of HGSOC drug resistance, as well as tumor microenvironment-derived biomarkers of chemoresistance, and discusses their possible use in the novel complex approach to ovarian cancer therapy and monitoring.

Declined plasma microfibrillar-associated protein 4 levels in acute coronary syndrome

BACKGROUND

Microfibrillar-associated protein (MFAP4), initially identified as an extracellular matrix protein, has been demonstrated in multiple human disorders, but it is yet to be discovered following acute coronary syndrome (ACS) in clinical practice. Therefore, this study aimed to investigate the relationship between circulating MFAP4 levels and coronary stenosis in ACS.

METHODS

We performed the study in 148 ACS subjects, including 75 ST-segment elevation myocardial infarction (STEMI), 27 non-ST-segment elevation myocardial infarction (non-STEMI) and 46 unstable angina (UA). Clinical variables were collected and Gensini and Syntax stenosis scoring systems were applied to assess the severity of coronary stenosis. Kaplan-Meier and logistic regression analysis were used to analyze the relationship between MFAP4 and the severity of coronary stenosis or ACS outcomes. Spearman analysis was used to describe the correlation between MFAP4 and clinical parameters.

RESULTS

Circulating MFAP4 levels were significantly decreased in the STEMI group (0.008 ng/ml) compared with the non-STEMI group (0.014 ng/ml) and UA group (0.019 ng/ml) (p < 0.001). After adjusting for confounding factors, we found that MFAP4 was an independent risk factor for STEMI (odds ratio = 0.395, 95% CI 0.174-0.895, p = 0.026). MFAP4 level was negatively correlated with Gensini score and Syntax score (r = - 0.311 and - 0.211, p < 0.001 and 0.01, respectively). Based on the MFAP4 level of 0.117 ng/ml, ACS patients were divided into two groups: the low-MFAP4 group (< 0.117 ng/ml, n = 60) and the high-MFAP4 group (≥ 0.117 ng/ml, n = 88). After the median follow-up of 165 days, Kaplan-Meier survival analysis revealed that the MACE-free rate was significantly lower in ACS patients with lower MFAP4 levels (p = 0.009).

CONCLUSIONS

MFAP4 has a potential as a biomarker for the degree of coronary stenosis in ACS. Confirmation of observations in larger cohorts and longer follow-up periods is warranted.

Deep learning magnetic resonance imaging predicts platinum sensitivity in patients with epithelial ovarian cancer

Objective

The aim of the study is to develop and validate a deep learning model to predict the platinum sensitivity of patients with epithelial ovarian cancer (EOC) based on contrast-enhanced magnetic resonance imaging (MRI).

Methods

In this retrospective study, 93 patients with EOC who received platinum-based chemotherapy (≥4 cycles) and debulking surgery at the Sun Yat-sen Memorial Hospital from January 2011 to January 2020 were enrolled and randomly assigned to the training and validation cohorts (2:1). Two different models were built based on either the primary tumor or whole volume of the abdomen as the volume of interest (VOI) within the same cohorts, and then a pre-trained convolutional neural network Med3D (Resnet 10 version) was transferred to automatically extract 1,024 features from two MRI sequences (CE-T1WI and T2WI) of each patient to predict platinum sensitivity. The performance of the two models was compared.

Results

A total of 93 women (mean age, 50.5 years ± 10.5 [standard deviation]) were evaluated (62 in the training cohort and 31 in the validation cohort). The AUCs of the whole abdomen model were 0.97 and 0.98 for the training and validation cohorts, respectively, which was better than the primary tumor model (AUCs of 0.88 and 0.81 in the training and validation cohorts, respectively). In k-fold cross-validation and stratified analysis, the whole abdomen model maintained a stable performance, and the decision function value generated by the model was a prognostic indicator that successfully discriminates high- and low-risk recurrence patients.

Conclusion

The non-manually segmented whole-abdomen deep learning model based on MRI exhibited satisfactory predictive performance for platinum sensitivity and may assist gynecologists in making optimal treatment decisions.

Genomic Landscape of RTK/RAS Pathway and Tumor Immune Infiltration as Prognostic Indicator of Lung Adenocarcinoma

The RTK/RAS pathway is an oncogenic signaling pathway for which many targeted drugs have been developed; however, survival remains poor. A combination of targeted therapy and immunotherapy has emerged as an option for improving cancer treatment responses. In this study, on the basis of the expression, survival, single nucleotide variation (SNV), copy number variation (CNV), and methylation data of lung adenocarcinoma (LUAD) from The Cancer Genome Atlas database, we comprehensively analyzed the genomic changes in the RTK/RAS pathway and their associations with tumor-infiltrating lymphocytes (TIL) and prognosis in LUAD to provide the genomics landscape of RTK/RAS with TIL and prognosis. We found that two rarely mutated genes, mitogen-activated protein kinase kinase 1 and insulin-like growth factor 1 receptor, were significantly associated with the worse survival of patients with LUAD. Patients with LUAD and co-mutation of KRAS proto-oncogene (KRAS) and neurofibromin 1 genes had worse survival, and the underlying mechanism could be insufficient for protein synthesis and intracellular signal deactivation. Methylation of the Rac family small GTPase 1 (RAC1) was associated with better survival. The SNVs of the top mutated genes, including epidermal growth factor receptor (12.7%), neurotrophic receptor tyrosine kinase 3 (7.8%), erb-b2 receptor tyrosine kinase 4 (8.5%), and KRAS (29.6%), were associated with T cell exhaustion in LUAD. To construct nomograms, we further screened the genes whose genomic changes were closely associated with survival and immune infiltration. The nomograms performed well in predicting disease-specific survival (DSS) with a concordance index of 0.7 (0.589, 0.811) and overall survival with a concordance index of 0.689 (0.603, 0.775) in test set; they also showed good correspondence between actual and ideal nomogram predictions. Tumor stage, RAC1 methylation, and type 1 regulatory T cells greatly contributed to DSS and OS nomograms. In summary, we provided a comprehensive genomic profile of the RTK/RAS pathway in LUAD and its association with immune cell infiltration and prognosis of LUAD. This profile would serve as a basis for developing better therapeutic strategies, improving patient prognosis, and understanding the mechanisms of immune disturbance from the perspective of oncogenic pathways of LUAD.

MFAP4-Mediated Effects in Elastic Fiber Homeostasis, Integrin Signaling and Cancer, and Its Role in Teleost Fish

Microfibrillar-associated protein 4 (MFAP4) is an extracellular matrix (ECM) protein belonging to the fibrinogen-related domain superfamily. MFAP4 is highly expressed in elastin-rich tissues such as lung, blood vessels and skin. MFAP4 is involved in organization of the ECM, regulating proper elastic fiber assembly. On the other hand, during pathology MFAP4 actively contributes to disease development and progression due to its interactions with RGD-dependent integrin receptors. Both tissue expression and circulating MFAP4 levels are associated with various disorders, including liver fibrosis and cancer. In other experimental models, such as teleost fish, MFAP4 appears to participate in host defense as a macrophage-specific innate immune molecule. The aim of this review is to summarize the accumulating evidence that indicates the importance of MFAP4 in homeostasis as well as pathological conditions, discuss its known biological functions with special focus on elastic fiber assembly, integrin signaling and cancer, as well as describe the reported functions of non-mammalian MFAP4 in fish. Overall, our work provides a comprehensive overview on the role of MFAP4 in health and disease.

Application of artificial intelligence in the diagnosis and prognostic prediction of ovarian cancer

In recent years, the wide application of artificial intelligence (AI) has dramatically improved the work efficiency of clinicians and reduced their workload. This review provides a glance at the latest advances in AI-assisted diagnosis and prognostic prediction of ovarian cancer (OC). We performed an advanced search in PubMed and IEEE/IET Electronic Library, and included 39 articles in this review. A comprehensive and objective criterion was built to assess the reliability and quality of all studies from four aspects: the size of datasets for model development, research design, the division of training sets and test sets, and the type of quantitative performance indicators. This review analyzed the construction of AI models, including data pre-processing methods, feature selection techniques, AI classifiers, or algorithms. Additionally, we compared the performance of these models built on different datasets, which may support researchers for further iteration and development of AI. Finally, we discussed the challenges and future directions for AI application in medicine.

KAZN as a diagnostic marker in ovarian cancer: a comprehensive analysis based on microarray, mRNA-sequencing, and methylation data

Background

Ovarian cancer (OC) is among the deadliest malignancies in women and the lack of appropriate markers for early diagnosis leads to poor prognosis in most cases. Previous studies have shown that KAZN is involved in multiple biological processes during development, such as cell proliferation, differentiation, and apoptosis, so defects or aberrant expression of KAZN might cause queer cell behaviors such as malignancy. Here we evaluated the KAZN expression and methylation levels for possible use as an early diagnosis marker for OC.

Methods

We used data from Gene Expression Omnibus (GEO) microarrays, The Cancer Genome Atlas (TCGA), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) to investigate the correlations between KAZN expression and clinical characteristics of OC by comparing methylation levels of normal and OC samples. The relationships among differentially methylated sites in the KAZN gene, corresponding KAZN mRNA expression levels and prognosis were analyzed.

Results

KAZN was up-regulated in ovarian epithelial tumors and the expression of KAZN was correlated with the patients’ survival time. KAZN CpG site cg17657618 was positively correlated with the expression of mRNA and the methylation levels were significantly differential between the group of stage “I and II” and the group of stage “III and IV”. This study also presents a new method to classify tumor and normal tissue in OC using DNA methylation pattern in the KAZN gene body region.

Conclusions

KAZN was involved in ovarian cancer pathogenesis. Our results demonstrate a new direction for ovarian cancer research and provide a potential diagnostic biomarker as well as a novel therapeutic target for clinical application.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09747-2.

The Value of the Stemness Index in Ovarian Cancer Prognosis

Ovarian cancer (OC) is one of the most common gynecological malignancies. It is associated with a difficult diagnosis and poor prognosis. Our study aimed to analyze tumor stemness to determine the prognosis feature of patients with OC. At this job, we selected the gene expression and the clinical profiles of patients with OC in the TCGA database. We calculated the stemness index of each patient using the one-class logistic regression (OCLR) algorithm and performed correlation analysis with immune infiltration. We used consensus clustering methods to classify OC patients into different stemness subtypes and compared the differences in immune infiltration between them. Finally, we established a prognostic signature by Cox and LASSO regression analysis. We found a significant negative correlation between a high stemness index and immune score. Pathway analysis indicated that the differentially expressed genes (DEGs) from the low- and high-mRNAsi groups were enriched in multiple functions and pathways, such as protein digestion and absorption, the PI3K-Akt signaling pathway, and the TGF-β signaling pathway. By consensus cluster analysis, patients with OC were split into two stemness subtypes, with subtype II having a better prognosis and higher immune infiltration. Furthermore, we identified 11 key genes to construct the prognostic signature for patients with OC. Among these genes, the expression levels of nine, including SFRP2, MFAP4, CCDC80, COL16A1, DUSP1, VSTM2L, TGFBI, PXDN, and GAS1, were increased in the high-risk group. The analysis of the KM and ROC curves indicated that this prognostic signature had a great survival prediction ability and could independently predict the prognosis for patients with OC. We established a stemness index-related risk prognostic module for OC, which has prognostic-independent capabilities and is expected to improve the diagnosis and treatment of patients with OC.

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

Background

Ovarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients.

Methods

In this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set.

Results

Forty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort.

Conclusion

These findings reveal a specific risk model that could serve as effective biomarkers to identify patients’ platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00969-3.

Five immune-related genes as diagnostic markers for endometriosis and their correlation with immune infiltration

Endometriosis (EMS) is a chronic disease that can cause dysmenorrhea, chronic pelvic pain, and infertility, among other symptoms. EMS diagnosis is often delayed compared to other chronic diseases, and there are currently no accurate, easily accessible, and non-invasive diagnostic tools. Therefore, it is important to elucidate the mechanism of EMS and explore potential biomarkers and diagnostic tools for its accurate diagnosis and treatment. In the present study, we comprehensively analyzed the differential expression, immune infiltration, and interactions of EMS-related genes in three Homo sapiens datasets. Our results identified 332 differentially expressed genes (DEGs) associated with EMS. Gene ontology analysis showed that these changes mainly focused on the positive regulation of endometrial cell proliferation, cell metabolism, and extracellular space, and EMS involved the integrin, complement activation, folic acid metabolism, interleukin, and lipid signaling pathways. The LASSO regression model was established using immune DEGs with an area under the curve of 0.783 for the internal dataset and 0.656 for the external dataset. Five genes with diagnostic value, ACKR1, LMNB1, MFAP4, NMU, and SEMA3C, were screened from M1 and M2 macrophages, activated mast cells, neutrophils, natural killer cells, follicular T helper cells, CD8⁺, and CD4⁺ cells. A protein-protein interaction network based on the immune DEGs was constructed, and ten hub genes with the highest scores were identified. Our results may provide a framework for the development of pathological molecular networks in EMS.

Gene Expression Subtyping Reveals Immune alterations:TCGA Database for Prognosis in Ovarian Serous Cystadenocarcinoma

Serous ovarian cancer is the most common and primary death type in ovarian cancer. In recent studies, tumor microenvironment and tumor immune infiltration significantly affect the prognosis of ovarian cancer. This study analyzed the four gene expression types of ovarian cancer in TCGA database to extract differentially expressed genes and verify the prognostic significance. Meanwhile, functional enrichment and protein interaction network analysis exposed that these genes were related to immune response and immune infiltration. Subsequently, we proved these prognostic genes in an independent data set from the GEO database. Finally, multivariate cox regression analysis revealed the prognostic significance of TAP1 and CXCL13. The genetic alteration and interaction network of these two genes were shown. Then, we established a nomogram model related to the two genes and clinical risk factors. This model performed well in Calibration plot and Decision Curve Analysis. In conclusion, we have obtained a list of genes related to the immune microenvironment with a better prognosis for serous ovarian cancer, and based on this, we have tried to establish a clinical prognosis model.

Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).

Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.

Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.

Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

Detailed Molecular Mechanism and Potential Drugs for COL1A1 in Carboplatin-Resistant Ovarian Cancer

Carboplatin resistance in ovarian cancer (OV) is a major medical problem. Thus, there is an urgent need to find novel therapeutic targets to improve the prognosis of patients with carboplatin-resistant OV. Accumulating evidence indicates that the gene COL1A1 (collagen type I alpha 1 chain) has an important role in chemoresistance and could be a therapeutic target. However, there have been no reports about the role of COL1A1 in carboplatin-resistant OV. This study aimed to establish the detailed molecular mechanism of COL1A1 and predict potential drugs for its treatment. We found that COL1A1 had a pivotal role in carboplatin resistance in OV by weighted gene correlation network analysis and survival analysis. Moreover, we constructed a competing endogenous RNA network (LINC00052/SMCR5-miR-98-COL1A1) based on multi-omics data and experiments to explore the upstream regulatory mechanisms of COL1A1. Two key pathways involving COL1A1 in carboplatin resistance were identified by co-expression analysis and pathway enrichment: the "ECM-receptor interaction" and "focal adhesion" Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, combining these results with those of cell viability assays, we proposed that ZINC000085537017 and quercetin were potential drugs for COL1A1 based on virtual screening and the TCMSP database, respectively. These results might help to improve the outcome of OV in the future.

Tumor-intrinsic and -extrinsic (immune) gene signatures robustly predict overall survival and treatment response in high grade serous ovarian cancer patients

In the present study, we developed a transcriptomic signature capable of predicting prognosis and response to primary therapy in high grade serous ovarian cancer (HGSOC). Proportional hazard analysis was performed on individual genes in the TCGA RNAseq data set containing 229 HGSOC patients. Ridge regression analysis was performed to select genes and develop multigenic models. Survival analysis identified 120 genes whose expression levels were associated with overall survival (OS) (HR = 1.49-2.46 or HR = 0.48-0.63). Ridge regression modeling selected 38 of the 120 genes for development of the final Ridge regression models. The consensus model based on plurality voting by 68 individual Ridge regression models classified 102 (45%) as low, 23 (10%) as moderate and 104 patients (45%) as high risk. The median OS was 31 months (HR = 7.63, 95% CI = 4.85-12.0, P < 1.0-10) and 77 months (HR = ref) in the high and low risk groups, respectively. The gene signature had two components: intrinsic (proliferation, metastasis, autophagy) and extrinsic (immune evasion). Moderate/high risk patients had more partial and non-responses to primary therapy than low risk patients (odds ratio = 4.54, P < 0.001). We concluded that the overall survival and response to primary therapy in ovarian cancer is best assessed using a combination of gene signatures. A combination of genes which combines both tumor intrinsic and extrinsic functions has the best prediction. Validation studies are warranted in the future.

The expression of AGGF1, FOXC2, and E-cadherin in esophageal carcinoma and their clinical significance

Angiogenic factor with G-patch and FHA domain 1 (AGGF1) is a newly initiator of angiogenesis. Forkhead box C2 (FOXC2) that is a member of the winged spiral transcription factor family plays an important role in epithelial-mesenchymal transition (EMT). Epithelial-cadherin (E-cad) that is an adhesion molecule is also involved in EMT. The purpose of this study is to investigate the expression of AGGF1, FOXC2, and E-cad in esophageal squamous cell carcinoma (ESCC) and their clinical significance.Immunohistochemistry was performed to investigate the expression of AGGF1, FOXC2, and E-cad in 170 ESCC specimens and corresponding normal esophageal mucosa tissues. Follow-up data was also collected.The positive rates of AGGF1 and FOXC2 expression were significantly higher in ESCC group when compared with the control group; the positive rate of E-cad expression was significantly lower in ESCC group when compared with the control group. Positive rates of AGGF1, FOXC2, and E-cad expression were significantly associated with grades of differentiation, tumor grades, lymph node metastasis stages, as well as tumor-node-metastasis stages. Kaplan-Meier analysis demonstrated that positive expression of AGGF1 or FOXC2 for ESCC patients had significantly unfavorably overall survival time when compared with patients with negative expression of AGGF1 or FOXC2; and positive expression of E-cad for ESCC patients had significantly longer overall survival time when compared with patients with negative expression of E-cad. Multivariate analysis indicated that AGGF1, FOXC2, and E-cad expression and tumor-node-metastasis stages were postoperative independent prognostic factors for ESCC patients.AGGF1, FOXC2, and E-cad may be considered promising biomarkers of ESCC patients' prognosis.

Nucleosome Assembly Protein 1-Like 3 Enhances Cisplatin Resistance of Ovarian Cancer Cell by Activating Transforming Growth Factor-Beta Pathway

Chemotherapy resistance is one of the main reasons for tumor-related death. In particular, ovarian cancer patients often acquire drug resistance after chemotherapy. In this study, we found that the histone chaperone, nucleosome assembly protein 1-like 3 (NAP1L3), was significantly upregulated in tissues with cisplatin resistance compared with cisplatin-sensitive tissues. Patients with high NAP1L3 levels had poor prognosis, suggesting that NAP1L3 might regulate ovarian cancer resistance. Colony formation and terminal deoxynulceotidyl transferase nick-end-labeling (TUNEL) assays showed cells with high NAP1L3 had high cisplatin resistance, whereas cells with low NAP1L3 had poor cisplatin resistance. NAP1L3 overexpression significantly increased cisplatin resistance, whereas NAP1L3 knockdown significantly reduced cisplatin resistance, suggesting that NAP1L3 promoted cisplatin resistance. Mechanistically, gene set enrichment analysis and luciferase reporter assays showed that NAP1L3 regulated the transforming growth factor-beta (TGF-β) pathway. NAP1L3 overexpression increased the phosphorylation and nuclear translocation of SMAD family member 2 (SMAD2) and SMAD3, confirming that NAP1L3 activated the TGF-β pathway. Therefore, NAP1L3 might represent a novel target to overcome ovarian cancer chemoresistance.

Microfibril-associated glycoprotein 4 (Mfap4) regulates haematopoiesis in zebrafish

Microfibril-associated glycoprotein 4 (MFAP4) is an extracellular matrix protein belonging to the fibrinogen-related protein superfamily. MFAP4 is produced by vascular smooth muscle cells and is highly enriched in the blood vessels of the heart and lung, where it is thought to contribute to the structure and function of elastic fibers. Genetic studies in humans have implicated MFAP4 in the pathogenesis of Smith-Magenis syndrome, in which patients present with multiple congenital abnormalities and mental retardation, as well as in the severe cardiac malformation left-sided congenital heart disease. Comprehensive genetic analysis of the role of MFAP4 orthologues in model organisms during development and tissue homeostasis is however lacking. Here, we demonstrate that zebrafish mfap4 transcripts are detected embryonically, resolving to the macrophage lineage by 24 h post fertilization. mfap4 null mutant zebrafish are unexpectedly viable and fertile, without ostensible phenotypes. However, tail fin amputation assays reveal that mfap4 mutants have reduced numbers of macrophages, with a concomitant increase in neutrophilic granulocytes, although recruitment of both cell types to the site of injury was unaffected. Molecular analyses suggest that loss of Mfap4 alters the balance between myeloid and lymphoid lineages during both primitive and definitive haematopoiesis, which could significantly impact the downstream function of the immune system.

Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma

The molecular mechanisms of adrenocortical carcinoma (ACC) carcinogenesis and progression remain unclear. In the present study, three microarray datasets from the Gene Expression Omnibus database were screened, which identified a total of 96 differentially expressed genes (DEGs). A protein-protein interaction network (PPI) was established for these DEGs and module analysis was performed using STRING and Cytoscape. A total of eight hub genes were identified from the most significant module; namely, calponin 1 (CNN1), myosin light chain kinase (MYLK), cysteine and glycine rich protein 1 (CSRP1), myosin heavy chain 11 (MYH11), fibulin extracellular matrix protein 2 (EFEMP2), fibulin 1 (FBLN1), microfibril associated protein 4 (MFAP4) and fibulin 5 (FBLN5). The biological functions of these hub genes were analyzed using the DAVID online tool. Changes in the expression of hub genes did not affect overall survival; however, downregulated EFEMP2 decreased disease-free survival. CSRP1 and MFAP4 expression levels were associated with adverse clinicopathological features. In conclusion, although all eight hub genes were downregulated in ACC, they appeared to have important functions in ACC carcinogenesis and progression. Identification of these genes complements the genetic expression profile of ACC and provides insight for the diagnosis, treatment and prognosis of ACC.

Molecular structure and function of microfibrillar-associated proteins in skeletal and metabolic disorders and cancers

Microfibrillar-associated proteins (MFAPs) are extracellular matrix glycoproteins, which play a role in microfibril assembly, elastinogenesis, and tissue homeostasis. MFAPs consist of five subfamily members, including MFAP1, MFAP2, MFAP3, MFAP4, and MFAP5. Among these, MFAP2 and MFAP5 are most closely related, and exhibit very limited amino acid sequence homology with MFAP1, MFAP3, and MFAP4. Gene expression profiling analysis reveals that MFAP2, MFAP5, and MFAP4 are specifically expressed in osteoblastic like cells, whereas MFAP1 and MFAP3 are more ubiquitously expressed, indicative of their diverse role in the tropism of tissues. Molecular structural analysis shows that each MFAP family member has distinct features, and functional evidence reveals discrete purposes of individual MFAPs. Animal studies indicate that MFAP2-deficient mice exhibit progressive osteopenia with elevated receptor activator of NF-κB ligand (RANKL) expression, whereas MFAP5-deficient mice are neutropenic, and MFAP4-deficient mice displayed emphysema-like pathology and the impaired formation of neointimal hyperplasia. Emerging data also suggest that MFAPs are involved in cancer progression and fat metabolism. Further understanding of tissue-specific pathophysiology of MFAPs might offer potential novel therapeutic targets for related diseases, such as skeletal and metabolic disorders, and cancers.

Chemoresistance-associated alternative splicing signatures in serous ovarian cancer

Primary platinum-based chemoresistance occurs in ~30% of patients with serous ovarian cancer. Chemoresistance is the main cause of disease recurrence, and accurate predictors to identify these patients with chemoresistance are required. Alternative splicing (AS) is a post-transcriptional modification process that is altered in cancer. A possible association between AS and chemoresistance is unclear and needs to be studied comprehensively in ovarian cancer. In the present study, RNA-sequencing data and clinical information for 320 patients with ovarian serous cystadenocarcinoma (OV) were downloaded from The Cancer Genome Atlas (TCGA) database. Splicing events were determined using the TCGA SpliceSeq tool. Seven types of AS events were identified. Univariate and multivariate logistic analyses were performed, and predictive models for OV chemoresistance were established, as well as a splicing network. A total of 22,036 AS events were identified in 7,404 genes, with 915 AS events detected in 677 genes that were significantly associated with chemoresistance in patients with OV. A receiver operating characteristic (ROC) curve was constructed for resistance predictive models composed of the most significant AS events. The area under the ROC curve was 0.931, indicating strong and efficient prediction of chemoresistance. Additionally, the high-risk score was associated with shorter overall survival. The splicing correlation network suggested a potential role of splicing factors in chemoresistance. In summary, the present study created a powerful predictor for primary platinum-based chemoresistance in patients with OV, identified splicing networks that could be involved in potential mechanisms of chemoresistance and provided potential targets to overcome chemoresistance.

Angiogenic Factor with G Patch and FHA Domains 1 (AGGF1) Acts as Diagnostic Biomarker and Adverse Prognostic Factor of Hepatocellular Carcinoma (HCC): Evidence from Bioinformatic Analysis

Background

Angiogenic factor with G patch and FHA domains 1 (AGGF1) is a novel identified initiator of angiogenesis through promoting the proliferation of endothelial cells. The continuous angiogenesis plays a key role in the growth, invasion, and metastasis of hepatocellular carcinoma (HCC), while the diagnostic and prognostic roles of AGGF1 for HCC need to be further studied.

Material/Methods

The mRNA sequencing datasets and clinical features of HCC patients were extracted from The Cancer Genome Atlas database. The relationship between clinical features and AGGF1 expression was analyzed by Wilcoxon test. Further validation explorations were carried out using online database Oncomine. The diagnostic receiver operating characteristic curves of AGGF1 and alpha-fetoprotein were compared to examine the diagnostic efficacy of AGGF1. Survival analysis and Gene Set Enrichment Analysis were performed to explore the prediction value and potential mechanism of AGGF1 dysregulation in HCC.

Results

Comprehensive overexpression of AGGF1 was observed in HCC, correlating with poor overall survival. Upregulated level of AGGF1 was statistically associated with poor differentiated histological grade, advanced cancer stage and T classification. AGGF1 was a more effective diagnostic marker than alpha-fetoprotein in HCC. Several important pathways related to HCC including pathway in cancer and P53 signaling pathway were differentially enriched in the high AGGF1 expression phenotype.

Conclusions

AGGF1 was a potential diagnostic and prognostic marker for poor clinical outcomes in HCC patients. Moreover, vital pathways regulated by AGGF1 in HCC may include regulation of autophagy, Wnt signaling pathway, pathway in cancer, cell cycle, and P53 signaling pathway.

Elevated expression of AGGF1 predicts poor prognosis and promotes the metastasis of colorectal cancer

Background

Angiogenic factor with G-patch and FHA domains 1 (AGGF1) can promote angiogenesis and increasing evidence has highlighted the important roles of AGGF1 in tumorigenesis. However, the differential expression as well as the biological functions of AGGF1 in colorectal cancer (CRC) remain to be established. The purpose of the present study is therefore to identify the effect of AGGF1 on prognosis and metastasis in CRC patients.

Methods

The expression level of AGGF1 in CRC was examined by qPCR, western blot and immunohistochemistry in a tissue microarray containing 236 CRC specimens and paired normal mucosae. And the effect of AGGF1 on CRC cell malignance was investigated in our established stable AGGF1 upregulated and knockdown CRC cell lines.

Results

The expression level of AGGF1 in CRC tissue was not significantly different to that in adjacent normal mucosa at the mRNA level. However, at the protein level, AGGF1 expression in CRC tissues was significantly higher than in paired normal mucosa, which showed a clear association with TNM stage, AJCC stage, vascular invasion, and differentiation. Further, we revealed an apparent correlation between AGGF1 expression and poorer disease-free survival and overall survival of CRC patients. In addition, we discovered that AGGF1 significantly promoted CRC cell wound healing, migration, and invasion in vitro and distant metastasis in vivo.

Conclusions

Our study demonstrates the aberrant overexpression of AGGF1 in CRC and provides a basis on which to explore the application of AGGF1 as a potential therapeutic target for CRC patients, especially for CRC patients with distant metastasis.