The prognostic significance and immune characteristics of bone morphogenetic proteins (BMPs) family: A pan-cancer multi-omics analysis.

BACKGROUND: Bone morphogenetic proteins (BMPs) are a group of cancer-related proteins vital for development and progression of certain cancer types. Nevertheless, function of BMP family in pan-cancer was not detailedly researched. OBJECTIVE: Investigating expression pattern and prognostic value of the BMPs family (BMP1-8A and BMP8B) expression across multiple cancer types. METHODS: Our research integrated multi-omics data for exploring potential associations between BMPs expression and prognosis, clinicopathological characteristics, copy number or somatic mutations, immune characteristics, tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint genes and drug sensitivity in The Cancer Genome Atlas (TCGA) tumors. Furthermore, association of BMPs expression and immunotherapy effectiveness was investigated in some confirmatory cohorts (GSE111636, GSE78220, GSE67501, GSE176307, IMvigor210 and mRNA sequencing data from currently undergoing TRUCE01 clinical research included), and biological function and potential signaling pathways of BMPs in bladder cancer (BCa) was explored via Gene Set Enrichment Analysis (GSEA). Eventually, immune infiltration analysis was done via BMPs expression, copy number or somatic mutations in BCa, as well as validation of the expression levels by reverse transcription-quantitative PCR and western blot, and in vitro functional experiments of BMP8A. RESULTS: Discoveries displayed BMPs expression was related to prognosis, clinicopathological characteristics, mutations, TME, TMB, MSI and immune checkpoint genes of TCGA tumors. Anticancer drug sensitivity analysis displayed BMPs were associated with various drug sensitivities. What's more, it was discovered that expression level of certain BMP family members related to objective response to immunotherapy. By GSEA, we discovered multiple immune-associated functions and pathways were enriched. Immune infiltration analysis on BCa also displayed significant associations among BMPs copy number variations, mutation status and infiltration level of diverse immune cells. Furthermore, differential expression validation and in vitro phenotypic experiment indicated that BMP8A significantly promoted BCa cell proliferation, migration and invasion. CONCLUSIONS: Current results confirmed significance of both BMPs expression and genomic alteration in the prognosis and treatment of diverse cancer types, and suggested that BMPs may be vital for BCa and can possibly be utilized as biomarkers for immunotherapy.

The prognostic values and immune characteristics of polo-like kinases (PLKs) family: A pan-cancer multi-omics analysis.

Background: In the realm of tumor-targeted therapeutics, Polo-like kinases (PLKs) are a significant group of protein kinases that were found recently as being related to tumors. However, the significance of PLKs in pan-cancer remains systematically studied. Methods and materials: We integrated multi-omics data to comprehensively investigate the expression patterns of the PLK family across various cancer types. Subsequently, study examined the associations between tumor mutation burden (TMB), microsatellite instability (MSI), immune subtype classification, immune infiltration, tumor microenvironment scores, immune checkpoint gene expression, and the PLKs expression profiles within various tumor types. Furthermore, using our mRNA sequencing data (TRUCE01) and four bladder cancer (BLCA) cohorts (GSE111636, GSE176307, and IMvigor210), We examined the correlation between the expression level of PLK and immunotherapy effectiveness. Next, Gene set enrichment analysis (GSEA) was evaluated to find that potentially enriched PLK signaling pathways. Utilizing TIMER 2.0, we conducted an immune infiltration analysis underlying transcriptome expression, copy number variations (CNV), or somatic mutations of PLKs in BLCA. Finally, mRNA expression validation of PLK1/3/4 by real-time PCR within 10 paired BLCA tissues, protein expression verification through the Human Protein Atlas (HPA), and PLK4 in vitro cytological studies have been employed in BLCA. Results: The expression of most of the PLK family members exhibits variation between cancerous tissues and adjacent normal tissues across various cancer species. Furthermore, the expression of PLKs demonstrates a significant association with immunotyping, infiltration of immune cell, tumor mutational burden (TMB), microsatellite instability (MSI), immunological checkpoint gene activity and therapeutic effectiveness in pan-tumor tissues. Additional investigation into the correlation between the PLK family and BLCA has revealed that the expression of the PLK genes holds substantial significance in the biological processes of BLCA. Furthermore, a notable association has been observed between the copy number variation, variant status, and the degree of certain immunological cell infiltration. Of note, the expression validation and in vitro phenotypic experiments have demonstrated that PLK4 has a significant function in promoting the BLCA cell proliferation, migration, and invasion. Conclusion: Collectively, based on various databases, our results highlight the involvement of PLK gene family in the formation of different types of tumors and identify PLK-related genes that may be used for therapy.

Clinical effectiveness of a multitarget urine DNA test for urothelial carcinoma detection: a double-blinded, multicenter, prospective trial.

Urine-based testing is promising for noninvasive diagnosis of urothelial carcinoma (UC) but has suboptimal sensitivity for early-stage tumors. Herein, we developed a multitarget urine tumor DNA test, UI-Seek, for UC detection and evaluated its clinical feasibility. The prediction model was developed in a retrospective cohort (n = 382), integrating assays for FGFR3 and TERT mutations and aberrant ONECUT2 and VIM methylation to generate a UC-score. The test performance was validated in a double-blinded, multicenter, prospective trial (n = 947; ChiCTR2300076543) and demonstrated a sensitivity of 91.37% and a specificity of 95.09%. The sensitivity reached 75.81% for low-grade Ta tumors and exceeded 93% in high-grade Ta and higher stages (T1 to T4). Simultaneous identification of both bladder and upper urinary tract tumors was enabled with sensitivities exceeding 90%. No significant confounding effects were observed regarding benign urological diseases or non-UC malignancies. The test showed improved sensitivities over urine cytology, the NMP22 test, and UroVysion FISH alongside comparable specificities. The single-target accuracy was greater than 98% as confirmed by Sanger sequencing. Post-surgery UC-score decreased in 97.7% of subjects. Overall, UI-Seek demonstrated robust performance and considerable potential for the early detection of UC.

An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate.

Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using genomic data to identify disease-associated genes to estimate cancer mortality risk remains challenging regarding to computational efficiency and risk integration. For determining mortality-related genes, we propose an information fusion system based on a fuzzy system to fuse the numerous deep-learning-based risk scores, consider the significance of features related to time-varying effects and risk stratifications, and interpret the directional relationship and interaction between outcome and predictors. Fuzzy rules were implemented to integrate the considerations mentioned above by merging all the risk score models to achieve advanced risk estimation. The genomic data of head and neck squamous cell carcinoma (HNSCC) were used to evaluate the performance of the proposed computational approach. The results indicated that the proposed computational approach exhibited optimal ability to identify mortality risk-related genes in HNSCC patients. The results also suggest that HNSCC mortality is associated with cancer inflammatory response, the interleukin-17A signaling pathway, stellate cell activation, and the extracellular-regulated protein kinase five signaling pathway, which might offer new therapeutic targets HNSCC through immunologic or antiangiogenic mechanisms. The proposed information fusion system can promote the determination of high-risk genes related to cancer mortality. This study contributes a valid cancer mortality risk estimate that can identify mortality-related genes.

Withdrawal notice to: "Pan-cancer analyses of clinical prognosis, immune infiltration, and immunotherapy efficacy for TRPV family using multi-omics data" [Heliyon 9, (2023) 16897].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e16897.].

Pan-cancer evidence of prognosis, immune infiltration, and immunotherapy efficacy for annexin family using multi-omics data.

The annexin superfamily (ANXA) is made up of 12 calcium (Ca2+) and phospholipid binding protein members that have a high structural homology and play a key function in cancer cells. However, little research has been done on the annexin family's function in pan-cancer. We examined the ANXA family's expression in various tumors through public databases using bioinformatics analysis, assessed the differences in ANXA expression between tumor and normal tissues in pan-cancer, and then investigated the relationship between ANXA expression and patient survival, prognosis, and clinicopathologic traits. Additionally, we investigated the relationships among TCGA cancers' mutations, tumor mutation burden (TMB), microsatellite instability (MSI), immunological subtypes, immune infiltration, tumor microenvironment, immune checkpoint genes, chemotherapeutics sensitivity, and ANXAs expression. cBioPortal was also used to uncover pan-cancer genomic anomalies in the ANXA family, study relationships between pan-cancer ANXA mRNA expression and copy number or somatic mutations, and assess the prognostic values of these variations. Moreover, we investigated the relationship between ANXAs expression and effectiveness of immunotherapy in multiple cohorts, including one melanoma (GSE78220), one renal cell carcinoma (GSE67501), and three bladder cancer cohorts (GSE111636, IMvigor210 and our own sequencing dataset (TRUCE-01)), and further analyzed the changes of ANXAs expression before and after treatment (tislelizumab combined with nab-paclitaxel) of bladder cancer. Then, we explored the biological function and potential signaling pathway of ANXAs using gene set enrichment analysis (GSEA), and first conducted immune infiltration analysis with ANXAs family genes expression, copy number, or somatic mutations of bladder cancer by TIMER 2.0. Most cancer types and surrounding normal tissues expressed ANXA differently. ANXA expression was linked to patient survival, prognosis, clinicopathologic features, mutations, TMB, MSI, immunological subtypes, tumor microenvironment, immune cell infiltration, and immune checkpoint gene expression in 33 TCGA cancers, with ANXA family members varied. The anticancer drug sensitivity analysis showed that ANXAs family members were significantly related to a variety of drug sensitivities. In addition, we also discovered that the expression level of ANXA1/2/3/4/5/7/9/10 was positively or negatively correlated with objective responses to anti-PD-1/PD-L1 across multiple immunotherapy cohorts. The immune infiltration analysis of bladder cancer further showed the significant relationships between ANXAs copy number variations or mutation status, and infiltration level of different immune cells. Overall, our analyses confirm the importance of ANXAs expression or genomic alterations in prognosis and immunological features of various cancer and identified ANXA-associated genes that may serve as potential therapeutic targets.

Pan-cancer analyses of clinical prognosis, immune infiltration, and immunotherapy efficacy for TRPV family using multi-omics data.

Background: Transient receptor potential cation channel subfamily V (TRPV) play an essential in cancer initiation, progression, and treatment. TRPV expression alteration are shown relate to multiple cancers prognosis and treatment of cancers but are less-studied in pan-cancer. In this study, we characterize the clinical prediction value of TRPV at pan-cancer level. Methods: Several databases were used to examine the transcript expression difference in tumor vs. normal tissue, copy-number variant (CNV) and single nucleotide polymorphisms (SNP) mutation of each TRPV members in pan-cancer, including The Cancer Genome Atlas (TCGA) and cBioPortal. We performed K-M survival curve and univariate Cox regression analyses to identify survival and prognosis value of TRPV. CellMiner were selected to explore drug sensitivity. We also analyzed association between tumor mutation burden (TMB), microsatellite instability (MSI), tumor immune microenvironment and TRPV family genes expression. Moreover, we investigated the relationship between TRPVs expression and effectiveness of immunotherapy in multiple cohorts, including one melanoma (GSE78220), one renal cell carcinoma (GSE67501), and three bladder cancer cohorts (GSE111636, IMvigor210, GSE176307 and our own sequencing dataset (TRUCE-01)), and further analyzed the changes of TRPVs expression before and after treatment (tislelizumab combined with nab-paclitaxel) of bladder cancer. Next, we made a special effort to investigate and study biological functions of TRPV in bladder cancer using gene set enrichment analysis (GSEA), and conducted immune infiltration analysis with TRPVs family genes expression, copy number or somatic mutations of bladder cancer by TIMER 2.0. Finally, real-time PCR and protein expression validation of TRPVs within 10 paired cancer and para-carcinoma tissue samples, were also performed in bladder cancer. Results: Only TRPV2 expression was lower in most cancer types among TRPV family genes. All TRPVs were correlated with survival changes. Amplification was the significant gene alternation in all TRPVs. Next, analysis between TRPVs and clinical traits showed that TRPVs were related to pathologic stage, TNM stage and first course treatment outcome. Moreover, TRPV expression was highly correlated with MSI and TMB. Immunotherapy is a research hotspot at present, our result showed the significant association between TRPVs expression and immune infiltration indicated that TRPV expression alternation could be used to guide prognosis. In addition, we also discovered that the expression level of TRPV1/2/3/4/6 was positively or negatively correlated with objective responses to anti-PD-1/PD-L1 across multiple immunotherapy cohort. Further analysis of drug sensitivity showed the value to treatment. Based on the above analysis, we next focused on TRPV family in bladder cancer. The result demonstrated TRPV also played an important role in bladder cancer. Finally, qPCR assay verified our analysis in bladder cancer. Conclusion: Our study firstly revealed expression and genome alternation of TRPV in pan-cancer. TRPV could be used to predict prognosis or instructing treatment of human cancers, especially bladder cancer.

Comprehensive characterization of endoplasmic reticulum stress in bladder cancer revealing the association with tumor immune microenvironment and prognosis.

Background: This study constructs a molecular subtype and prognostic model of bladder cancer (BLCA) through endoplasmic reticulum stress (ERS) related genes, thus helping to clinically guide accurate treatment and prognostic assessment. Methods: The Bladder Cancer (BLCA) gene expression data was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We clustered by ERS-related genes which obtained through GeneCards database, results in the establishment of a new molecular typing of bladder cancer. Further, we explored the characteristics of each typology in terms of immune microenvironment, mutations, and drug screening. By analyzing the ERS-related genes with univariate Cox, LASSO and multivariate Cox analyses, we also developed the four-gene signature, while validating the prognostic effect of the model in GSE32894 and GSE13507 cohorts. Finally, we evaluated the prognostic value of the clinical data in the high and low ERS score groups and constructed a prognostic score line graph by Nomogram. Results: We constructed four molecular subtypes (C1- C4) of bladder cancer, in which patients with C2 had a poor prognosis and those with C3 had a better prognosis. The C2 had a high degree of TP53 mutation, significant immune cell infiltration and high immune score. In contrast, C3 had a high degree of FGFR3 mutation, insignificant immune cell infiltration, and reduced immune checkpoint expression. After that, we built ERS-related risk signature to calculate ERS score, including ATP2A3, STIM2, VWF and P4HB. In the GSE32894 and GSE13507, the signature also had good predictive value for prognosis. In addition, ERS scores were shown to correlate well with various clinical features. Finally, we correlated the ERS clusters and ERS score. Patients with high ERS score were more likely to have the C2 phenotype, while patients with low ERS score were C3. Conclusion: In summary, we identified four novel molecular subtypes of BLCA by ERS-related genes which could provide some new insights into precision medicine. Prognostic models constructed from ERS-related genes can be used to predict clinical outcomes. Our study contributes to the study of personalized treatment and mechanisms of BLCA.

Construction and validation of a bladder cancer risk model based on autophagy-related genes.

Autophagy has an important association with tumorigenesis, progression, and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagy-related genes, which further illustrates the value of genes associated with autophagy in the treatment of BC. We first downloaded human autophagy-associated genes and BC datasets from Human Autophagy Database and The Cancer Genome Atlas (TCGA) database, and finally obtained differential prognosis-associated genes for autophagy by univariate regression analysis and differential analysis of cancer versus normal tissues. Subsequently, we downloaded two datasets from Gene Expression Omnibus (GEO), GSE31684 and GSE15307, to expand the total number of samples. Based on these genes, we distinguished the molecular subtypes (C1, C2) and gene classes (A, B) of BC by consistent clustering analysis. Using the genes merged from TCGA and the two GEO datasets, we conducted least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to obtain risk genes and construct autophagy-related risk prediction models. The accuracy of this risk prediction model was assessed by receiver operating characteristic (ROC) and calibration curves, and then nomograms were constructed to predict the survival of bladder cancer patients at 1, 3, and 5 years, respectively. According to the median value of the risk score, we divided BC samples into the high- and low-risk groups. Kaplan-Meier (K-M) survival analysis was performed to compare survival differences between subgroups. Then, we used single sample gene set enrichment analysis (ssGSEA) for immune cell infiltration abundance, immune checkpoint genes, immunotherapy response, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and tumor mutation burden (TMB) analysis for different subgroups. We also applied quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) techniques to verify the expression of these six genes in the model. Finally, we chose the IMvigor210 dataset for external validation. Six risk genes associated with autophagy (SPOCD1, FKBP10, NAT8B, LDLR, STMN3, and ANXA2) were finally screened by LASSO regression algorithm and multivariate Cox regression analysis. ROC and calibration curves showed that the model established was accurate and reliable. Univariate and multivariate regression analyses were used to verify that the risk model was an independent predictor. K-M survival analysis indicated that patients in the high-risk group had significantly worse overall survival than those in the low-risk group. Analysis by algorithms such as correlation analysis, gene set variation analysis (GSVA), and ssGSEA showed that differences in immune microenvironment, enrichment of multiple biologically active pathways, TMB, immune checkpoint genes, and human leukocyte antigens (HLAs) were observed in the different risk groups. Then, we constructed nomograms that predicted the 1-, 3-, and 5-year survival rates of different BC patients. In addition, we screened nine sensitive chemotherapeutic drugs using the correlation between the obtained expression status of risk genes and drug sensitivity results. Finally, the external dataset IMvigor210 verified that the model is reliable and efficient. We established an autophagy-related risk prognostic model that is accurate and reliable, which lays the foundation for future personalized treatment of bladder cancer.

A novel model based on immune-related genes for differentiating biliary atresia from other cholestatic diseases.

PURPOSE: Based on a public gene expression database, this study established the immune-related genetic model that distinguished BA from other cholestasis diseases (DC) for the first time. We explored the molecular mechanism of BA based on the gene model. METHODS: The BA microarray dataset GSE46960, containing BA, other cause of intrahepatic cholestasis than biliary atresia and normal liver gene expression data, was downloaded from the Gene Expression Omnibus (GEO) database. We performed a comprehensive bioinformatics analysis to establish and validate an immune-related gene model and subsequently identified hub genes as biomarkers associated with the molecular mechanisms of BA. To assess the model's performance for separating BA from other cholestasis diseases, we used receiver operating characteristic (ROC) curves and the area under the curve (AUC) of the ROC. Independent datasets GSE69948 and GSE122340 were used for the validation process. RESULTS: The model was built using eight immune-related genes, including EDN1, HAMP, SAA1, SPP1, ANKRD1, MMP7, TACSTD2, and UCA1. In the GSE46960 and validation group, it presented excellent results, and the prediction accuracy of BA in comparison to other cholestasis diseases was good. Functional enrichment analysis revealed significant immunological differences between BA and other cholestatic diseases. Finally, we found that the TNFα-NF-κB pathway is associated with EDN1 gene expression and may explain fibrosis progression, which may become a new therapeutic target. CONCLUSION: In summary, we have successfully constructed an immune-related gene model that can distinguish BA from other cholestatic diseases, while identifying the hub gene. Our exploration of immune genes provides new clues for the early diagnosis, molecular mechanism, and clinical treatment of biliary atresia.

Identification of a dysregulated CircRNA-associated gene signature for predicting prognosis, immune landscape, and drug candidates in bladder cancer.

Increasing evidences have demonstrated that circular RNA (circRNAs) plays a an essential regulatory role in initiation, progression and immunotherapy resistance of various cancers. However, circRNAs have rarely been studied in bladder cancer (BCa). The purpose of this research is to explore new circRNAs and their potential mechanisms in BCa. A novel ceRNA-regulated network, including 87 differentially expressed circRNAs (DE-circRNAs), 126 DE-miRNAs, and 217 DE-mRNAs was constructed to better understanding the biological processes using Cytoscape 3.7.1 based on our previously high-throughput circRNA sequencing and five GEO datasets. Subsequently, five randomly selected circRNAs (upregulated circ_0001681; downregulated circ_0000643, circ_0001798, circ_0006117 and circ_0067900) in 20 pairs of BCa and paracancerous tissues were confirmed using qRT-PCR. Functional analysis results determined that 772 GO functions and 32 KEGG pathways were enriched in the ceRNA network. Ten genes (PFKFB4, EDNRA, GSN, GAS1, PAPPA, DTL, TGFBI, PRSS8, RGS1 and TCF4) were selected for signature construction among the ceRNA network. The Human Protein Atlas (HPA) expression of these genes were consistent with the above sequencing data. Notably, the model was validated in multiple external datasets (GSE13507, GSE31684, GSE48075, IMvigor210 and GSE32894). The immune-infiltration was evaluated by 7 published algorithms (i.e., TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL and EPIC). Next, Correlations between riskscore or risk groups and clinicopathological data, overall survival, recognized immunoregulatory cells or common chemotherapeutic agents of BCa patients were performed using wilcox rank test, chi-square test, cox regression and spearman's correlation analysis; and, these results are significant. According to R package "GSVA" and "clusterProfiler", the most significantly enriched HALLMARK and KEGG pathway was separately the 'Epithelial Mesenchymal Transition' and 'Ecm Receptor Interaction' in the high- vs. low-risk group. Additionally, the functional experiments in vitro also revealed that the overexpression of has_circ_0067900 significantly impaired the proliferation, migration, and invasion capacities of BCa cells. Collectively, the results of the current study provide a novel landscape of circRNA-associated ceRNA-regulated network in BCa. The ceRNA-associated gene model which was constructed presented a high predictive performance for the prognosis, immunotherapeutic responsiveness, and chemotherapeutic sensitivity of BCa. And, has_circ_0067900 was originally proposed as tumor suppressor for patients with BCa.

MOAI: a multi-outcome interaction identification approach reveals an interaction between vaspin and carcinoembryonic antigen on colorectal cancer prognosis.

Identifying and characterizing the interaction between risk factors for multiple outcomes (multi-outcome interaction) has been one of the greatest challenges faced by complex multifactorial diseases. However, the existing approaches have several limitations in identifying the multi-outcome interaction. To address this issue, we proposed a multi-outcome interaction identification approach called MOAI. MOAI was motivated by the limitations of estimating the interaction simultaneously occurring in multi-outcomes and by the success of Pareto set filter operator for identifying multi-outcome interaction. MOAI permits the identification for the interaction of multiple outcomes and is applicable in population-based study designs. Our experimental results exhibited that the existing approaches are not effectively used to identify the multi-outcome interaction, whereas MOAI obviously exhibited superior performance in identifying multi-outcome interaction. We applied MOAI to identify the interaction between risk factors for colorectal cancer (CRC) in both metastases and mortality prognostic outcomes. An interaction between vaspin and carcinoembryonic antigen (CEA) was found, and the interaction indicated that patients with CRC characterized by higher vaspin (≥30%) and CEA (≥5) levels could simultaneously increase both metastases and mortality risk. The immunostaining evidence revealed that determined multi-outcome interaction could effectively identify the difference between non-metastases/survived and metastases/deceased patients, which offers multi-prognostic outcome risk estimation for CRC. To our knowledge, this is the first report of a multi-outcome interaction associated with a complex multifactorial disease. MOAI is freely available at https://sites.google.com/view/moaitool/home.

Effect of Baseline Characteristics and Tumor Burden on Vaspin Expression and Progressive Disease in Operable Colorectal Cancer.

Colorectal cancer is a highly heterogeneous malignancy in the Asian population, and it is considered an important prognostic factor for baseline characteristics, tumor burden, and tumor markers. This study investigated the effect of baseline characteristics and tumor burden on tumor marker expression and progressive disease in colorectal cancer by using partial least squares variance-based path modeling (PLS-PM). PLS-PM can be used to evaluate the complex relationship between prognostic variables and progressive disease status with a small sample of measurements and structural models. A total of 89 tissue samples of colorectal cancer were analyzed. Our results suggested that the expression of visceral adipose tissue-derived serpin (vaspin) is a potential indicator of colorectal cancer progression and may be affected by baseline characteristics such as age, sex, body mass index, and diabetes mellitus. Moreover, according to the characteristics of tumor burden, the expression of vaspin was generally higher in each progressive disease patient. The overall findings suggest that vaspin is a potential indicator of the progressive disease and may be affected by the baseline characteristics of patients.

A comparative analysis of chaotic particle swarm optimizations for detecting single nucleotide polymorphism barcodes.

OBJECTIVE: Evolutionary algorithms could overcome the computational limitations for the statistical evaluation of large datasets for high-order single nucleotide polymorphism (SNP) barcodes. Previous studies have proposed several chaotic particle swarm optimization (CPSO) methods to detect SNP barcodes for disease analysis (e.g., for breast cancer and chronic diseases). This work evaluated additional chaotic maps combined with the particle swarm optimization (PSO) method to detect SNP barcodes using a high-dimensional dataset. METHODS AND MATERIAL: Nine chaotic maps were used to improve PSO method results and compared the searching ability amongst all CPSO methods. The XOR and ZZ disease models were used to compare all chaotic maps combined with PSO method. Efficacy evaluations of CPSO methods were based on statistical values from the chi-square test (χ2). RESULTS: The results showed that chaotic maps could improve the searching ability of PSO method when population are trapped in the local optimum. The minor allele frequency (MAF) indicated that, amongst all CPSO methods, the numbers of SNPs, sample size, and the highest χ2 value in all datasets were found in the Sinai chaotic map combined with PSO method. We used the simple linear regression results of the gbest values in all generations to compare the all methods. Sinai chaotic map combined with PSO method provided the highest ß values (ß≥0.32 in XOR disease model and ß≥0.04 in ZZ disease model) and the significant p-value (p-value<0.001 in both the XOR and ZZ disease models). CONCLUSION: The Sinai chaotic map was found to effectively enhance the fitness values (χ2) of PSO method, indicating that the Sinai chaotic map combined with PSO method is more effective at detecting potential SNP barcodes in both the XOR and ZZ disease models.

Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4-related genes by an improved multifactor dimensionality reduction (MDR-ER).

In association studies, the combined effects of single nucleotide polymorphism (SNP)-SNP interactions and the problem of imbalanced data between cases and controls are frequently ignored. In the present study, we used an improved multifactor dimensionality reduction (MDR) approach namely MDR-ER to detect the high order SNP­SNP interaction in an imbalanced breast cancer data set containing seven SNPs of chemokine CXCL12/CXCR4 pathway genes. Most individual SNPs were not significantly associated with breast cancer. After MDR­ER analysis, six significant SNP­SNP interaction models with seven genes (highest cross­validation consistency, 10; classification error rates, 41.3­21.0; and prediction error rates, 47.4­55.3) were identified. CD4 and VEGFA genes were associated in a 2­loci interaction model (classification error rate, 41.3; prediction error rate, 47.5; odds ratio (OR), 2.069; 95% bootstrap CI, 1.40­2.90; P=1.71E­04) and it also appeared in all the best 2­7­loci models. When the loci number increased, the classification error rates and P­values decreased. The powers in 2­7­loci in all models were >0.9. The minimum classification error rate of the MDR­ER­generated model was shown with the 7­loci interaction model (classification error rate, 21.0; OR=15.282; 95% bootstrap CI, 9.54­23.87; P=4.03E­31). In the epistasis network analysis, the overall effect with breast cancer susceptibility was identified and the SNP order of impact on breast cancer was identified as follows: CD4 = VEGFA > KITLG > CXCL12 > CCR7 = MMP2 > CXCR4. In conclusion, the MDR­ER can effectively and correctly identify the best SNP­SNP interaction models in an imbalanced data set for breast cancer cases.

The Combinational Polymorphisms of ORAI1 Gene Are Associated with Preventive Models of Breast Cancer in the Taiwanese.

The ORAI calcium release-activated calcium modulator 1 (ORAI1) has been proven to be an important gene for breast cancer progression and metastasis. However, the protective association model between the single nucleotide polymorphisms (SNPs) of ORAI1 gene was not investigated. Based on a published data set of 345 female breast cancer patients and 290 female controls, we used a particle swarm optimization (PSO) algorithm to identify the possible protective models of breast cancer association in terms of the SNPs of ORAI1 gene. Results showed that the PSO-generated models of 2-SNP (rs12320939-TT/rs12313273-CC), 3-SNP (rs12320939-TT/rs12313273-CC/rs712853-(TT/TC)), 4-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs712853-(TT/TC)), and 5-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs6486795-CC/rs712853-(TT/TC)) displayed low values of odds ratios (0.409-0.425) for breast cancer association. Taken together, these results suggested that our proposed PSO strategy is powerful to identify the combinational SNPs of rs12320939, rs12313273, rs7135617, rs6486795, and rs712853 of ORAI1 gene with a strongly protective association in breast cancer.

A systematic gene-gene and gene-environment interaction analysis of DNA repair genes XRCC1, XRCC2, XRCC3, XRCC4, and oral cancer risk.

Oral cancer is the sixth most common cancer worldwide with a high mortality rate. Biomarkers that anticipate susceptibility, prognosis, or response to treatments are much needed. Oral cancer is a polygenic disease involving complex interactions among genetic and environmental factors, which require multifaceted analyses. Here, we examined in a dataset of 103 oral cancer cases and 98 controls from Taiwan the association between oral cancer risk and the DNA repair genes X-ray repair cross-complementing group (XRCCs) 1-4, and the environmental factors of smoking, alcohol drinking, and betel quid (BQ) chewing. We employed logistic regression, multifactor dimensionality reduction (MDR), and hierarchical interaction graphs for analyzing gene-gene (G×G) and gene-environment (G×E) interactions. We identified a significantly elevated risk of the XRCC2 rs2040639 heterozygous variant among smokers [adjusted odds ratio (OR) 3.7, 95% confidence interval (CI)=1.1-12.1] and alcohol drinkers [adjusted OR=5.7, 95% CI=1.4-23.2]. The best two-factor based G×G interaction of oral cancer included the XRCC1 rs1799782 and XRCC2 rs2040639 [OR=3.13, 95% CI=1.66-6.13]. For the G×E interaction, the estimated OR of oral cancer for two (drinking-BQ chewing), three (XRCC1-XRCC2-BQ chewing), four (XRCC1-XRCC2-age-BQ chewing), and five factors (XRCC1-XRCC2-age-drinking-BQ chewing) were 32.9 [95% CI=14.1-76.9], 31.0 [95% CI=14.0-64.7], 49.8 [95% CI=21.0-117.7] and 82.9 [95% CI=31.0-221.5], respectively. Taken together, the genotypes of XRCC1 rs1799782 and XRCC2 rs2040639 DNA repair genes appear to be significantly associated with oral cancer. These were enhanced by exposure to certain environmental factors. The observations presented here warrant further research in larger study samples to examine their relevance for routine clinical care in oncology.

Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interactions.

Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer.

Identifying association model for single-nucleotide polymorphisms of ORAI1 gene for breast cancer.

BACKGROUND: ORAI1 channels play an important role for breast cancer progression and metastasis. Previous studies indicated the strong correlation between breast cancer and individual single nucleotide polymorphisms (SNPs) of ORAI1 gene. However, the possible SNP-SNP interaction of ORAI1 gene was not investigated. RESULTS: To develop the complex analyses of SNP-SNP interaction, we propose a genetic algorithm (GA) to detect the model of breast cancer association between five SNPs (rs12320939, rs12313273, rs7135617, rs6486795 and rs712853) of ORAI1 gene. For individual SNPs, the differences between case and control groups in five SNPs of ORAI1 gene were not significant. In contrast, GA-generated SNP models show that 2-SNP (rs12320939-GT/rs6486795-CT), 3-SNP (rs12320939-GT/rs12313273-TT/rs6486795-TC), 5-SNP (rs12320939-GG/rs12313273-TC/rs7135617-TT/rs6486795-TT/rs712853-TT) have higher risks for breast cancer in terms of odds ratio analysis (1.357, 1.689, and 13.148, respectively). CONCLUSION: Taken together, the cumulative effects of SNPs of ORAI1 gene in breast cancer association study were well demonstrated in terms of GA-generated SNP models.

Identifying the association rules between clinicopathologic factors and higher survival performance in operation-centric oral cancer patients using the Apriori algorithm.

This study computationally determines the contribution of clinicopathologic factors correlated with 5-year survival in oral squamous cell carcinoma (OSCC) patients primarily treated by surgical operation (OP) followed by other treatments. From 2004 to 2010, the program enrolled 493 OSCC patients at the Kaohsiung Medical Hospital University. The clinicopathologic records were retrospectively reviewed and compared for survival analysis. The Apriori algorithm was applied to mine the association rules between these factors and improved survival. Univariate analysis of demographic data showed that grade/differentiation, clinical tumor size, pathology tumor size, and OP grouping were associated with survival longer than 36 months. Using the Apriori algorithm, multivariate correlation analysis identified the factors that coexistently provide good survival rates with higher lift values, such as grade/differentiation = 2, clinical stage group = early, primary site = tongue, and group = OP. Without the OP, the lift values are lower. In conclusion, this hospital-based analysis suggests that early OP and other treatments starting from OP are the key to improving the survival of OSCC patients, especially for early stage tongue cancer with moderate differentiation, having a better survival (>36 months) with varied OP approaches.