Identification of two novel biomarkers of rectal carcinoma progression and prognosis via co-expression network analysis

m1A regulator-mediated methylation modification patterns correlated with autophagy to predict the prognosis of hepatocellular carcinoma

BACKGROUND

N1-methyladenosine (m1A), among the most common internal modifications on RNAs, has a crucial role to play in cancer development. The purpose of this study were systematically investigate the modification characteristics of m1A in hepatocellular carcinoma (HCC) to unveil its potential as an anticancer target and to develop a model related to m1A modification characteristics with biological functions. This model could predict the prognosis for patients with HCC.

METHODS

An integrated analysis of the TCGA-LIHC database was performed to explore the gene signatures and clinical relevance of 10 m1A regulators. Furthermore, the biological pathways regulated by m1A modification patterns were investigated. The risk model was established using the genes that showed differential expression (DEGs) between various m1A modification patterns and autophagy clusters. These in vitro experiments were subsequently designed to validate the role of m1A in HCC cell growth and autophagy. Immunohistochemistry was employed to assess m1A levels and the expression of DEGs from the risk model in HCC tissues and paracancer tissues using tissue microarray.

RESULTS

The risk model, constructed from five DEGs (CDK5R2, TRIM36, DCAF8L, CYP26B, and PAGE1), exhibited significant prognostic value in predicting survival rates among individuals with HCC. Moreover, HCC tissues showed decreased levels of m1A compared to paracancer tissues. Furthermore, the low m1A level group indicated a poorer clinical outcome for patients with HCC. Additionally, m1A modification may positively influence autophagy regulation, thereby inhibiting HCC cells proliferation under nutrient deficiency conditions.

CONCLUSIONS

The risk model, comprising m1A regulators correlated with autophagy and constructed from five DEGs, could be instrumental in predicting HCC prognosis. The reduced level of m1A may represent a potential target for anti-HCC strategies.

Cross-Platform Synaptic Network Analysis of Human Entorhinal Cortex Identifies TWF2 as a Modulator of Dendritic Spine Length

Proteomic studies using postmortem human brain tissue samples have yielded robust assessments of the aging and neurodegenerative disease(s) proteomes. While these analyses provide lists of molecular alterations in human conditions, like Alzheimer's disease (AD), identifying individual proteins that affect biological processes remains a challenge. To complicate matters, protein targets may be highly understudied and have limited information on their function. To address these hurdles, we sought to establish a blueprint to aid selection and functional validation of targets from proteomic datasets. A cross-platform pipeline was engineered to focus on synaptic processes in the entorhinal cortex (EC) of human patients, including controls, preclinical AD, and AD cases. Label-free quantification mass spectrometry (MS) data (n = 2260 proteins) was generated on synaptosome fractionated tissue from Brodmann area 28 (BA28; n = 58 samples). In parallel, dendritic spine density and morphology was measured in the same individuals. Weighted gene co-expression network analysis was used to construct a network of protein co-expression modules that were correlated with dendritic spine metrics. Module-trait correlations were used to guide unbiased selection of Twinfilin-2 (TWF2), which was the top hub protein of a module that positively correlated with thin spine length. Using CRISPR-dCas9 activation strategies, we demonstrated that boosting endogenous TWF2 protein levels in primary hippocampal neurons increased thin spine length, thus providing experimental validation for the human network analysis. Collectively, this study describes alterations in dendritic spine density and morphology as well as synaptic proteins and phosphorylated tau from the entorhinal cortex of preclinical and advanced stage AD patients.SIGNIFICANCE STATEMENT Proteomic studies can yield vast lists of molecules that are altered under various experimental or disease conditions. Here, we provide a blueprint to facilitate mechanistic validation of protein targets from human brain proteomic datasets. We conducted a proteomic analysis of human entorhinal cortex (EC) samples spanning cognitively normal and Alzheimer's disease (AD) cases with a comparison of dendritic spine morphology in the same samples. Network integration of proteomics with dendritic spine measurements allowed for unbiased discovery of Twinfilin-2 (TWF2) as a regulator of dendritic spine length. A proof-of-concept experiment in cultured neurons demonstrated that altering Twinfilin-2 protein level induced corresponding changes in dendritic spine length, thus providing experimental validation for the computational framework.

Identification of Novel Biomarkers for Abdominal Aortic Aneurysm Promoted by Obstructive Sleep Apnea

BACKGROUND

We sought to find new biomarkers for abdominal aortic aneurysms (AAA) caused by chronic intermittent hypoxia (CIH).

METHODS

The AAA mice model was created using Ang II. The mice were divided into normoxic and CIH groups. The structure of AAA was observed using abdominal ultrasonography, Elastica van Gieson (EVG), and hematoxylin and eosin (HE) staining. The expression of ɑ-SMA was investigated using immunohistochemistry. The novel biomarkers were screened using bioinformatics analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to verify the expression of novel genes in both normal oxygen and CIH.

RESULTS

CIH appears to cause greater aortic dilation, higher AAA incidence, lower survival rate, thicker vessel wall, and more brittle elastic lamellae when compared to controls. The immunohistochemistry results showed that the expression of ɑ-SMA in the CIH group was reduced significantly. Four novel genes, including Homer2, Robo2, Ehf, and Asic1, were found to be differentially expressed between normal oxygen and CIH using qRT-PCR, indicating the same trend as bioinformatics analysis.

CONCLUSIONS

We discovered that CIH could hasten the occurrence and progression of AAA. Four genes (Homer2, Robo2, Ehf, and Asic1) may be novel biomarkers for AAA, which could aid in the search for new therapies for patients with AAA caused by CIH.

Network Preservation Analysis Reveals Dysregulated Synaptic Modules and Regulatory Hubs Shared Between Alzheimer’s Disease and Temporal Lobe Epilepsy

Background: There is increased prevalence of epilepsy in patients with Alzheimer’s disease (AD). Although shared pathological and clinical features have been identified, the underlying pathophysiology and cause-effect relationships are poorly understood. We aimed to identify commonly dysregulated groups of genes between these two disorders.

Methods: Using publicly available transcriptomic data from hippocampal tissue of patients with temporal lobe epilepsy (TLE), late onset AD and non-AD controls, we constructed gene coexpression networks representing all three states. We then employed network preservation statistics to compare the density and connectivity-based preservation of functional gene modules between TLE, AD and controls and used the difference in significance scores as a surrogate quantifier of module preservation.

Results: The majority (>90%) of functional gene modules were highly preserved between all coexpression networks, however several modules identified in the TLE network showed various degrees of preservation in the AD network compared to that of control. Of note, two synaptic signalling-associated modules and two metabolic modules showed substantial gain of preservation, while myelination and immune system-associated modules showed significant loss of preservation. The genes SCN3B and EPHA4 were identified as central regulatory hubs of the highly preserved synaptic signalling-associated module. GABRB3 and SCN2A were identified as central regulatory hubs of a smaller neurogenesis-associated module, which was enriched for multiple epileptic activity and seizure-related human phenotype ontologies.

Conclusion: We conclude that these hubs and their downstream signalling pathways are common modulators of synaptic activity in the setting of AD and TLE, and may play a critical role in epileptogenesis in AD.

Role of adaptin protein complexes in intracellular trafficking and their impact on diseases

Adaptin proteins (APs) play a crucial role in intracellular cell trafficking. The 'classical' role of APs is carried out by AP1‒3, which bind to clathrin, cargo, and accessory proteins. Accordingly, AP1-3 are crucial for both vesicle formation and sorting. All APs consist of four subunits that are indispensable for their functions. In fact, based on studies using cells, model organism knockdown/knock-out, and human variants, each subunit plays crucial roles and contributes to the specificity of each AP. These studies also revealed that the sorting and intracellular trafficking function of AP can exert varying effects on pathology by controlling features such as cell development, signal transduction related to the apoptosis and proliferation pathways in cancer cells, organelle integrity, receptor presentation, and viral infection. Although the roles and functions of AP1‒3 are relatively well studied, the functions of the less abundant and more recently identified APs, AP4 and AP5, are still to be investigated. Further studies on these APs may enable a better understanding and targeting of specific diseases.APs known or suggested locations and functions.

Homer2 and Homer3 Act as Novel Biomarkers in Diagnosis of hepatitis B virus-induced Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common causes of cancer‐related mortality worldwide. Early detection of HCC can significantly improve patients' outcomes. An increasing number of studies have validated that Homer is dysregulated in cancers and may serve as diagnostic markers. In the present study, we investigated the expression profile and diagnostic significance of Homer2 and Homer3 in hepatitis B virus-induced HCC (HBV-HCC).

Methods: Quantitative real-time PCR (QRT-PCR), western blot analysis and immunohistochemistry analysis.

Results: Homer2 and Homer3 were downregulated in HCC. The expression of Homer2 was associated with tumor differentiation grade (P= 0.012) and total protein (TP) level (P= 0.032). Homer3 was related to tumor size (P= 0.010), tumor nodes (P= 0.026) and γ-glutamyl transferase (GGT) level (P= 0.001). The receiver operating characteristic curve analyses indicated that the combination of Homer2, Homer3 and AFP possessed a high accuracy (AUC=0.900) to diagnose HCC cases from healthy controls.

Conclusion: Our data indicated that Homer2 and Homer3 were downregulated in HCC and might be potential diagnostic marker for HCC.

A Four Gene-Based Risk Score System Associated with Chemoradiotherapy Response and Tumor Recurrence in Rectal Cancer by Co-Expression Network Analysis

Aim

Resistance to neoadjuvant chemoradiotherapy (NCRT) and tumor recurrence presents a major clinical problem in locally advanced rectal cancer (LARC) patients. This study aimed to explore a genetic risk score related to NCRT response and tumor recurrence in rectal cancer after NCRT.

Materials and Methods

Weighted gene co-expression network analysis was employed to identify hub genes associated with NCRT response from the GSE93375 dataset. Prognostic hub genes were determined using Cox regression analysis and associated with disease-free survival (DFS). A risk score system was constructed and the prognostic significance of the risk score was validated in our patient cohort. A predictive nomogram for DFS was developed and validated internally.

Results

The Tan module had the highest correlations with NCRT response. Ten hub genes (COL15A1, THBS2, ITGB1, MMP2, CD34, SPARC, NOTCH3, PDGFRB, DCN, and SERPINH1) were associated with NCRT response. Immunostaining expression of four genes (NOTCH3, SPARC, DCN, and ITGB1) was found to be significantly associated with both NCRT response and DFS in our patient cohort and was selected to build a prognostic risk score for DFS as follows: risk score= (0.6188×Exp _NOTCH3 ) + (0.6511×Exp _SPARC ) + (-0.2976×Exp _DCN ) + (1.0035×Exp _ITGB1 ). Using this risk score, patients could be separated into high- and low-risk groups for tumor recurrence. A nomogram that incorporated the risk score, ypTNM stage, and tumor regression grade (TRG) was constructed and utilized to predict DFS in LARC patients.

Conclusion

The four-gene expression-based risk score system presented here could be potentially used for predicting tumor recurrence in LARC patients after NCRT.

Large-scale proteomic analysis of Alzheimer's disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation

Our understanding of Alzheimer's disease (AD) pathophysiology remains incomplete. Here we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.

Construction of a CXC Chemokine-Based Prediction Model for the Prognosis of Colon Cancer

Colon cancer is the third most common cancer, with a high incidence and mortality. Construction of a specific and sensitive prediction model for prognosis is urgently needed. In this study, profiles of patients with colon cancer with clinical and gene expression data were downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA). CXC chemokines in patients with colon cancer were investigated by differential expression gene analysis, overall survival analysis, receiver operating characteristic analysis, gene set enrichment analysis (GSEA), and weighted gene coexpression network analysis. CXCL1, CXCL2, CXCL3, and CXCL11 were upregulated in patients with colon cancer and significantly correlated with prognosis. The area under curve (AUC) of the multigene forecast model of CXCL1, CXCL11, CXCL2, and CXCL3 was 0.705 in the GSE41258 dataset and 0.624 in TCGA. The prediction model was constructed using the risk score of the multigene model and three clinicopathological risk factors and exhibited 92.6% and 91.8% accuracy in predicting 3-year and 5-year overall survival of patients with colon cancer, respectively. In addition, by GSEA, expression of CXCL1, CXCL11, CXCL2, and CXCL3 was correlated with several signaling pathways, including NOD-like receptor, oxidative phosphorylation, mTORC1, interferon-gamma response, and IL6/JAK/STAT3 pathways. Patients with colon cancer will benefit from this prediction model for prognosis, and this will pave the way to improve the survival rate and optimize treatment for colon cancer.

Integrated Analysis Identifies a Nine-microRNA Signature Biomarker for Diagnosis and Prognosis in Colorectal Cancer

BACKGROUND

Colorectal cancer (CRC) is the third most lethal and malignant type of cancer in the world. Abnormal expression of human microRNA-200a (hsa-miRNA-200a or miR-200a) has previously been characterized as a clinically noticeable biomarker in several cancers, but its role in CRC is still unclear.

METHODS

Three CRC miRNA expression datasets were integratively analyzed by Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms. Nine candidate miRNAs were identified and validated for diagnostic and prognostic capability with the prediction model. The potential roles of the tumor suppressor miR-200a-3p in invasion, migration, and epithelial-mesenchymal transition of CRC cells were elaborated by in vitro studies.

RESULTS

Nine miRNAs (miR-492, miR-200a, miR-338, miR-29c, miR-101, miR-148a, miR-92a, miR-424, and miR-210) were identified as potentially useful diagnostic biomarkers in the clinic. The overall accuracy rate of the nine miRNAs in the diagnostic model was 0.94, 0.89, and 0.978 in the testing, validation, and independent validation dataset, respectively. CRC patients in the GSE29622 cohort were separated by the prognostic model into the low-risk score group and the high-risk score group. The area under the receiver operating characteristic curve (AUC) was 0.872 and 0.783 for predicting the 1- to 10-year survival of CRC patients. The performance of the prognostic model was validated by an independent TCGA-Colon Adenocarcinoma (COAD) dataset with AUC values between 0.911 and 0.796 in predicting 1- to 10-year survival. Nomograms comprising risk scores, tumor stage, and TNM staging were generated for predicting 1-, 3-, and 5-year overall survival (OS) in the GSE29622 and TCGA-COAD datasets. Colony formation, invasion, and migration in DLD1 and SW480 cells were suppressed by overexpression of miR-200a-3p. Inhibition of miR-200a-3p function contributed to abnormal colony formation, migration, invasion, and epithelial-mesenchymal transition (EMT). miR-200a-3p binding sites were located within the 3'-untranslated region (3'-UTR) of the Forkhead box protein A1 (FOXA1) mRNA.

CONCLUSION

We developed and validated a diagnostic and prognostic prediction model for CRC. miR-200a-3p was determined to be a potential diagnostic and prognostic biomarker for CRC.

Optimal Bayesian Filtering for Biomarker Discovery: Performance and Robustness

Optimal Bayesian feature filtering (OBF) is a fast and memory-efficient algorithm that optimally identifies markers with distributional differences between treatment groups under Gaussian models. Here, we study the performance and robustness of OBF for biomarker discovery. Our contributions are twofold: (1) we examine how OBF performs on data that violates modeling assumptions, and (2) we provide guidelines on how to set input parameters for robust performance. Contribution (1) addresses an important, relevant, and commonplace problem in computational biology, where it is often impossible to validate an algorithm's core assumptions. To accomplish both tasks, we present a battery of simulations that implement OBF with different inputs and challenge each assumption made by OBF. In particular, we examine the robustness of OBF with respect to incorrect input parameters, false independence, imbalanced sample size, and we address the Gaussianity assumption by considering performance on an extensive family of non-Gaussian distributions. We address advantages and disadvantages between different priors and optimization criteria throughout. Finally, we evaluate the utility of OBF in biomarker discovery using acute myeloid leukemia (AML) and colon cancer microarray datasets, and show that OBF is successful at identifying well-known biomarkers for these diseases that rank low under moderated t-test.

A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network

Background and Goals. To identify a multigene signature model for prognosis of non-small-cell lung cancer (NSCLC) patients, we first found 2146 consensus differentially expressed genes (DEGs) in NSCLC overlapped in Gene Expression Omnibus (GEO) and TCGA lung adenocarcinoma (LUAD) datasets using integrated analysis. We constructed a weighted gene coexpression network (WGCN) using the consensus DEGs and identified the module significantly associated with pathological M stage and consisted of 61 genes. After univariate Cox regression analysis and subsequent stepwise model selection by the Akaike information criterion (AIC) and multivariate Cox hazard model analysis, an mRNA signature model which calculated prognostic score was generated: prognostic score = (-0.2491 × EXP_RRAGB) + (-0.0679 × EXP_RSPH9) + (-0.2317 × EXP_RPS6KL1) + (-0.1035 × EXP_RXFP1) + 0.1571 × EXP_RRM2 + 0.1104 × EXP_RTL1, where EXP is the fragments per kilobase million (FPKM) value of the mRNA included in the model. The prognostic model separated NSCLC patients in the TCGA-LUAD dataset into the low- and high-risk score groups with a median prognostic score of 0.972. Higher scores predicted higher risk. The area under ROC curve (AUC) was 0.994 or 0.776 in predicting the 1- to 10-year survival of NSCLC patients. The prognostic performance of this prognostic model was validated by an independent GSE11969 dataset of NSCLC adenocarcinoma with AUC values between 0.822 and 0.755 in predicting 1- to 10-year survival of NSCLC. These results suggested that the six-gene signature functioned as an independent biomarker to predict the overall survival of NSCLC adenocarcinoma.

Whole exome sequencing identified a second pathogenic variant in HOMER2 for autosomal dominant non-syndromic deafness

Hearing loss is one of the most common sensory disorders worldwide, and about half of all occurrences are attributable to genetic factors. Here, we have identified a novel pathogenic variant in HOMER2 in a Chinese family with autosomal dominant, non-syndromic hearing loss. This is the second family reported globally with hearing loss caused by a variant in HOMER2. The pathogenic variant c.840_841insC in HOMER2 (NM_199330), segregating with the hearing-loss phenotype in the family, leads to a premature stop codon producing a truncated protein. The coiled-coil domain in the C-terminal of HOMER2 protein is essential for protein multimerization and HOMER2-CDC42 interaction. We compared the phenotypes in the two families and found that hearing impairment in this Chinese family was more severe. Furthermore, we found that the ability of this insertion mutant type HOMER2 (HOMER2_MU ) to multimerize decreased more significantly than wild-type HOMER2 (HOMER2_WT ) and the reported c.554G>C (NM_004839) mutant HOMER2. HOMER2_MU protein tended to be distributed in a diffuse manner, whereas HOMER2_WT and the reported mutant HOMER2 tended to cluster together. Our research provides a validating second family for variants in HOMER2 causing non-syndromic sensorineural hearing loss. HOMER2 homo-/hetero-multimerization might be the first step in exerting its normal function.

Chromogranin-A Expression as a Novel Biomarker for Early Diagnosis of Colon Cancer Patients

Colon cancer is one of the major causes of cancer death worldwide. The five-year survival rate for the early-stage patients is more than 90%, and only around 10% for the later stages. Moreover, half of the colon cancer patients have been clinically diagnosed at the later stages. It is; therefore, of importance to enhance the ability for the early diagnosis of colon cancer. Taking advantages from our previous studies, there are several potential biomarkers which have been associated with the early diagnosis of the colon cancer. In order to investigate these early diagnostic biomarkers for colon cancer, human chromogranin-A (CHGA) was further analyzed among the most powerful diagnostic biomarkers. In this study, we used a logistic regression-based meta-analysis to clarify associations of CHGA expression with colon cancer diagnosis. Both healthy populations and the normal mucosa from the colon cancer patients were selected as the double normal controls. The results showed decreased expression of CHGA in the early stages of colon cancer as compared to the normal controls. The decline of CHGA expression in the early stages of colon cancer is probably a new diagnostic biomarker for colon cancer diagnosis with high predicting possibility and verification performance. We have also compared the diagnostic powers of CHGA expression with the typical oncogene KRAS, classic tumor suppressor TP53, and well-known cellular proliferation index MKI67, and the CHGA showed stronger ability to predict early diagnosis for colon cancer than these other cancer biomarkers. In the protein-protein interaction (PPI) network, CHGA was revealed to share some common pathways with KRAS and TP53. CHGA might be considered as a novel, promising, and powerful biomarker for early diagnosis of colon cancer.

Identification of ITGA3 as an Oncogene in Human Tongue Cancer via Integrated Bioinformatics Analysis

Human tongue cancer (TC) is an aggressive malignancy with a very poor prognosis. There is an urgent need to elucidate the underlying molecular mechanisms involved in TC progression. mRNA expression profiles play a vital role in the exploration of cancer-related genes. Therefore, the purpose of our study was to identify the progression associated candidate genes of TC by bioinformatics analysis. Five microarray datasets of TC samples were downloaded from the Gene Expression Omnibus (GEO) database and the data of 133 TC patients were screened from The Cancer Genome Atlas (TCGA) head and neck squamous cell carcinoma (HNSC) database. The integrated analysis of five microarray datasets and the RNA sequencing data of TC samples in TCGA-HNSC was performed to obtain 1023 overlapping differentially expressed genes (DEGs) in TC and adjacent normal tissue (ANT) samples. Next, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to enrich the significant pathways of the 1023 DEGs and PI3KAkt signaling pathway (P=0.011) was selected to be the candidate pathway. A total of 23 DEGs with |log2 fold change (FC)| ≥1.0 in phosphatidylinositol 3-kinase-serine/threonine kinase (PI3K-Akt) signaling pathway were subjected to survival analysis of 125 eligible TC samples in TCGA database, indicating increased integrin-α3 gene (ITGA3) expression was significantly associated with poorer prognosis. Taken together, our study suggested ITGA3 may facilitate the development of TC via activating PI3K-Akt signaling pathway.

Integrative analysis of promising molecular biomarkers and pathways for coronary artery disease using WGCNA and MetaDE methods

The present study aimed to examine the molecular mechanisms of coronary artery disease (CAD). A total of four microarray datasets (training dataset no. GSE12288; validation dataset nos. GSE20680, GSE20681 and GSE42148) were downloaded from the Gene Expression Omnibus database, which included CAD and healthy samples. Weighted gene co-expression network analysis was applied to identify highly preserved modules across the four datasets. Differentially expressed genes (DEGs) with significant consistency in the four datasets were selected using the MetaDE method. The overlapping genes amongst the DEGs with significant consistency and in the preserved modules were used to construct a protein-protein interaction (PPI) network, followed by functional enrichment analysis. A total of 11 modules were established in the training dataset, and five of them were highly preserved across all four datasets, including 873 genes. There was a total of 836 DEGs with significant consistency in the four datasets. A total of 177 overlapping genes were selected, with which a PPI network was constructed. The top five genes of the PPI network were identified based on their degrees: LCK proto-oncogene, Src family tyrosine kinase (LCK), euchromatic histone lysine methyltransferase 2 (EHMT2), inosine monophosphate dehydrogenase 2 (IMPDH2), protein phosphatase 4 catalytic subunit (PPP4C) and ζ-chain of T-cell receptor associated protein kinase 70 (ZAP70). Genes in the PPI network were significantly involved in a number of Kyoto Encyclopedia Genes and Genomes pathways, including the ‘natural killer cell mediated cytotoxicity’, ‘primary immunodeficiency’ and ‘Fc gamma R-mediated phagocytosis’ pathways. LCK, EHMT2, IMPDH2, PPP4C and ZAP70 are suggested as promising molecular biomarkers for CAD. The ‘natural killer cell mediated cytotoxicity’, ‘primary immunodeficiency’ and ‘Fc gamma R-mediated phagocytosis’ pathways may serve important roles in CAD.