As genes with similar expression pattern are very likely having the same biological function, cluster analysis becomes an important tool to understand and predict gene functions from gene expression profi les. In many situations, each gene expression profi le only contains a few data points. Directly applying traditional clustering algorithms to such short gene expression profi les does not yield satisfactory results. Developing clustering algorithms for short gene expression profi les is necessary. In this thesis, two novel methods are developed for clustering short gene expression pro files. The fi rst method, called the network-based clustering method, deals with the defect of short gene expression profi les by generating a gene co-expression network using conditional mutual information (CMI), which measures the non-linear relationship between two genes, as well as considering indirect gene relationships in the presence of other genes. The network-based clustering method consists of two steps. A gene co-expression network is firstly constructed from short gene expression profi les using a path consistency algorithm (PCA) based on the CMI between genes. Then, a gene functional module is identi ed in terms of cluster cohesiveness. The network-based clustering method is evaluated on 10 large scale Arabidopsis thaliana short time-course gene expression profi le datasets in terms of gene ontology (GO) enrichment analysis, and compared with an existing method called Clustering with Over-lapping Neighbourhood Expansion (ClusterONE). Gene functional modules identi ed by the network-based clustering method for 10 datasets returns target GO p-values as low as 10-24, whereas the original ClusterONE yields insigni cant results. In order to more speci cally cluster gene expression profi les, a second clustering method, namely the protein-protein interaction (PPI) integrated clustering method, is developed. It is designed for clustering short gene expression profi les by integrating gene expression profi le patterns and curated PPI data. The method consists of the three following steps: (1) generate a number of prede ned profi le patterns according to the number of data points in the profi les and assign each gene to the prede fined profi le to which its expression profi le is the most similar; (2) integrate curated PPI data to refi ne the initial clustering result from (1); (3) combine the similar clusters from (2) to gradually reduce cluster numbers by a hierarchical clustering method. The PPI-integrated clustering method is evaluated on 10 large scale A. thaliana datasets using GO enrichment analysis, and by comparison with an existing method called Short Time-series Expression Miner (STEM). Target gene functional clusters identi ed by the PPI-integrated clustering method for 10 datasets returns GO p-values as low as 10-62, whereas STEM returns GO p-values as low as 10-38. In addition to the method development, obtained clusters by two proposed methods are further analyzed to identify cross-talk genes under fi ve stress conditions in root and shoot tissues. A list of potential abiotic stress tolerant genes are found.

Indiana University-Purdue University Indianapolis (IUPUI) / New neurons are generated throughout adulthood in two regions of the brain, the dentate gyrus of the hippocampus, which is important for memory formation and cognitive functions, and the sub-ventricular zone of the olfactory bulb, which is important for the sense of smell, and are incorporated into hippocampal network circuitry. Disruption of this process has been postulated to contribute to neurodegenerative disorders including Alzheimer’s disease [1]. AD is the most common form of adult-onset dementia and the number of patients with AD escalates dramatically each year. The generation of new neurons in the dentate gyrus declines with age and in AD. Many of the molecular players in AD are also modulators of adult neurogenesis, but the genetic mechanisms influencing adult neurogenesis in AD are unclear. The overall goal of this project is to identify candidate genes and pathways that play a role in neurogenesis in the adult brain and to test the hypotheses that 1) hippocampal neurogenesis-related genes and pathways are significantly perturbed in AD and 2) neurogenesis-related pathways are significantly associated with hippocampal volume and other AD-related biomarker endophenotypes including brain deposition of amyloid-β and tau pathology. First, potential modulators of adult neurogenesis and their roles in neurodegenerative diseases were evaluated. Candidate genes that control the turnover process of neural stem cells/precursors to new functional neurons during adult neurogenesis were manually curated using a pathway-based systems biology approach. Second, a targeted neurogenesis pathway-based gene analysis was performed resulting in the identification of ADORA2A as associated with hippocampal volume and memory performance in mild cognitive impairment and AD. Third, a genome-wide gene-set enrichment analysis was conducted to discover associations between hippocampal volume and AD related endophenotypes and neurogenesis-related pathways. Within the discovered neurogenesis enriched pathways, a gene-based association analysis identified TESC and ACVR1 as significantly associated with hippocampal volume and APOE and PVLR2 as significantly associated with tau and amyloid beta levels in cerebrospinal fluid. This project identifies new genetic contributions to hippocampal neurogenesis with translational implications for novel therapeutic targets related to learning and memory and neuroprotection in AD.

Regulator of Chromatin Condensation 1 (RCC1) is the only known guanine nucleotide exchange factor that acts on the Ras-like G protein Ran and plays a key role in cell cycle regulation. Although there is growing evidence to support the relationship between RCC1 and cancer, detailed pancancer analyses have not yet been performed. In this genome database study, based on The Cancer Genome Atlas, Genotype-Tissue Expression and Gene Expression Omnibus databases, the potential role of RCC1 in 33 tumors’ entities was explored. The results show that RCC1 is highly expressed in most human malignant neoplasms in contrast to healthy tissues. RCC1 expression is closely related to the prognosis of a broad variety of tumor patients. Enrichment analysis showed that some tumor-related pathways such as “cell cycle” and “RNA transport” were involved in the functional mechanism of RCC1. In particular, the conducted analysis reveals the relation of RCC1 to multiple immune checkpoint genes and suggests that the regulation of RCC1 is closely related to tumor infiltration of cancer-associated fibroblasts and CD8+ T cells. Coherent data demonstrate the association of RCC1 with the tumor mutation burden and microsatellite instability in various tumors. These findings provide new insights into the role of RCC1 in oncogenesis and tumor immunology in various tumors and indicate its potential as marker for therapy prognosis and targeted treatment strategies.

The overexpression of the enzymes involved in the degradation of procollagen lysine is correlated with various tumor entities. Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3 (PLOD3) expression was found to be correlated to the progression and migration of cancer cells in gastric, lung and prostate cancer. Here, we analyzed the gene expression, protein expression, and the clinical parameters of survival across 33 cancers based on the Clinical Proteomic Tumor Analysis Consortium (CPTAC), function annotation of the mammalian genome 5 (FANTOM5), Gene Expression Omnibus (GEO), Genotype-Tissue Expression (GTEx), Human Protein Atlas (HPA) and The Cancer Genome Atlas (TCGA) databases. Genetic alteration, immune infiltration and relevant cellular pathways were analyzed in detail. PLOD3 expression negatively correlated with survival periods and the infiltration level of CD8+ T cells, but positively correlated to the infiltration of cancer associated fibroblasts in diverse cancers. Immunohistochemistry in colon carcinomas, glioblastomas, and soft tissue sarcomas further confirm PLOD 3 expression in human cancer tissue. Moreover, amplification and mutation accounted for the largest proportion in esophageal adenocarcinoma and uterine corpus endometrial carcinoma, respectively; the copy number alteration of PLOD3 appeared in all cancers from TCGA; and molecular mechanisms further proved the effect of PLOD3 on tumorigenesis. In particular, PLOD3 expression appears to have a tumor immunological effect, and is related to multiple immune cells. Furthermore, it is also associated with tumor mutation burden and microsatellite instability in various tumors. PLOD3 acts as an inducer of various cancers, and it could be a potential biomarker for prognosis and targeted treatment.

Colorectal cancer is the third most common cancer and the leading cause of cancer deaths in Western industrialised countries. Despite recent advances in the screening, diagnosis, and treatment of colorectal cancer, an estimated 608,000 people die every year due to colon cancer. Our current knowledge of colorectal carcinogenesis indicates a multifactorial and multi-step process that involves various genetic alterations and several biological pathways. The identification of molecular markers with early diagnostic and precise clinical outcome in colon cancer is a challenging task because of tumour heterogeneity. This Ph.D.-thesis presents the molecular and cellular mechanisms leading to colorectal cancer. A systematical review of the literature is conducted on Microarray Gene expression profiling, gene ontology enrichment analysis, microRNA and system Biology and various bioinformatics tools. We aimed this study to stratify a colon tumour into molecular distinct subtypes, identification of novel diagnostic targets and prediction of reliable prognostic signatures for clinical practice using microarray expression datasets. We performed an integrated analysis of gene expression data based on genetic, epigenetic and extensive clinical information using unsupervised learning, correlation and functional network analysis. As results, we identified 267-gene and 124-gene signatures that can distinguish normal, primary and metastatic tissues, and also involved in important regulatory functions such as immune-response, lipid metabolism and peroxisome proliferator-activated receptors (PPARs) signalling pathways. For the first time, we also identify miRNAs that can differentiate between primary colon from metastatic and a prognostic signature of grade and stage levels, which can be a major contributor to complex transcriptional phenotypes in a colon tumour.