Background Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. Results The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. Conclusion A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)—are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient ≥ 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed—maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer.

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Cancer recurrence after therapy and long periods of remission are frequent in melanoma patients, likely due to the presence of residual disseminated tumor cells (DTCs). DTCs can enter dormancy and become refractory to targeted or conventional therapies. Extracellular matrix (ECM) proteins and basement membrane (BM) formation are critical structural components required to support the tumor microenvironment. Dystroglycan (DG) receptor complex, which is highly glycosylated in the alpha subunit, is a crucial ECM binding receptor because it functions in bridging the ECM and the cytoskeleton. DG receptor has been implicated in a variety of cancers, including melanoma, but its function in DTCs and dormancy has not been evaluated. In this study, we performed a bioinformatics analysis to study the implication of mutations in DAG1 (DG receptor gene) and its associated genes in cutaneous melanoma patients. We found that patients harboring mutations in the protein O-mannosyltransferase 1 enzyme (POMT1), which is essential for the transfer of a mannosyl residue to the alpha-DG subunit, had a significantly lower median months survival (27 months) compared to those with no POMT1 mutations (61 months). We noted missense mutations (Phe-Ser) in DAG1 occurred in the mucin-like region of the alpha subunit of DG. Also, the expression of DAG1, dystrophin (DMD), and utrophin (UTRN) at the mRNA level was significantly augmented under the presence of DAG1 mutations compared to the cohort of patients without DAG1 mutations. We evaluated the expression of alpha-DG, dystrophin, utrophin, and non-receptor tyrosine kinase (FER) to establish a potential relationship between the DAG receptor complex and FER in a metastatic mouse model. This mouse model is based on the overexpression of GPCRs (Tg(Grm1)Epv(E)/K5-tTA-Edn3) and spontaneously develops melanoma tumors on the ear, tail and dorsal skin, and presents with 90% penetrance of metastasis in the lymph nodes and lung. Using a genetic lineage tracing, we detected the presence of intravascular metastatic cells in the lung that are negative for Ki67 (proliferation marker), caspase3 (apoptosis marker), and other dormancy markers (p21, p27, p-ERK) indicating they are in a dormant state. The intravascular dormant DTC cells expressed high levels of alpha-DG and utrophin, and moderate levels of dystrophin but low levels of FER. Overall, our results suggest that alterations in alpha-DG glycosylation resulting from POMT1 mutations and subsequent FER downregulation may contribute to the dormancy status of metastatic melanoma cells in the lung vascular niche. Our findings will help us further understand the mechanisms that underlie melanoma dormancy and have the potential to contribute to the development of therapeutic strategies for recurrent disease. Citation Format: Israel Castillo Gonzalez, Mona Maharjan, Ananda Mohan Mondal, Lidia Kos. Dystroglycan receptor and FER maintain melanoma dormancy in the vascular niche [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3956.