Investigation On HCC Functional Genes From The Viewpoint Of Bioinformatics

Hepatocellular carcinoma (HCC) accounts for70–85%of primary liver cancers,ranking the fifth among the most common malignancies worldwide and the third as acause of cancer death. HCC is a devastating tumor, with a mean survival of much lessthan one year if left untreated HCC. More than500million people worldwide arepersistently infected with the hepatitis B virus (HBV) and/or hepatitis C virus (HCV),who are at a risk of developing chronic liver diseases, cirrhosis and evenhepatocellular carcinoma.In this dissertation, the Stanford database liver cancer genetic data are used forinvestigation. The research has both contributios in theoretical methods and results.From the viewpoint of theoretic methods, a number of novel concepts such as HCCgene model, HCC core gene, distinction immune gene, HCC Top-N gene, and HCCtags gene are proposed. Furthermore, a series of new design algorithms, includingPAM algorithm, core genetic screening algorithm, three-layer filtering algorithm,gene-based collaborative filtering algorithm, and tag gene-based classification, areaddressed. In order to illustrate the design ideas and methods, a couple of innovativeformulas, such as gene community networks, modularity of genes, genetic influence,distinction immune genes, gene penalty precision, and scoring function of treatment,are also presented.From the viewpoint of experimental results and clinic advices, the contributionsare listed as follows.⑴HCC gene modeling: Pearson agglomerative method (PAM) is applied toidentify functional modules of liver cancer genes, and interval Pearson correlationcoefficient (PCC) modularity is employed to assess the decomposition of the modules.We obtained13very-strong-correlation modules and14strong-correlation modules.In addition to some common modules, a number of new functional modules, such ashemostasis module S1, fibrinogen module W9, antiterminator module S8, immortalmodule S9, anti-growth inhibition module W6, anti-apoptotic module W12, ironregulation module S6, and metalloproteins module S7, are obtained.⑵HCC core genes: A novel multi-filter, called core gene screening (CGS)algorithm is proposed. The15target genes, composed of HAMP, RNAHP, MT1H,MT1G, MT1L, AQP4, GPC3, MT1E, VIPR1, DNASE1L3, MT1B are screened, and finally the three core genes HAMP, GPC3and MTs are identified successfully byusing the proposed algorithm. Agene therapy method, called Zinc supplementation, issuggested to regulate the levels of the genes expression and the metal content, whichis a simple and safe way to treat liver cancer and relieve the symptoms of the patients.⑶Distinction immune genes: the concept of distinction immune gene isproposed. A filter is then designed to screen these genes. The23key distinctionimmune genes are screened, which are divided into four clusters: T cells, B cells,immune signaling, and MHC. Therefore, the clinic advice is either to increase theupdate speed of antigens or reduce the update speed of the viruses during thetreatment of HCV-induced HCC. Moreover, it is also advised to add T cells or add theexpression levels of T cells to strengthen the ability to kill cancer cells. In contrast,HBV updates slowly, but the immunity system in HBV-induced HCC has beendamaged seriously. As a result, the clinic advice is to improve the immune ability ofpatients subjected to HBV-induced HCC, such as increasing immunoglobulin, T cells,and B cells etc.⑷HCC Top-N genes. Agene collaborative filtering algorithm, called GeneCF, isprposed, which employs an ascending interest sort in essence. According to the geneinterest, the top N ones are extracted as target genes for each patient. Based onaccuracy and coverage, the new evaluation indexes such as gene penalty precision andgene penalty coverage are presented. The most remarkable advantage of the GeneCFalgorithm is the algorthim has better robustness against the missing values of the useddata.⑸HCC tags genes: A novel concept, called tag gene, is proposed, and the twotags genes HAMP and GPC3are obtained, leading to the two treatmeant schemes,namely HMAP treatment scheme and GPC3treatment scheme. The HAMP treatmentmethod is to lower iron by using iron chelator. The HCC gene data of52patients aretested, incidating about46%of patients are not sutiable for the HAMP treatment, butabout20%of patients have good effects under this treatment. It is worthy to point outthat the patient with lower iron in boides should never use the HAMP treatment. As aresult, it should be assessed seriously for individual patients when taking HAMPtreatments.