| Abstract: | 根據「美國癌症協會」（American Cancer Society, ACS）在「臨床醫師癌症雜誌」（CA：A Cancer Journal for Clinicians）文章中的調查， 2020 年全球發生了 1930 萬新癌症病例和近 1000 萬癌症死亡病例，癌症死亡率前三名，肝細胞癌排名第三，佔8.3%。衛生福利部統計109年全臺灣因癌症而死亡人數總共為5萬161人，佔全臺死亡人數的29.0%，其該年死亡率每10萬人口有212.7人，較前一年108年下降了1.8%，十大癌症死亡率排名，肝癌排名第二。肝癌對人類的存活及生命健康都有著不容小覷的影響。但國家衛生研究院最新研究顯示，脂肪肝、糖尿病、三酸甘油脂過高，已成國人罹患肝癌的三大風險因子，研究同時打破傳統罹癌迷思，無酒癮、無肝炎、肝硬化也可能罹患肝癌。因此應該將新的代謝性疾病風險因子列入肝癌發生風險評估。而代謝性疾病最容易讓人從外觀上發現有潛在風險的即為肥胖這種流行性疾病，故著手研究肥胖相關基因與肝癌之間的關係。因此，本研究以肝癌為研究標的，為了探討肥胖基因導致肝癌的影響，我們將分析肝癌患者與非肝癌患者之肥胖相關基因之表現量，結合病人臨床數據，以期發現肝癌之致病因子。本研究從PubMed資料庫中搜尋肝癌及肥胖基因相關之論文，為了有效率整理出肥胖相關基因群，我們透過便利的工具PubTator做資料探勘篩選出其基因群。並使用GEO-2R取得三組實驗數據，共379筆病例數，實驗項目為肝癌患者與非肝癌患者其肥胖相關基因表現量的差異，進行條件篩選P-Value值≦0.01為分析目標，並且具有至少兩倍差異量的基因，三組實驗數據的肥胖基因篩選後發現共有448個相同基因，將共同基因數據資料導入GENE-E分析出的熱圖(Heatmap)。將前述篩選後的448個共同基因透過DAVID (The Database for Annotation, Visualization and Integrated Discovery，簡稱DAVID )分析平台，探討其基因功能及參與之代謝途徑。接著利用cBioPortal for cancer genomics，分析肝癌患者與非肝癌患者之差異表現基因是否對於預後生存有相關聯。最後，再利用文獻進行探討與驗證。結果發現本研究中差異表現量之基因:SPP1、GPC3、NQO1、CDK1、PEG10、HGF、HAMP、CYP2E1與病人之存活預後無相關，反而是從突變率的角度發現之基因:TP53及APOB與存活預後較有相關，經探勘Xiang DM et. al.研究也發現類似結果。值得注意的是，在文氏圖的交集中，發現了TENM1這個基因與肝癌之預後有關，但無任何文獻提及類似佐證，可能將成為新的研究方向。我們透過一系列之生物資訊分析與文獻佐證，希望能提供研究學者對肝癌有進一步地深入了解，有望成為治療相關疾病的研究方向。
According to a survey by the American Cancer Society (ACS) in an article in the “Clinician Cancer Journal” (CA: A Cancer Journal for Clinicians), there were 19.3 million new cancer cases and nearly 10 million cancer deaths worldwide in 2020 Cases, the top three cancer deaths, hepatocellular carcinoma ranked third, accounting for 8.3%. According to statistics from the Ministry of Health and Welfare, the total number of deaths due to cancer in Taiwan in 109 in 109 was 50,161, accounting for 29.0% of all deaths in Taiwan. The death rate for that year was 212.7 per 100,000 population, a decrease from 108 in the previous year. 1.8%, the mortality rate of the top ten cancers is ranked, and liver cancer ranks second. Liver cancer has a significant impact on human survival and health. However, the latest research from the National Institutes of Health shows that fatty liver, diabetes, and high triglycerides have become the three major risk factors for liver cancer in Chinese people. The research also breaks the traditional cancer myth, no alcohol addiction, no hepatitis, and liver cirrhosis. You may also suffer from liver cancer. Therefore, new metabolic disease risk factors should be included in the risk assessment of liver cancer. However, metabolic diseases are the most likely to be an epidemic of obesity from the appearance of potential risks. Therefore, we set out to study the relationship between obesity-related genes and liver cancer. Therefore, this study takes liver cancer as the research target. In order to explore the influence of obesity genes on liver cancer, we will analyze the expression of obesity-related genes in liver cancer patients and non-liver cancer patients, combined with patient clinical data, in order to discover the pathogenic factors of liver cancer. In this study, we searched the PubMed database for papers related to liver cancer and obesity genes. In order to efficiently sort out obesity-related gene groups, we used the convenient tool PubTator to do data exploration and screen out the gene groups. And use GEO-2R to obtain three sets of experimental data, a total of 379 cases, the experimental item is the difference in the expression of obesity-related genes between liver cancer patients and non-liver cancer patients. Conditional screening P-Value ≦0.01 is the analysis target, and After screening the obese genes of the three sets of experimental data with at least twice the amount of difference genes, a total of 447 identical genes were found. The common gene data were imported into the heatmap analyzed by GENE-E.The 447 common genes after the aforementioned screening were analyzed through the DAVID (The Database for Annotation, Visualization and Integrated Discovery, referred to as DAVID) analysis platform to explore their gene functions and metabolic pathways involved. Then use cBioPortal for cancer genomics to analyze whether the differentially expressed genes between liver cancer patients and non-liver cancer patients are associated with prognostic survival. Finally, use the literature for discussion and verification. It was found that the genes with different expression levels in this study: SPP1, GPC3, NQO1, CDK1, PEG10, HGF, HAMP, CYP2E1 were not related to the patient’s survival prognosis, but the genes found from the perspective of mutation rate: TP53 and APOB and survival The prognosis is more relevant, and similar results have been found in the investigation of Xiang DM et. al. It is worth noting that in the intersection of the Venn diagrams, it was found that the TENM1 gene is related to the prognosis of liver cancer, but there is no literature mentioning similar evidence, which may become a new research direction. Through a series of bio-information analysis and documentary evidence, we hope to provide researchers with a further in-depth understanding of liver cancer, which is expected to become a research direction for the treatment of related diseases. |
