Whole-exome Sequencing Analysis For Identification Of Cancer-causing Novel Variants In Taiwanese Ovarian Cancer Cell Lines

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题名:	Whole-exome Sequencing Analysis For Identification Of Cancer-causing Novel Variants In Taiwanese Ovarian Cancer Cell Lines Whole-exome Sequencing Analysis For Identification Of Cancer-causing Novel Variants In Taiwanese Ovarian Cancer Cell Lines
作者:	Reddy, Mekala Venugopala REDDY, MEKALA VENUGOPALA
贡献者:	生物資訊與醫學工程學系
关键词:	whole-exome sequencing;next-generation sequencing;cell lines;ovarian cancer;structural variants;missense variant whole-exome sequencing;next-generation sequencing;cell lines;ovarian cancer;structural variants;missense variant
日期:	2022-07-11
上传时间:	2022-10-31 02:53:40 (UTC+0)
出版者:	亞洲大學
摘要:	ABSTRACTMalignant or cancer growth is completely due to abnormal brought about by an assembly of genetic and epigenetic variations. These variants resulted in changes during nucleotide sequencing, sometimes further leading to an effect on other parts of the sequencing. Continuously changing a single mutation can cause initiation or inactivation of genetic functions. Voluminous variants have no effect on genes, but a few are extremely unsafe. Recently, medicine is changing its way by finding new methods and targets for the alteration of structural variants in cancer diseases. There are countless variants were identified in every study by using advanced computational tools, but when it came to the validation section it fails for confirmation. Currently, everyone is focused on novel variants in cancer genes, and getting control of those identified variants is a big challenge, especially in pathological and clinical conditions of cancer patients. Many complications have been overcome by developing next-generation sequencing analysis techniques. Presently, women are often facing gynecological cancers and symptoms during their lifetime. It is difficult to diagnose in the early stage, patient event information can detect in advanced stage leads to difficult for medical treatment. In the end patient may meet with chemotherapy or even death. To overcome this, cell lines are the best way to reveal treasures to find and elucidate cancer mutations. Nowadays, there are core cancer-causing novel variants are found and successfully treating cancer patients. In the present study, three ovarian cancer cell lines: OCPC-2-VGH, OC-3-VGH and OC-109-VGH, with different pathological stages were selected to understand structural variants. Furthermore, various preprocessing quality control analysis was performed for sequences of the three cell lines separately. After general trimming and the remaining sequencing reads were mapped to the human genome assembly hg19. After this process, reference mappings of cell line sequences were stored in FASTQ data as BAM file. In addition, the number of structural changes observed in cell lines were saved in variant calling files (VCF). We performed whole-exome sequencing analysis to prioritize structural variants in each cell line. First, we used VCF files in the China Medical University Hospital (CMUH) developed pipeline (Internal use only - same as Genome Analysis Tool Kit-GATK) to extract all the variant information of the three cell lines. The evaluation step involved population frequency filtering criteria, predictive tools, evidence-based and functional impact of structural variants. By utilizing these results, we comprehensively explore all variants in various populations and calculate variant allele frequency. Then, we isolated novel variants by comparing with of the TCGA, COSMIC, and 188 CMUH patient whole-exome sequencing (WES) data. Next, structural variants genomic coordinates are used to perform alternative splicing and further predicted variant genes involvements in KEGG pathways, driver genes, ACMG variant relations, and gene-phenotypic relationships. Structural variants that belong to missense mutations are selected for protein 3D strictures stability studies. Finally, we validated the novel cancer variants by performing PCR sequencing experiments. This study applied bioinformatics analyses and identified 296 novel variants, which may cause cancer in three human ovarian cancer cell lines. Alternative splicing analysis revealed cancerous protein isoforms that have missense mutations. Some novel variant genes are recognized as cancer driver genes. AFF1, IKBKB, PLCG2, PRKACA, PLCB3, TRAF1, PRKACA, ADCY6 and VEGFA genes are directly involved in cancer pathways (KEGG: hsa05200). Results show that novel variants belonging to missense mutations are observed as 11 novel variants in cancerous genes, and 54 novel variants belong to non-cancerous genes. We traced missense mutations that can cause unstable proteins in canonical isoforms by performing 3D structure analysis. Finally, PCR sequencing results indicate that there is a total of 11 cancer causing novel variants in the ovarian cell lines. Overall, the results of this developed method based on WES, alternative sequencing and 3D protein structure stability analysis, that enable us to predict significant cancer-causing novel variants in ovarian cancer cell lines or patients and identified 11 cancer novel variants; this can be used as an effective model in precision medicine for individual patient. ABSTRACTMalignant or cancer growth is completely due to abnormal brought about by an assembly of genetic and epigenetic variations. These variants resulted in changes during nucleotide sequencing, sometimes further leading to an effect on other parts of the sequencing. Continuously changing a single mutation can cause initiation or inactivation of genetic functions. Voluminous variants have no effect on genes, but a few are extremely unsafe. Recently, medicine is changing its way by finding new methods and targets for the alteration of structural variants in cancer diseases. There are countless variants were identified in every study by using advanced computational tools, but when it came to the validation section it fails for confirmation. Currently, everyone is focused on novel variants in cancer genes, and getting control of those identified variants is a big challenge, especially in pathological and clinical conditions of cancer patients. Many complications have been overcome by developing next-generation sequencing analysis techniques. Presently, women are often facing gynecological cancers and symptoms during their lifetime. It is difficult to diagnose in the early stage, patient event information can detect in advanced stage leads to difficult for medical treatment. In the end patient may meet with chemotherapy or even death. To overcome this, cell lines are the best way to reveal treasures to find and elucidate cancer mutations. Nowadays, there are core cancer-causing novel variants are found and successfully treating cancer patients. In the present study, three ovarian cancer cell lines: OCPC-2-VGH, OC-3-VGH and OC-109-VGH, with different pathological stages were selected to understand structural variants. Furthermore, various preprocessing quality control analysis was performed for sequences of the three cell lines separately. After general trimming and the remaining sequencing reads were mapped to the human genome assembly hg19. After this process, reference mappings of cell line sequences were stored in FASTQ data as BAM file. In addition, the number of structural changes observed in cell lines were saved in variant calling files (VCF). We performed whole-exome sequencing analysis to prioritize structural variants in each cell line. First, we used VCF files in the China Medical University Hospital (CMUH) developed pipeline (Internal use only - same as Genome Analysis Tool Kit-GATK) to extract all the variant information of the three cell lines. The evaluation step involved population frequency filtering criteria, predictive tools, evidence-based and functional impact of structural variants. By utilizing these results, we comprehensively explore all variants in various populations and calculate variant allele frequency. Then, we isolated novel variants by comparing with of the TCGA, COSMIC, and 188 CMUH patient whole-exome sequencing (WES) data. Next, structural variants genomic coordinates are used to perform alternative splicing and further predicted variant genes involvements in KEGG pathways, driver genes, ACMG variant relations, and gene-phenotypic relationships. Structural variants that belong to missense mutations are selected for protein 3D strictures stability studies. Finally, we validated the novel cancer variants by performing PCR sequencing experiments. This study applied bioinformatics analyses and identified 296 novel variants, which may cause cancer in three human ovarian cancer cell lines. Alternative splicing analysis revealed cancerous protein isoforms that have missense mutations. Some novel variant genes are recognized as cancer driver genes. AFF1, IKBKB, PLCG2, PRKACA, PLCB3, TRAF1, PRKACA, ADCY6 and VEGFA genes are directly involved in cancer pathways (KEGG: hsa05200). Results show that novel variants belonging to missense mutations are observed as 11 novel variants in cancerous genes, and 54 novel variants belong to non-cancerous genes. We traced missense mutations that can cause unstable proteins in canonical isoforms by performing 3D structure analysis. Finally, PCR sequencing results indicate that there is a total of 11 cancer causing novel variants in the ovarian cell lines. Overall, the results of this developed method based on WES, alternative sequencing and 3D protein structure stability analysis, that enable us to predict significant cancer-causing novel variants in ovarian cancer cell lines or patients and identified 11 cancer novel variants; this can be used as an effective model in precision medicine for individual patient.
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