肾癌相关细胞癌变早筛检测数据

癌症早筛检测，通过检测15毫升血液中DNA碎片里面是否含有癌特异性突变，实现对细胞癌化信号的早期排查。采用专利分子捕获技术和高通量测序技术检测血液中来自不同组织癌化细胞的DNA小片段，覆盖关键抑癌基因中超1900种特异突变指标，对体内是否存在癌化克隆细胞（癌细胞的前体）、存在多少以及在哪里进行计算，可对正常细胞的癌化进程进行定量监控，能让预警发生在细胞癌变前，及早发现和治疗肾癌患者，减少因晚期治疗而带来的高昂医疗费用和医疗资源的浪费。 1.数据预处理：质量控制：首先对原始的FASTQ文件进行质控，检查序列数据的质量，确保数据的准确性。过滤掉低质量读段，移除污染序列，保证后续分析的可靠性。去接头：去除接头序列和低质量的碱基，确保分析过程中只使用高质量的读段。2.序列比对：映射到参考基因组：使用比对工具将质控后的读段映射到参考基因组。3.变异检测：突变位点检测：选择特定的变异检测工具，对比对后的序列数据进行变异检测。检测单核苷酸多态性和插入缺失变异，并注释每个突变位点的变异类型（例如错义突变、同义突变等）。4.过滤显著变异位点：基于变异频率、测序深度和统计显著性（如P值等）对检测到的变异位点进行过滤。去除假阳性和低置信度的位点，保留显著的候选突变位点。5.临床信息关联：位点功能注释：结合选择的BED文件对显著变异位点进行功能注释。判断这些位点在基因组中的位置（如是否位于功能重要区域或已知的致病性位点）。6.癌症相关性分析：利用注释信息和临床数据库，分析突变位点与不同类型癌症的关联性。7.最终输出本数据，包含显著突变位点、基因名、体突变(氨基酸)等癌症相关信息，结合临床数据，为早期癌症筛查提供参考依据。Cancer early screening test enables early screening for cellular carcinogenic signals by detecting whether cancer-specific mutations exist in DNA fragments extracted from 15 mL of blood. It adopts patented molecular capture technology and high-throughput sequencing (HTS) to detect small DNA fragments from cancerous cells of different tissues in blood, covering over 1900 specific mutation markers across key tumor suppressor genes. It quantitatively calculates the presence, quantity and location of premalignant clonal cells (precursors of cancer cells) in the body, enabling real-time quantitative monitoring of the carcinogenesis process of normal cells. This allows early warning prior to cellular malignant transformation, facilitating early detection and treatment of renal cancer patients, and reducing exorbitant medical costs and waste of medical resources incurred by late-stage cancer treatment. 1. Data Preprocessing: Quality Control: First, perform quality control (QC) on the original FASTQ files to assess the quality of sequence data and ensure data accuracy. Filter out low-quality reads and contaminant sequences to guarantee the reliability of subsequent analysis. Adapter Trimming: Remove adapter sequences and low-quality bases to ensure only high-quality reads are utilized in the analysis. 2. Sequence Alignment: Mapping to Reference Genome: Use alignment tools to map the quality-controlled reads to the reference genome. 3. Variant Detection: Mutation Site Detection: Select specific variant detection tools to conduct variant calling on the aligned sequence data. Detect single nucleotide polymorphisms (SNPs) and insertions-deletions (indels), and annotate the variant type of each mutation site (e.g., missense mutation, synonymous mutation, etc.). 4. Filtering of Significant Variant Sites: Filter the detected variant sites based on variant frequency, sequencing depth and statistical significance (such as P-value). Remove false-positive and low-confidence sites, and retain significant candidate mutation sites. 5. Association with Clinical Information: Functional Annotation of Variant Sites: Perform functional annotation on the identified significant variant sites using the selected BED file. Determine the genomic positions of these sites (e.g., whether they lie in functionally critical regions or known pathogenic loci). 6. Cancer Correlation Analysis: Use annotation information and clinical databases to analyze the correlation between mutation sites and different types of cancers. 7. Final Dataset Output: The output encompasses cancer-related information including significant mutation sites, gene names, somatic mutations (amino acid alterations), etc. Combined with clinical data, it serves as a reliable reference for early cancer screening.