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Abstract：
UNASSIGNED: Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths, and improving the prognosis of CRC patients is an urgent concern. The aim of this study was to explore new immunotherapy targets to improve survival in CRC patients.
UNASSIGNED: We analyzed CRC-related single-cell data GSE201348 from the Gene Expression Omnibus (GEO) database, and identified differentially expressed genes (DEGs). Subsequently, we performed differential analysis on the rectum adenocarcinoma (READ) and colon adenocarcinoma (COAD) transcriptome sequencing data [The Cancer Genome Atlas (TCGA)-CRC queue] and clinical data downloaded from TCGA database. Subgroup analysis was performed using CIBERSORTx and cluster analysis. Finally, biomarkers were identified by one-way cox regression as well as least absolute shrinkage and selection operator (LASSO) analysis.
UNASSIGNED: In this study, we analyzed CRC-related single-cell data GSE201348, and identified 5,210 DEGs. Subsequently, we performed differential analysis on the TCGA-CRC queue database, and obtained 4,408 DEGs. Then, we categorized the cancer samples in the sequencing data into three groups (k1, k2, and k3), with significant differences observed between the k1 and k2 groups via survival analysis. Further differential analysis on the samples in the k1 and k2 groups identified 1,899 DEGs. A total of 77 DEGs were selected among those DEGs obtained from three differential analyses. Through subsequent Cox univariate analysis and LASSO analysis, seven biomarkers (RETNLB, CLCA4, UGT2A3, SULT1B1, CCL24, BMP5, and ATOH1) were identified and selected to establish a risk score (RS).
UNASSIGNED: To sum up, this study demonstrates the potential of the seven-gene prognostic risk model as instrumental variables for predicting the prognosis of CRC.
UNASSIGNED: We analyzed CRC-related single-cell data GSE201348 from the Gene Expression Omnibus (GEO) database, and identified differentially expressed genes (DEGs). Subsequently, we performed differential analysis on the rectum adenocarcinoma (READ) and colon adenocarcinoma (COAD) transcriptome sequencing data [The Cancer Genome Atlas (TCGA)-CRC queue] and clinical data downloaded from TCGA database. Subgroup analysis was performed using CIBERSORTx and cluster analysis. Finally, biomarkers were identified by one-way cox regression as well as least absolute shrinkage and selection operator (LASSO) analysis.
UNASSIGNED: In this study, we analyzed CRC-related single-cell data GSE201348, and identified 5,210 DEGs. Subsequently, we performed differential analysis on the TCGA-CRC queue database, and obtained 4,408 DEGs. Then, we categorized the cancer samples in the sequencing data into three groups (k1, k2, and k3), with significant differences observed between the k1 and k2 groups via survival analysis. Further differential analysis on the samples in the k1 and k2 groups identified 1,899 DEGs. A total of 77 DEGs were selected among those DEGs obtained from three differential analyses. Through subsequent Cox univariate analysis and LASSO analysis, seven biomarkers (RETNLB, CLCA4, UGT2A3, SULT1B1, CCL24, BMP5, and ATOH1) were identified and selected to establish a risk score (RS).
UNASSIGNED: To sum up, this study demonstrates the potential of the seven-gene prognostic risk model as instrumental variables for predicting the prognosis of CRC.
摘要：
■结直肠癌（CRC）是癌症相关死亡的主要原因之一，改善CRC患者的预后是当务之急。这项研究的目的是探索新的免疫治疗靶点，以提高CRC患者的生存率。
■我们分析了来自基因表达综合（GEO）数据库的CRC相关单细胞数据GSE201348，并鉴定了差异表达基因(DEGs)。随后，我们对直肠腺癌(READ)和结肠腺癌(COAD)转录组测序数据[癌症基因组图谱(TCGA)-CRC队列]以及从TCGA数据库下载的临床数据进行了差异分析.亚组分析采用CIBERSORTx和聚类分析。最后,通过单向cox回归以及最小绝对收缩和选择算子(LASSO)分析鉴定生物标志物。
■在这项研究中，我们分析了CRC相关的单细胞数据GSE201348,确定了5,210DEGs.随后，我们对TCGA-CRC队列数据库进行了差异分析，获得了4,408度。然后，我们将测序数据中的癌症样本分为三组（k1，k2和k3），通过生存分析观察到k1和k2组之间存在显着差异。对k1和k2组样品的进一步差异分析确定了1,899个DEG。从三个差异分析获得的DEG中总共选择了77个DEG。通过随后的Cox单变量分析和LASSO分析，七个生物标志物（RETNLB，CLCA4，UGT2A3，SULT1B1，CCL24，BMP5和ATOH1）被识别并选择以建立风险评分（RS）。
■总而言之，这项研究证明了七基因预后风险模型作为预测CRC预后的工具变量的潜力.
■我们分析了来自基因表达综合（GEO）数据库的CRC相关单细胞数据GSE201348，并鉴定了差异表达基因(DEGs)。随后，我们对直肠腺癌(READ)和结肠腺癌(COAD)转录组测序数据[癌症基因组图谱(TCGA)-CRC队列]以及从TCGA数据库下载的临床数据进行了差异分析.亚组分析采用CIBERSORTx和聚类分析。最后,通过单向cox回归以及最小绝对收缩和选择算子(LASSO)分析鉴定生物标志物。
■在这项研究中，我们分析了CRC相关的单细胞数据GSE201348,确定了5,210DEGs.随后，我们对TCGA-CRC队列数据库进行了差异分析，获得了4,408度。然后，我们将测序数据中的癌症样本分为三组（k1，k2和k3），通过生存分析观察到k1和k2组之间存在显着差异。对k1和k2组样品的进一步差异分析确定了1,899个DEG。从三个差异分析获得的DEG中总共选择了77个DEG。通过随后的Cox单变量分析和LASSO分析，七个生物标志物（RETNLB，CLCA4，UGT2A3，SULT1B1，CCL24，BMP5和ATOH1）被识别并选择以建立风险评分（RS）。
■总而言之，这项研究证明了七基因预后风险模型作为预测CRC预后的工具变量的潜力.
