基于生物信息学探讨蛋白磷酸酶-1催化亚基β（PPP1CB）在结直肠癌（CRC）中的表达、预后、功能富集、免疫浸润及药物敏感性分析。

分别从癌症基因组图谱（TCGA）和GEO数据库GSE41258下载CRC转录组及相应临床数据。根据PPP1CB表达中位数分组，确定两组间临床数据的差异情况。利用R语言survminer包分析PPP1CB高低表达组间的生存差异；对PPP1CB表达与临床特征进行单因素和多因素Cox回归分析，筛选出影响肿瘤进展的独立预后因素；基于独立预后因素构建预测预后情况的列线图，并通过矫正曲线和ROC对模型进行内外部评估；采用R语言limma包对PPP1CB高低组进行差异分析，在DAVID数据库中进行差异表达基因（DEGs）的功能富集分析；采用R语言cibersort包进行免疫浸润丰度的评估；利用OncoPredict包进行药物敏感性预测。两组间比较采用独立样本t检验，分类变量间比较采用卡方检验。

与正常组比较，PPP1CB在肿瘤组中表达降低（P < 0.001）；其低表达与患者预后不良相关（P = 0.021），在M0-1和N1-2亚组的生存分析中也得到相似结果（P = 0.012，P = 0.024）。ROC曲线下面积为0.746，提示其具有良好的诊断价值。单因素和多因素Cox回归分析结果显示，PPP1CB、年龄、T分期、N分期以及M分期可作为CRC的独立预后因素。模型的矫正曲线拟合度良好，1、3和5年的ROC曲线下面积均大于0.75。在PPP1CB高低表达组中，共有303个DEGs，其中297个基因表达增加，6个基因表达降低。功能富集分析显示，该差异基因主要富集在细胞周期、细胞肌动蛋白骨架调节等多个通路。免疫细胞浸润结果显示，在两组之间存在6种免疫细胞的表达差异，其中与Treg细胞相关性最高为- 0.416。OncoPredict包进一步筛选出9种可能针对CRC的治疗药物。

PPP1CB在CRC发生发展中可能扮演关键的抑癌基因角色，为CRC患者靶向治疗提供新的潜在靶点。

Using bioinformatics techniques to investigate the expression, diagnosis, prognosis, functional enrichment, immune invasion, and drug sensitivity of protein phosphatase-1 catalytic subunit β (PPP1CB) in colorectal cancer (CRC) .

The transcriptome data of CRC and corresponding clinical data were obtained from the Cancer Genome Atlas (TCGA) and GEO databases GSE41258, respectively. According to the median expression of PPP1CB, the differences in clinical data between the two groups were determined. The survival differences between the high and low expression groups of PPP1CB were analyzed using the R survminer package. Univariate and multivariate Cox regression analyses were performed using SPSS software to assess the association between PPP1CB expression and clinical features, and to identify independent prognostic factors influencing tumor progression. Based on these independent prognostic factors, a nomogram was constructed to predict prognosis, and the model was internally and externally validated using calibration curves and ROC analysis. The R limma package was utilized to analyze the differential expression of PPP1CB between high and low expression groups, and functional enrichment analysis of the differentially expressed genes (DEGs) was conducted using the DAVID database. The R language cibersort package was employed to assess the abundance of immune infiltration, while drug susceptibility prediction was performed using the OncoPredict package. The independent samples t-test was used between the two groups, and the chi-square test was used between the categorical variables.

There was no significant difference in clinical data between the two groups (P > 0.05). The expression of PPP1CB in CRC exhibited a significant reduction (P < 0.001). The low expression was associated with poor patient prognosis (P = 0.021), and similar findings were observed in the survival analysis of M0-1 and N1-2 subgroups (P = 0.012, P = 0.024). The area under the ROC curve was 0.746, indicating its favourable diagnostic value. Univariate and multivariate Cox regression analysis revealed that PPP1CB, age, T stage, N stage, and M stage could serve as independent prognostic factors for CRC, leading to the construction of a nomogram. The model demonstrated a satisfactory fit, and the area under the ROC curve at 1, 3, and 5 years exceeded 0.75. Within the high and low expression groups of PPP1CB, a total of 303 DEGs, with 297 genes showing increased expression and six genes showing decreased expression. Functional enrichment analysis highlighted the enrichment of differential genes in various pathways, including cell cycle and cellular actin backbone regulation. Additionally, the immune cell infiltration analysis results indicated six differences in immune cell expression between the two groups, with the strongest correlation observed with Treg cells (- 0.416). Further utilization of the OncoPredict package identified nine potential treatments for CRC.

The potential role of PPP1CB as a tumor suppressor gene in the initiation and progression of CRC has been identified, presenting a novel therapeutic target for precision treatment in patients with CRC.