第一部分

KITLG与APOBEC家族成员促进胰腺癌发生发展的分子机制研究

第一章

KITLG促进胰腺癌发生发展的分子机制研究 

摘要

背景：胰腺癌是一种恶性程度高、预后极差的消化系统肿瘤，缺少有效治疗靶点和分子标记物是目前制约疗效的主要瓶颈之一。KITLG在多种癌症中扮演重要角色，但在胰腺癌中的具体作用尚未阐明。本研究旨在通过体内外实验全面探索KITLG在胰腺癌发生发展中的生物学功能，并揭示其潜在的分子机制。

方法：采用了五种胰腺癌细胞系（SW1990、AsPC-1、MIA-PACA-2、BxPC-3和PANC-1），通过qPCR和Western Blot分析KITLG、C-Kit和SLC1A4等基因的表达水平。构建慢病毒载体进行KITLG、SLC1A4和BDH1等基因的敲降和过表达实验，联合CCK-8实验、克隆成型实验、Transwell实验、细胞划痕实验和动物实验等研究从体内外水平探索KITLG、SLC1A4和BDH1等基因对胰腺癌细胞增殖和迁移能力的影响。使用KITLG促进剂SCF和抑制剂ISCK03探索KITLG的表达水平变化对伊马替尼药物敏感性的影响。采用转录组测序和代谢组学分析技术深入探索与KITLG促癌作用相关的信号通路和下游靶点。

结果：KITLG和C-Kit在不同胰腺癌细胞系中的转录水平存在差异。KITLG活化剂 SCF的浓度越大，对PANC-1细胞增殖和迁移能力的促进作用越强，KITLG抑制剂ISCK03 浓度越大，对 PANC-1 细胞增殖和迁移能力的抑制作用越强。在一定浓度下，伊马替尼和 ISCK03 联合使用能够达到协同抑制 PANC-1 细胞增殖和迁移的作用。三种不同浓度 （1ng/mL，10ng/mL和100ng/mL） 的 KITLG活化剂SCF与10mM/mL的伊马替尼联合使用时，SCF的浓度越大，伊马替尼对 PANC-1 细胞增殖能力的抑制作用越强。降低 KITLG 的表达水平能够明显抑制小鼠体内肿瘤的生长。KITLG通过调控氨基酸代谢相关通路发挥促进胰腺癌发生发展的作用，SLC1A4可能是参与这一过程的关键靶基因。随着 IFN-γ浓度的增加和刺激时间的延长，敲低 KITLG 能够明显抑制 PD-L1的表达。

结论：KITLG通过调控氨基酸代谢相关通路发挥促进胰腺癌发生发展的作用，SLC1A4可能是参与这一过程的关键靶基因，KITLG还可能在IFN-γ的作用下通过调节PD-L1的表达水平而增加胰腺癌细胞的免疫逃逸能力。

第二章

APOBEC家族成员在胰腺癌中的致癌作用 

摘要

背景：许多证据表明APOBEC家族与多种癌症的发生发展有关。然而，APOBEC1、APOBEC3A、APOBEC3G和APOBEC3H在胰腺癌中的功能仍不清楚。

方法：使用R软件（版本4.2.0）、TCGA、GTEx、Kaplan-Meier Plotter、cBioPortal、TISIDB、LinkedOmics、Metascape、TIMER、GSCALite、STRING和Cytoscape等在线分析数据库和软件进行了全面的生物信息学分析，以研究APOBEC1、APOBEC3A、APOBEC3G和APOBEC3H在胰腺癌中的致癌作用。

结果：APOBEC1、APOBEC3A、APOBEC3G和APOBEC3H在胰腺癌中的表达显著高于癌旁组织或正常组织。它们的高表达或扩增与胰腺癌患者更差的临床病理特征和预后显著相关。此外，APOBEC1、APOBEC3A、APOBEC3G和APOBEC3H在免疫调控中的作用多样且复杂，APOBEC1的高表达可能抑制多种免疫活性肿瘤浸润细胞的浸润水平，这可能是导致胰腺癌细胞免疫逃逸的重要因素。在机制上，APOBEC1、APOBEC3A、APOBEC3G和APOBEC3H在多个致癌途径中起激活作用，包括EMT、RAS/MAPK和TSC/mTOR途径。此外，我们发现APOBEC3G的表达水平与吉西他滨和多柔比星的敏感性呈正相关。

结论： APOBEC1、APOBEC3A、APOBEC3G及APOBEC3H的高表达水平与胰腺癌患者不良临床病理特征及更差预后相关。APOBEC1与多种免疫活性肿瘤浸润细胞的浸润水平呈负相关，并且与大部分免疫促进剂的表达水平呈负相关，可能具有抑制机体对胰腺癌免疫应答能力的作用。APOBEC1、APOBEC3A、APOBEC3G及APOBEC3H在多种致癌信号通路中发挥激活作用，可能是促进胰腺癌发生发展的潜在机制。APOBEC3G高表达的胰腺癌患者选用吉西他滨或多柔比星进行治疗可能有更好的治疗效果。

第二部分

使用 Lasso-logistic 回归开发和验证一种新型的胰腺切除术后出血预测模型：一项针对9631名胰腺切除患者的国际多中心观察性研究

摘要

背景: 胰腺切除术后出血是一种严重并发症，对患者的预后产生重大影响。为了改善手术安全和患者预后，开发一个准确预测胰腺切除术后出血风险的模型具有重要意义。

方法: 采用美国国家外科质量改进计划数据库2014年至2017年间进行胰腺切除手术的患者队列（n=5779）作为训练队列建立Lasso-logistic模型，使用中国的全国多中心胰腺切除患者数据库2014年至2020年间接受胰腺切除手术的患者队列（n=3852）作为外部验证队列对模型进行验证。建立胰腺切除术后出血的预测列线图，并提取多项式方程。通过受试者工作特征曲线，校准曲线和决策曲线分析来评估预测模型的性能。

结果: 在训练和验证队列中，分别有 9.0% （520/5779）和8.5% （328/3852）的患者出现胰腺切除术后出血。使用lasso和logistic回归进行筛选后，只有9个预测因子是与胰腺切除术后出血独立相关的危险因素，包括5个术前指标（BMI、ASA≥3、术前梗阻性黄疸、术前90天内化疗和术前90天内放疗），2个术中指标（手术时间和血管切除）和2个术后指标（术后感染性休克和胰瘘）。新的模型预测准确性较高，在外部验证队列中受试者工作特征曲线下面积为0.87，预测性能优于之前的五种胰腺切除术后出血风险预测模型（P<0.001，似然比检验）。

结论: 手术时间、BMI、ASA≥3、术前梗阻性黄疸、术前90天内化疗、术前90天内放疗、术后感染性休克、血管切除和胰瘘可能是胰腺切除术后出血的危险因素。

Abstract

Background: Pancreatic cancer is a highly malignant digestive system tumor with an extremely poor prognosis, and the lack of effective therapeutic targets and molecular markers is one of the main bottlenecks currently limiting treatment efficacy. KITLG plays an important role in various cancers, but its specific function in pancreatic cancer has not yet been elucidated. This study aims to comprehensively explore the biological functions of KITLG in the occurrence and development of pancreatic cancer through in vivo and in vitro experiments, and to reveal its potential molecular mechanisms.

Methods: Five pancreatic cancer cell lines (SW1990, AsPC-1, MIA-PACA-2, BxPC-3, and PANC-1) were employed to analyze the expression levels of KITLG, C-Kit, and SLC1A4, among other genes, through qPCR and Western Blot analysis. Lentiviral vectors were constructed to conduct knockdown and overexpression experiments for genes such as KITLG, SLC1A4, and BDH1, in conjunction with CCK-8 assays, colony formation assays, Transwell assays, wound healing assays, and animal experiments, to investigate the effects of genes like KITLG, SLC1A4, and BDH1 on the proliferation and migration capabilities of pancreatic cancer cells both in vitro and in vivo. The use of KITLG stimulant SCF and inhibitor ISCK03 explored the impact of changes in KITLG expression levels on the sensitivity to the drug Imatinib. Transcriptome sequencing and metabolomics analysis techniques were utilized to delve into the signaling pathways and downstream targets associated with KITLG's oncogenic effects.

Results: There are differences in the transcriptional levels of KITLG and C-Kit across various pancreatic cancer cell lines. The higher the concentration of the KITLG activator SCF, the stronger its promotive effect on the proliferation and migration capabilities of PANC-1 cells; similarly, the higher the concentration of the KITLG inhibitor ISCK03, the stronger its inhibitory effect on the proliferation and migration capabilities of PANC-1 cells. At certain concentrations, the combined use of Imatinib and ISCK03 can achieve a synergistic inhibitory effect on the proliferation of PANC-1 cells. When three different concentrations (1ng/mL, 10ng/mL, and 100ng/mL) of the KITLG activator SCF are used in combination with 10mM/mL of Imatinib, the higher the concentration of SCF, the stronger the inhibitory effect of Imatinib on the proliferation capability of PANC-1 cells. Lowering the expression level of KITLG can significantly inhibit tumor growth in mice. KITLG promotes the occurrence and development of pancreatic cancer through regulating amino acid metabolism-related pathways, with SLC1A4 potentially being a key target gene involved in this process. With increasing concentrations of IFN-γ and extended stimulation time, knocking down KITLG can significantly inhibit the expression of PD-L1.

Conclusion: KITLG promotes the occurrence and development of pancreatic cancer by regulating amino acid metabolism-related pathways, with SLC1A4 potentially being a key target gene involved in this process. Furthermore, under the influence of IFN-γ, KITLG may also increase the immune evasion capabilities of pancreatic cancer cells by modulating the expression levels of PD-L1.

Abstract

Background: Numerous pieces of evidence suggest that the APOBEC family is associated with the occurrence and development of various cancers. However, the functions of APOBEC1, APOBEC3A, APOBEC3G, and APOBEC3H in pancreatic cancer remain unclear.

Methods: Comprehensive bioinformatics analyses were conducted using R software (version 4.2.0) and online analysis databases and tools including TCGA, GTEx, Kaplan-Meier Plotter, cBioPortal, TISIDB, LinkedOmics, Metascape, TIMER, GSCALite, STRING, and Cytoscape to investigate the carcinogenic roles of APOBEC1, APOBEC3A, APOBEC3G, and APOBEC3H in pancreatic cancer.

Results: The expression of APOBEC1, APOBEC3A, APOBEC3G, and APOBEC3H in pancreatic cancer is significantly higher than in adjacent cancerous tissues or normal tissues. Their high expression or amplification is significantly associated with worse clinicopathological features and prognosis in patients with pancreatic cancer. Furthermore, the roles of APOBEC1, APOBEC3A, APOBEC3G, and APOBEC3H in immune regulation are diverse and complex. The high expression of APOBEC1 may inhibit the infiltration level of various immune-active tumor-infiltrating cells, which could be a crucial factor leading to immune evasion of pancreatic cancer cells. Mechanistically, APOBEC1, APOBEC3A, APOBEC3G, and APOBEC3H activate multiple carcinogenic pathways, including EMT, RAS/MAPK, and TSC/mTOR pathways. Additionally, we found that the expression level of APOBEC3G is positively correlated with the sensitivity to gemcitabine and doxorubicin.

Conclusion: The high expression levels of APOBEC1, APOBEC3A, APOBEC3G, and APOBEC3H are associated with adverse clinicopathological characteristics and poorer prognosis in patients with pancreatic cancer. APOBEC1's expression is negatively correlated with the infiltration levels of various immune-active tumor-infiltrating cells and is negatively associated with the expression levels of most immune stimulants, potentially acting to inhibit the body's immune response capability against pancreatic cancer. APOBEC1, APOBEC3A, APOBEC3G, and APOBEC3H activate multiple carcinogenic signaling pathways, possibly serving as a potential mechanism promoting the occurrence and development of pancreatic cancer. Patients with high expression of APOBEC3G in pancreatic cancer may achieve better therapeutic effects when treated with gemcitabine or docetaxel.

Abstract

Background: Hemorrhage following pancreatectomy represents a grave complication, exerting a significant impact on patient prognosis. The formulation of a precise predictive model for postpancreatectomy hemorrhage risk holds substantial importance in enhancing surgical safety and improving patient outcomes.

Methods: This study utilized the patient cohort from the American College of Surgeons National Surgical Quality Improvement Program database, who underwent pancreatectomy between 2014 and 2017 (n=5779), as the training set to establish the Lasso-logistic model. For external validation, a patient cohort (n=3852) from the Chinese National Multicenter Database of Pancreatectomy Patients, who underwent the procedure between 2014 and 2020, was employed. A predictive nomogram for postpancreatectomy hemorrhage was developed, and polynomial equations were extracted. The performance of the predictive model was assessed through the receiver operating characteristic curve, calibration curve, and decision curve analysis.

Results: In the training and validation cohorts, 9.0% (520/5779) and 8.5% (328/3852) of patients, respectively, experienced postpancreatectomy hemorrhage. Following selection via lasso and logistic regression, only nine predictive factors were identified as independent risk factors associated with postpancreatectomy hemorrhage. These included five preoperative indicators (BMI, ASA ≥3, preoperative obstructive jaundice, chemotherapy within 90 days before surgery, and radiotherapy within 90 days before surgery), two intraoperative indicators (total operation time, vascular resection), and two postoperative indicators (postoperative septic shock, pancreatic fistula). The new model demonstrated high predictive accuracy, with an area under the receiver operating characteristic curve of 0.87 in the external validation cohort. Its predictive performance significantly surpassed that of the previous five postpancreatectomy hemorrhage risk prediction models (P<0.001, likelihood ratio test).

Conclusion: Surgical duration, BMI, ASA≥3, preoperative obstructive jaundice, chemotherapy within 90 days before surgery, radiation therapy within 90 days before surgery, postoperative septic shock, vascular resection, and pancreatic fistula may be risk factors for postoperative hemorrhage following pancreatic resection.