Pancreatic cancer is the fourth leading cause of cancer deaths in the United States both in females and in males, and is projected to become the second deadliest cancer by 2030. The overall 5-year survival rate remains at around 10%. Cancer metabolism and specifically lipid metabolism plays an important role in pancreatic cancer progression and metastasis. Lipid droplets can not only store and transfer lipids, but also act as molecular messengers, and signaling factors. As lipid droplets are implicated in reprogramming tumor cell metabolism and in invasion and migration of pancreatic cancer cells, we aimed to identify lipid droplet-associated genes as prognostic markers in pancreatic cancer. We performed a literature search on review articles related to lipid droplet-associated proteins. To select relevant lipid droplet-associated factors, bioinformatics analysis on the GEPIA platform (data are publicly available) was carried out for selected genes to identify differential expression in pancreatic cancer versus healthy pancreatic tissues. Differentially expressed genes were further analyzed regarding overall survival of pancreatic cancer patients. 65 factors were identified as lipid droplet-associated factors. Bioinformatics analysis of 179 pancreatic cancer samples and 171 normal pancreatic tissue samples on the GEPIA platform identified 39 deferentially expressed genes in pancreatic cancer with 36 up-regulated genes (ACSL3, ACSL4, AGPAT2, BSCL2, CAV1, CAV2, CAVIN1, CES1, CIDEC, DGAT1, DGAT2, FAF2, G0S2, HILPDA, HSD17B11, ICE2, LDAH, LIPE, LPCAT1, LPCAT2, LPIN1, MGLL, NAPA, NCEH1, PCYT1A, PLIN2, PLIN3, RAB5A, RAB7A, RAB8A, RAB18, SNAP23, SQLE, VAPA, VCP, VMP1) and 3 down-regulated genes (FITM1, PLIN4, PLIN5). Among 39 differentially expressed factors, seven up-regulated genes (CAV2, CIDEC, HILPDA, HSD17B11, NCEH1, RAB5A, and SQLE) and two down-regulation genes (BSCL2 and FITM1) were significantly associated with overall survival of pancreatic cancer patients. Multivariate Cox regression analysis identified CAV2 as the only independent prognostic factor. Through bioinformatics analysis, we identified nine prognostic relevant differentially expressed genes highlighting the role of lipid droplet-associated factors in pancreatic cancer.

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通过生物信息学分析鉴定胰腺癌患者预后脂滴相关基因

胰腺癌是美国女性和男性癌症死亡的第四大原因，预计到 2030 年将成为第二大致命癌症。总体 5 年生存率保持在 10% 左右。癌症代谢，特别是脂质代谢在胰腺癌的进展和转移中起着重要作用。脂滴不仅可以储存和转移脂质，还可以作为分子信使和信号因子。由于脂滴涉及重编程肿瘤细胞代谢以及胰腺癌细胞的侵袭和迁移，我们的目标是鉴定脂滴相关基因作为胰腺癌的预后标志物。我们对与脂滴相关蛋白相关的评论文章进行了文献检索。选择相关的脂滴相关因子，GEPIA 平台上的生物信息学分析（数据可公开获得）对选定的基因进行了分析，以确定胰腺癌与健康胰腺组织中的差异表达。进一步分析了关于胰腺癌患者总生存期的差异表达基因。65 个因素被确定为脂滴相关因素。在 GEPIA 平台上对 179 个胰腺癌样本和 171 个正常胰腺组织样本进行生物信息学分析，确定了胰腺癌中的 39 个顺从表达基因，其中 36 个上调基因（ACSL3、ACSL4、AGPAT2、BSCL2、CAV1、CAV2、CAVIN1、CES1、CIDEC、 DGAT1, DGAT2, FAF2, G0S2, HILPDA, HSD17B11, ICE2, LDAH, LIPE, LPCAT1, LPCAT2, LPIN1, MGLL, NAPA, NCEH1, PCYT1A, PLIN2, PLIN3, RAB5A, RAB7A, NAPVA, RABPA, RAB18A VCP, VMP1) 和 3 个下调基因 (FITM1、PLIN4、PLIN5)。在 39 个差异表达因子中，7 个上调基因（CAV2、CIDEC、HILPDA、HSD17B11、NCEH1、RAB5A 和 SQLE）和两个下调基因（BSCL2 和 FITM1）与胰腺癌患者的总生存期显着相关。多变量 Cox 回归分析确定 CAV2 是唯一的独立预后因素。通过生物信息学分析，我们确定了九个预后相关的差异表达基因，突出了脂滴相关因素在胰腺癌中的作用。和 SQLE）和两个下调基因（BSCL2 和 FITM1）与胰腺癌患者的总生存期显着相关。多变量 Cox 回归分析确定 CAV2 是唯一的独立预后因素。通过生物信息学分析，我们确定了九个预后相关的差异表达基因，突出了脂滴相关因素在胰腺癌中的作用。和 SQLE）和两个下调基因（BSCL2 和 FITM1）与胰腺癌患者的总生存期显着相关。多变量 Cox 回归分析确定 CAV2 是唯一的独立预后因素。通过生物信息学分析，我们确定了九个预后相关的差异表达基因，突出了脂滴相关因素在胰腺癌中的作用。