Skip to main content
Key Specialties
Anesthesiology Cardiology Dermatology Diabetology Endocrinology Gastroenterology General Medicine Geriatrics Hematology Infectious Diseases Internal Medicine Nephrology Neurology Obstetrics and Gynecology Oncology Pediatrics Psychiatry Radiology Respiratory Medicine Rheumatology Surgery Urology
Congress Coverage
2024 ERS Congress 2024 EASD Congress 2024 ESC Congress 2024 EAN Congress 2024 ASCO Annual Meeting 2024 ACC Congress
Clinical Collections
Advances in Alzheimer’s

ERS Congress 2024

Keep up to date on the latest news from the annual European Respiratory Society conference.
ADVANCED SEARCH
Log in
Top
Cellular Oncology
Published in:

16-05-2024 | Research

Deciphering fatty acid biosynthesis-driven molecular subtypes in pancreatic ductal adenocarcinoma with prognostic insights

Authors: Junyi Xu, Mingzhu Liu, Jing Xue, Ping Lu

Published in: Cellular Oncology | Issue 4/2024

Login to get access

Abstract

Purpose

Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge due to its high heterogeneity and aggressiveness. Recognizing the urgency to delineate molecular subtypes, our study focused on the emerging field of lipid metabolism remodeling in PDAC, particularly exploring the prognostic potential and molecular classification associated with fatty acid biosynthesis.

Methods

Gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) were performed to evaluate the dysregulation of lipid metabolism in PDAC. Univariate cox analysis and the LASSO module were used to build a prognostic risk score signature. The distinction of gene expression in different risk groups was explored by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The biological function of Acyl-CoA Synthetase Long Chain Family Member 5 (ACSL5), a pivotal gene within 7-hub gene signature panel, was validated through in vitro assays.

Results

Our study identified a 7-hub gene signature associated with fatty acid biosynthesis-related genes (FRGs), providing a robust tool for prognosis prediction. The high-FRGs score group displayed a poorer prognosis, decreased immune cell infiltration, and a higher tumor mutation burden. Interestingly, this group exhibited enhanced responsiveness to various compounds according to the Genomics of Drug Sensitivity in Cancer (GDSC) database. Notably, ACSL5 was upregulated in PDAC and essential for tumor progression.

Conclusion

In conclusion, our research defined two novel fatty acid biosynthesis-based subtypes in PDAC, characterized by distinct transcriptional profiles. These subtypes not only served as prognostic indicator, but also offered valuable insights into their metastatic propensity and therapeutic potential.
Appendix
Available only for authorised users
Literature
1.
2.
3.
4.
J. Encarnacion-Rosado, A.C. Kimmelman, Harnessing metabolic dependencies in pancreatic cancers. Nat. Rev. Gastroenterol. Hepatol. 18(7), 482–492 (2021)PubMedPubMedCentralCrossRef
5.
6.
S. Barthel, et al., Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy. Nat. Cancer 4(4), 454–467 (2023)PubMedPubMedCentralCrossRef
7.
Y. Li, et al., Metabolic classification suggests the GLUT1/ALDOB/G6PD axis as a therapeutic target in chemotherapy-resistant pancreatic cancer. Cell Rep. Med. 4(9), 101162 (2023)PubMedPubMedCentralCrossRef
8.
Z. Jin, Y.D. Chai, S. Hu, Fatty acid metabolism and cancer. Adv. Exp. Med. Biol. 1280, 231–241 (2021)PubMedCrossRef
9.
S.M. Rossi, G. Konstantinidou, Targeting long chain acyl-CoA synthetases for cancer therapy. Int. J. Mol. Sci. 20(15), 3624 (2019)CrossRef
10.
M. Lopes-Marques, et al., Diversity and history of the long-chain acyl-CoA synthetase (Acsl) gene family in vertebrates. BMC Evol. Biol. 13, 271 (2013)PubMedPubMedCentralCrossRef
11.
S. Hanzelmann, R. Castelo, J. Guinney, GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 14, 7 (2013)CrossRef
12.
A. Subramanian, et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102(43), 15545–15550 (2005)PubMedPubMedCentralCrossRef
13.
14.
J. Gao, P.W. Kwan, D. Shi, Sparse kernel learning with LASSO and Bayesian inference algorithm. Neural Netw. 23(2), 257–264 (2010)PubMedCrossRef
15.
D.W. Huang, B.T. Sherman, R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4(1), 44–57 (2009)PubMedCrossRef
16.
P. Langfelder, S. Horvath, WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 9, 559 (2008)CrossRef
17.
R.A. Moffitt, et al., Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47(10), 1168–1178 (2015)
18.
19.
K. Yoshihara, et al., Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013)PubMedCrossRef
20.
B. Ru, et al., TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics 35(20), 4200–4202 (2019)PubMedCrossRef
21.
22.
D. Maeser, R.F. Gruener, R.S. Huang, oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 22(6), bbab260 (2021)PubMedPubMedCentralCrossRef
23.
Y. Shu, C.W. Chua, An organoid assay for long-term maintenance and propagation of mouse prostate luminal epithelial progenitors and cancer cells. Methods Mol. Biol. 1940, 231–254 (2019)PubMedCrossRef
24.
M.A. Fleming, P. Storz, Mimicking and manipulating pancreatic acinar-to-ductal metaplasia in 3-dimensional cell culture. J. Vis. Exp. (144) (2019)
25.
J.A. Menendez, R. Lupu, Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat. Rev. Cancer 7(10), 763–777 (2007)
26.
N. Zaidi, et al., Lipogenesis and lipolysis: the pathways exploited by the cancer cells to acquire fatty acids. Prog. Lipid Res. 52(4), 585–589 (2013)PubMedPubMedCentralCrossRef
27.
W.C. Huang, et al., A novel miR-365-3p/EHF/keratin 16 axis promotes oral squamous cell carcinoma metastasis, cancer stemness and drug resistance via enhancing beta5-integrin/c-met signaling pathway. J. Exp. Clin. Cancer Res. 38(1), 89 (2019)PubMedPubMedCentralCrossRef
28.
F. Matsuzawa, et al., Mesothelin blockage by Amatuximab suppresses cell invasiveness, enhances gemcitabine sensitivity and regulates cancer cell stemness in mesothelin-positive pancreatic cancer cells. BMC Cancer 21(1), 200 (2021)PubMedPubMedCentralCrossRef
29.
T. Arumugam, et al., S100P promotes pancreatic cancer growth, survival, and invasion. Clin. Cancer Res. 11(15), 5356–5364 (2005)PubMedCrossRef
30.
R. Fischer-Colbrie, A. Laslop, R. Kirchmair, Secretogranin II: molecular properties, regulation of biosynthesis and processing to the neuropeptide secretoneurin. Prog. Neurobiol. 46(1), 49–70 (1995)PubMedCrossRef
31.
T. Takeuchi, M. Hosaka, Sorting mechanism of peptide hormones and biogenesis mechanism of secretory granules by secretogranin III, a cholesterol-binding protein, in endocrine cells. Curr. Diabetes Rev. 4(1), 31–38 (2008)PubMedCrossRef
32.
I. Comerford, et al., A myriad of functions and complex regulation of the CCR7/CCL19/CCL21 chemokine axis in the adaptive immune system. Cytokine Growth Factor Rev. 24(3), 269–283 (2013)PubMedCrossRef
33.
J.D. Klement, et al., An osteopontin/CD44 immune checkpoint controls CD8+ T cell activation and tumor immune evasion. J. Clin. Invest. 128(12), 5549–5560 (2018)PubMedPubMedCentralCrossRef
34.
35.
36.
R. Wang, et al., Interferon gamma-induced interferon regulatory factor 1 activates transcription of HHLA2 and induces immune escape of hepatocellular carcinoma cells. Inflammation 45(1), 308–330 (2022)PubMedCrossRef
37.
38.
M.R. Stratton, Exploring the genomes of cancer cells: progress and promise. Science 331(6024), 1553–1558 (2011)PubMedCrossRef
39.
N.A. Rizvi, et al., Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348(6230), 124–128 (2015)PubMedPubMedCentralCrossRef
40.
41.
L. Liu, et al., Combination of TMB and CNA stratifies prognostic and predictive responses to immunotherapy across metastatic cancer. Clin. Cancer Res. 25(24), 7413–7423 (2019)PubMedCrossRef
42.
H.F. Hu, et al., Mutations in key driver genes of pancreatic cancer: molecularly targeted therapies and other clinical implications. Acta Pharmacol. Sin. 42(11), 1725–1741 (2021)PubMedPubMedCentralCrossRef
43.
X. Xu, et al., Metabolic reprogramming and epigenetic modifications in cancer: from the impacts and mechanisms to the treatment potential. Exp. Mol. Med. 55(7), 1357–1370 (2023)PubMedPubMedCentralCrossRef
44.
Z. Tang, et al., GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 45(W1), W98–W102 (2017)PubMedPubMedCentralCrossRef
45.
46.
Y. Chen, et al., The diverse pancreatic tumor cell-intrinsic response to IFNgamma is determined by epigenetic heterogeneity. Cancer Lett. 562, 216153 (2023)PubMedCrossRef
47.
P. Bailey, et al., Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531(7592), 47–52 (2016)PubMedCrossRef
48.
49.
50.
51.
J.M. Karasinska, et al., Altered gene expression along the glycolysis-cholesterol synthesis axis is associated with outcome in pancreatic cancer. Clin. Cancer Res. 26(1), 135–146 (2020)PubMedCrossRef
52.
J. Chen, Y. Wang, H. Jiang, Features of metabolism associated molecular patterns in pancreatic ductal adenocarcinoma. Cancer Gene Ther. 30(9), 1296–1307 (2023)PubMedCrossRef
53.
J. Swierczynski, A. Hebanowska, T. Sledzinski, Role of abnormal lipid metabolism in development, progression, diagnosis and therapy of pancreatic cancer. World J. Gastroenterol. 20(9), 2279–2303 (2014)PubMedPubMedCentralCrossRef
54.
X. Yang, et al., Progressive and prognostic performance of an extracellular matrix-receptor interaction signature in gastric cancer. Dis. Markers 2020, 8816070 (2020)PubMedPubMedCentralCrossRef
55.
G. Pascual, et al., Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature 541(7635), 41–45 (2017)PubMedCrossRef
56.
W.W. Feng, et al., CD36-mediated metabolic rewiring of breast cancer cells promotes resistance to HER2-targeted therapies. Cell Rep. 29(11), 3405–3420.e5 (2019)PubMedPubMedCentralCrossRef
57.
58.
G.M. Alicea, et al., Changes in aged fibroblast lipid metabolism induce age-dependent melanoma cell resistance to targeted therapy via the fatty acid transporter FATP2. Cancer Discov. 10(9), 1282–1295 (2020)PubMedPubMedCentralCrossRef
59.
N. Koundouros, G. Poulogiannis, Reprogramming of fatty acid metabolism in cancer. Br. J. Cancer 122(1), 4–22 (2020)PubMedCrossRef
60.
61.
D. Wang, et al., Cyclooxygenases and prostaglandins in tumor immunology and microenvironment of gastrointestinal cancer. Gastroenterology 161(6), 1813–1829 (2021)PubMedCrossRef
62.
C. Porta, et al., Tumor-derived prostaglandin E2 promotes p50 NF-kappaB-dependent differentiation of monocytic MDSCs. Cancer Res. 80(13), 2874–2888 (2020)PubMedCrossRef
63.
F.S. Basingab, M. Ahmadi, D.J. Morgan, IFNgamma-dependent interactions between ICAM-1 and LFA-1 counteract prostaglandin E2-mediated inhibition of antitumor CTL responses. Cancer Immunol. Res. 4(5), 400–411 (2016)PubMedCrossRef
64.
N. Caronni, et al., IL-1beta(+) macrophages fuel pathogenic inflammation in pancreatic cancer. Nature 623(7986), 415–422 (2023)PubMedCrossRef
65.
J.P. Bottcher, et al., NK cells stimulate recruitment of cDC1 into the tumor microenvironment promoting cancer immune control. Cell 172(5), 1022–1037.e14 (2018)PubMedPubMedCentralCrossRef
66.
T. Mashima, et al., Promotion of glioma cell survival by acyl-CoA synthetase 5 under extracellular acidosis conditions. Oncogene 28(1), 9–19 (2009)PubMedCrossRef
67.
C. Klaus, et al., Modulating effects of acyl-CoA synthetase 5-derived mitochondrial Wnt2B palmitoylation on intestinal Wnt activity. World J. Gastroenterol. 20(40), 14855–14864 (2014)PubMedPubMedCentralCrossRef
68.
Y. Lai, et al., Dietary elaidic acid boosts tumoral antigen presentation and cancer immunity via ACSL5. Cell Metab. 36(4), 822–838.e8 (2024)PubMedCrossRef
69.
E.H. Seo, et al., ONECUT2 upregulation is associated with CpG hypomethylation at promoter-proximal DNA in gastric cancer and triggers ACSL5. Int. J. Cancer 146(12), 3354–3368 (2020)PubMedPubMedCentralCrossRef
70.
Metadata
Title
Deciphering fatty acid biosynthesis-driven molecular subtypes in pancreatic ductal adenocarcinoma with prognostic insights
Authors
Junyi Xu
Mingzhu Liu
Jing Xue
Ping Lu
Publication date
16-05-2024
Publisher
Springer Netherlands
Published in
Cellular Oncology / Issue 4/2024
Print ISSN: 2211-3428
Electronic ISSN: 2211-3436
DOI
https://doi.org/10.1007/s13402-024-00953-7

Other articles of this Issue 4/2024

Cellular Oncology 4/2024 Go to the issue

Research

STK3 kinase activation inhibits tumor proliferation through FOXO1-TP53INP1/P21 pathway in esophageal squamous cell carcinoma

Research

The prognostic effect of tumor-associated macrophages in stage I-III colorectal cancer depends on T cell infiltration

Research

Repaglinide restrains HCC development and progression by targeting FOXO3/lumican/p53 axis

Research

Copy number signatures and CCNE1 amplification reveal the involvement of replication stress in high-grade endometrial tumors oncogenesis

Correspondence

The Ribonuclease ZC3H12A is required for self-inflicted DNA breaks after DNA damage in small cell lung cancer cells

Research

The predictive significance of chromobox family members in prostate cancer in humans