Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold
Abstract
:1. Introduction
2. Results
2.1. Performance Comparison with Other Feature Engineering Methods
2.2. The Identification of Dysregulated Pathways
2.3. The Identification of Prognostic Pathway Biomarkers
2.4. The Selection of Prognostic Pathway Biomarkers
3. Discussion
4. Materials and Methods
4.1. Data Collection
4.2. Space of Symmetric Positive Definite (SPD) Matrices
4.3. Riemannian Tangent Space
4.4. Pathway Dysregulation Scores
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Li, X.; Li, Y.; Shang, X.; Kong, H. A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus. Front. Microbiol. 2024, 15, 1345794. [Google Scholar] [CrossRef] [PubMed]
- Li, X.; Xiang, J.; Wu, F.X.; Li, M. A dual ranking algorithm based on the multiplex network for heterogeneous complex disease analysis. IEEE/ACM Trans. Comput. Biol. Bioinform. 2021, 19, 1993–2002. [Google Scholar] [CrossRef] [PubMed]
- Li, X.; Xiang, J.; Wang, J.; Li, J.; Wu, F.X.; Li, M. FUNMarker: Fusion network-based method to identify prognostic and heterogeneous breast cancer biomarkers. IEEE/ACM Trans. Comput. Biol. Bioinform. 2020, 18, 2483–2491. [Google Scholar] [CrossRef] [PubMed]
- Rapaport, F.; Khanin, R.; Liang, Y.; Pirun, M.; Krek, A.; Zumbo, P.; Mason, C.E.; Socci, N.D.; Betel, D. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 2013, 14, 1–13. [Google Scholar] [CrossRef] [PubMed]
- Soneson, C.; Delorenzi, M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinform. 2013, 14, 91. [Google Scholar] [CrossRef] [PubMed]
- Goel, G.; Conway, K.L.; Jaeger, M.; Netea, M.G.; Xavier, R.J. Multivariate inference of pathway activity in host immunity and response to therapeutics. Nucleic Acids Res. 2014, 42, 10288–10306. [Google Scholar] [CrossRef] [PubMed]
- Uhlen, M.; Zhang, C.; Lee, S.; Sjöstedt, E.; Fagerberg, L.; Bidkhori, G.; Benfeitas, R.; Arif, M.; Liu, Z.; Edfors, F.; et al. A pathology atlas of the human cancer transcriptome. Science 2017, 357, eaan2507. [Google Scholar] [CrossRef] [PubMed]
- Symmans, W.F.; Liu, J.; Knowles, D.M.; Inghirami, G. Breast cancer heterogeneity: Evaluation of clonality in primary and metastatic lesions. Hum. Pathol. 1995, 26, 210–216. [Google Scholar] [CrossRef] [PubMed]
- Ein-Dor, L.; Kela, I.; Getz, G.; Givol, D.; Domany, E. Outcome signature genes in breast cancer: Is there a unique set? Bioinformatics 2005, 21, 171–178. [Google Scholar] [CrossRef]
- Lee, E.; Chuang, H.Y.; Kim, J.W.; Ideker, T.; Lee, D. Inferring pathway activity toward precise disease classification. PLoS Comput. Biol. 2008, 4, e1000217. [Google Scholar] [CrossRef]
- Li, X.; Li, M.; Zheng, R.; Chen, X.; Xiang, J.; Wu, F.X.; Wang, J. Evaluation of pathway activation for a single sample toward inflammatory bowel disease classification. Front. Genet. 2020, 10, 1401. [Google Scholar] [CrossRef]
- Lim, S.; Lee, S.; Jung, I.; Rhee, S.; Kim, S. Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data. Brief. Bioinform. 2020, 21, 36–46. [Google Scholar] [CrossRef]
- Drier, Y.; Sheffer, M.; Domany, E. Pathway-based personalized analysis of cancer. Proc. Natl. Acad. Sci. USA 2013, 110, 6388–6393. [Google Scholar] [CrossRef]
- Mao, W.; Zaslavsky, E.; Hartmann, B.M.; Sealfon, S.C.; Chikina, M. Pathway-level information extractor (PLIER) for gene expression data. Nat. Methods 2019, 16, 607–610. [Google Scholar] [CrossRef]
- Li, F.; Wu, T.; Xu, Y.; Dong, Q.; Xiao, J.; Xu, Y.; Li, Q.; Zhang, C.; Gao, J.; Liu, L.; et al. A comprehensive overview of oncogenic pathways in human cancer. Brief. Bioinform. 2020, 21, 957–969. [Google Scholar] [CrossRef]
- Li, X.; Li, M.; Xiang, J.; Zhao, Z.; Shang, X. SEPA: Signaling entropy-based algorithm to evaluate personalized pathway activation for survival analysis on pan-cancer data. Bioinformatics 2022, 38, 2536–2543. [Google Scholar] [CrossRef]
- Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef]
- Romero, P.; Wagg, J.; Green, M.L.; Kaiser, D.; Krummenacker, M.; Karp, P.D. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2005, 6, R2. [Google Scholar] [CrossRef]
- Pico, A.R.; Kelder, T.; Van Iersel, M.P.; Hanspers, K.; Conklin, B.R.; Evelo, C. WikiPathways: Pathway editing for the people. PLoS Biol. 2008, 6, e184. [Google Scholar] [CrossRef]
- Schaefer, C.F.; Anthony, K.; Krupa, S.; Buchoff, J.; Day, M.; Hannay, T.; Buetow, K.H. PID: The pathway interaction database. Nucleic Acids Res. 2009, 37, D674–D679. [Google Scholar] [CrossRef]
- Jassal, B.; Matthews, L.; Viteri, G.; Gong, C.; Lorente, P.; Fabregat, A.; Sidiropoulos, K.; Cook, J.; Gillespie, M.; Haw, R.; et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020, 48, D498–D503. [Google Scholar] [CrossRef]
- Huang, E.; Ishida, S.; Pittman, J.; Dressman, H.; Bild, A.; Kloos, M.; D’Amico, M.; Pestell, R.G.; West, M.; Nevins, J.R. Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat. Genet. 2003, 34, 226–230. [Google Scholar] [CrossRef]
- Bild, A.H.; Yao, G.; Chang, J.T.; Wang, Q.; Potti, A.; Chasse, D.; Joshi, M.B.; Harpole, D.; Lancaster, J.M.; Berchuck, A.; et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006, 439, 353–357. [Google Scholar] [CrossRef]
- Young, M.R.; Craft, D.L. Pathway-informed classification system (PICS) for cancer analysis using gene expression data. Cancer Inform. 2016, 15, CIN-S40088. [Google Scholar] [CrossRef]
- Han, L.; Maciejewski, M.; Brockel, C.; Gordon, W.; Snapper, S.B.; Korzenik, J.R.; Afzelius, L.; Altman, R.B. A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease. Bioinformatics 2018, 34, 985–993. [Google Scholar] [CrossRef]
- Hänzelmann, S.; Castelo, R.; Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 2013, 14, 7. [Google Scholar] [CrossRef]
- Tomfohr, J.; Lu, J.; Kepler, T.B. Pathway level analysis of gene expression using singular value decomposition. BMC Bioinform. 2005, 6, 225. [Google Scholar] [CrossRef]
- Barbie, D.A.; Tamayo, P.; Boehm, J.S.; Kim, S.Y.; Moody, S.E.; Dunn, I.F.; Schinzel, A.C.; Sandy, P.; Meylan, E.; Scholl, C.; et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 2009, 462, 108–112. [Google Scholar] [CrossRef]
- Vitali, F.; Li, Q.; Schissler, A.G.; Berghout, J.; Kenost, C.; Lussier, Y.A. Developing a ‘personalome’ for precision medicine: Emerging methods that compute interpretable effect sizes from single-subject transcriptomes. Brief. Bioinform. 2019, 20, 789–805. [Google Scholar] [CrossRef]
- Liberzon, A.; Birger, C.; Thorvaldsdóttir, H.; Ghandi, M.; Mesirov, J.P.; Tamayo, P. The molecular signatures database hallmark gene set collection. Cell Syst. 2015, 1, 417–425. [Google Scholar] [CrossRef]
- Su, K.; Yu, Q.; Shen, R.; Sun, S.Y.; Moreno, C.S.; Li, X.; Qin, Z.S. Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis. Cell Rep. Methods 2021, 1, 100050. [Google Scholar] [CrossRef] [PubMed]
- Takebe, N.; Warren, R.Q.; Ivy, S.P. Breast cancer growth and metastasis: Interplay between cancer stem cells, embryonic signaling pathways and epithelial-to-mesenchymal transition. Breast Cancer Res. 2011, 13, 211. [Google Scholar] [CrossRef]
- Schmid, S.; Bieber, M.; Zhang, F.; Zhang, M.; He, B.; Jablons, D.; Teng, N.N. Wnt and hedgehog gene pathway expression in serous ovarian cancer. Int. J. Gynecol. Cancer 2011, 21, 975. [Google Scholar] [CrossRef]
- Dong, H.; Claffey, K.P.; Brocke, S.; Epstein, P.M. Inhibition of breast cancer cell migration by activation of cAMP signaling. Breast Cancer Res. Treat. 2015, 152, 17–28. [Google Scholar] [CrossRef]
- Tang, X.; Zhang, Q.; Shi, S.; Yen, Y.; Li, X.; Zhang, Y.; Zhou, K.; Le, A.D. Bisphosphonates suppress insulin-like growth factor 1-induced angiogenesis via the HIF-1α/VEGF signaling pathways in human breast cancer cells. Int. J. Cancer 2010, 126, 90–103. [Google Scholar] [CrossRef]
- Spangle, J.M.; Dreijerink, K.M.; Groner, A.C.; Cheng, H.; Ohlson, C.E.; Reyes, J.; Lin, C.Y.; Bradner, J.; Zhao, J.J.; Roberts, T.M.; et al. PI3K/AKT signaling regulates H3K4 methylation in breast cancer. Cell Rep. 2016, 15, 2692–2704. [Google Scholar] [CrossRef]
- Madsen, R.R.; Erickson, E.C.; Rueda, O.M.; Robin, X.; Caldas, C.; Toker, A.; Semple, R.K.; Vanhaesebroeck, B. Positive correlation between transcriptomic stemness and PI3K/AKT/mTOR signaling scores in breast cancer, and a counterintuitive relationship with PIK3CA genotype. PLoS Genet. 2021, 17, e1009876. [Google Scholar] [CrossRef]
- Zhu, K.; Wu, Y.; He, P.; Fan, Y.; Zhong, X.; Zheng, H.; Luo, T. PI3K/AKT/mTOR-targeted therapy for breast cancer. Cells 2022, 11, 2508. [Google Scholar] [CrossRef] [PubMed]
- Starzec, A.B.; Spanakis, E.; Nehme, A.; Salle, V.; Veber, N.; Mainguene, C.; Planchon, P.; Valette, A.; Prevost, G.; Israel, L. Proliferative responses of epithelial cells to 8-bromo-cyclic AMP and to a phorbol ester change during breast Pathogenesis. J. Cell. Physiol. 1994, 161, 31–38. [Google Scholar] [CrossRef]
- Cho-Chung, Y.S. Suppression of malignancy targeting cyclic AMP signal transducing proteins. Biochem. Soc. Trans. 1992, 20, 425–430. [Google Scholar] [CrossRef]
- Kim, S.N.; Ahn, Y.H.; Kim, S.G.; Park, S.D.; Cho-Chung, Y.S.; Hong, S.H. 8-Cl-cAMP induces cell cycle-specific apoptosis in human cancer cells. Int. J. Cancer 2001, 93, 33–41. [Google Scholar] [CrossRef] [PubMed]
- Loi, S.; Haibe-Kains, B.; Majjaj, S.; Lallemand, F.; Durbecq, V.; Larsimont, D.; Gonzalez-Angulo, A.M.; Pusztai, L.; Symmans, W.F.; Bardelli, A.; et al. PIK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor–positive breast cancer. Proc. Natl. Acad. Sci. USA 2010, 107, 10208–10213. [Google Scholar] [CrossRef] [PubMed]
- Teschendorff, A.E.; Enver, T. Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nat. Commun. 2017, 8, 15599. [Google Scholar] [CrossRef] [PubMed]
- Hu, J.; Chen, M.; Zhou, X. Effective and scalable single-cell data alignment with non-linear canonical correlation analysis. Nucleic Acids Res. 2022, 50, e21. [Google Scholar] [CrossRef] [PubMed]
- Zhang, J.D.; Wiemann, S. KEGGgraph: A graph approach to KEGG PATHWAY in R and bioconductor. Bioinformatics 2009, 25, 1470–1471. [Google Scholar] [CrossRef] [PubMed]
- Förstner, W.; Moonen, B. A metric for covariance matrices. In Geodesy—The Challenge of the 3rd Millennium; Springer: Berlin/Heidelberg, Germany, 2003; pp. 299–309. [Google Scholar]
- Congedo, M.; Barachant, A.; Bhatia, R. Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Comput. Interfaces 2017, 4, 155–174. [Google Scholar] [CrossRef]
- Nguyen, C.H.; Artemiadis, P. EEG feature descriptors and discriminant analysis under Riemannian Manifold perspective. Neurocomputing 2018, 275, 1871–1883. [Google Scholar] [CrossRef]
- Barachant, A.; Bonnet, S.; Congedo, M.; Jutten, C. Multiclass brain–computer interface classification by Riemannian geometry. IEEE Trans. Biomed. Eng. 2011, 59, 920–928. [Google Scholar] [CrossRef]
- Tuzel, O.; Porikli, F.; Meer, P. Pedestrian detection via classification on riemannian manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30, 1713–1727. [Google Scholar] [CrossRef]
Dataset | RiePath | CORGs | GSVA | PLAGE | ssGSEA |
---|---|---|---|---|---|
BLCA | 198.78 | 0.98 | 1.04 | 0.98 | 196.87 |
BRCA | 352.89 | 0.97 | 1.05 | 0.95 | 252.27 |
COAD | 286.14 | 0.93 | 1.01 | 0.94 | 281.74 |
HNSC | 263.76 | 0.95 | 1.05 | 0.97 | 254.19 |
KICH | 389.52 | 0.83 | 1.02 | 0.77 | 293.59 |
KIRC | 309.68 | 0.94 | 1.00 | 0.96 | 297.61 |
KIRP | 387.01 | 0.91 | 1.01 | 0.94 | 316.69 |
LGG | 688.92 | 0.94 | 1.00 | 0.91 | 635.00 |
LIHC | 227.58 | 0.86 | 1.02 | 0.89 | 212.01 |
LUAD | 271.53 | 0.97 | 1.03 | 0.95 | 242.96 |
LUSC | 308.13 | 0.97 | 1.02 | 0.92 | 262.08 |
OV | 408.63 | 0.92 | 1.02 | 0.94 | 358.85 |
PRAD | 771.00 | 0.87 | 0.97 | 0.87 | 620.76 |
STAD | 235.47 | 0.97 | 1.03 | 0.97 | 231.12 |
THCA | 683.27 | 0.91 | 0.96 | 0.88 | 621.84 |
UCEC | 220.73 | 0.98 | 1.07 | 0.97 | 217.65 |
Code | Source | Number of Tumor Samples | Number of Normal Samples | Number of Total Samples |
---|---|---|---|---|
BLCA | Bladder carcinoma | 400 | 23 | 423 |
BRCA | Breast invasive carcinoma | 1057 | 136 | 1193 |
COAD | Colorectal adenocarcinoma | 432 | 54 | 486 |
LGG | Low-grade gliomas | 509 | 14 | 523 |
HNSC | Head and neck squamous cell carcinoma | 496 | 48 | 544 |
PRAD | Prostate adenocarcinoma | 428 | 68 | 496 |
THCA | Thyroid carcinoma | 504 | 62 | 566 |
KIRC | Kidney renal clear cell carcinoma | 523 | 78 | 601 |
KIRP | Kidney renal papillary cell carcinoma | 285 | 32 | 317 |
KICH | Kidney chromophobe | 64 | 23 | 87 |
LIHC | Liver infiltrate hepatocellular carcinoma | 36 | 5 | 417 |
LUAD | Lung adenocarcinoma | 499 | 72 | 571 |
LUSC | Lung squamous cell carcinoma | 489 | 52 | 541 |
OV | Ovarian carcinoma | 358 | 19 | 377 |
STAD | Stomach adenocarcinoma | 348 | 32 | 380 |
UCEC | Endometrioid carcinoma | 534 | 32 | 566 |
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Li, X.; Hao, J.; Li, J.; Zhao, Z.; Shang, X.; Li, M. Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold. Int. J. Mol. Sci. 2024, 25, 4411. https://doi.org/10.3390/ijms25084411
Li X, Hao J, Li J, Zhao Z, Shang X, Li M. Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold. International Journal of Molecular Sciences. 2024; 25(8):4411. https://doi.org/10.3390/ijms25084411
Chicago/Turabian StyleLi, Xingyi, Jun Hao, Junming Li, Zhelin Zhao, Xuequn Shang, and Min Li. 2024. "Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold" International Journal of Molecular Sciences 25, no. 8: 4411. https://doi.org/10.3390/ijms25084411