Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors—A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Acquisition
2.2. Machine Learning Model
2.3. Bioinformatical Analyses
2.4. Literature Search Regarding MicroRNA Functions
3. Results
3.1. Signaling Pathways and Drug-Specific Signatures Related to CXCR4 Overexpression
3.2. CXCR4-Associated Tumor Microenvironment from a Pan-Cancer Perspective
3.3. FAP-Associated Signaling and Tumor Microenvironment
3.4. MicroRNAs Characterizing CXCR4 and FAP Overexpression
3.5. Transferability of Transcriptomic Results to Protein Expression and Theranostics
4. Discussion
4.1. CXCR4 as Immune-Related Biomarker in Solid Tumors
4.2. FAP as Potential Biomarker for Anti-Angiogenic Therapy Stratification
4.3. Limitations and Future Directions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Rank | miR Candidate | Mean High (+/−Std High) | Mean Low (+/−Std Low) | p Value | Immune-Related Target Genes/In Vitro (Selection) | Covered in Review Articles |
---|---|---|---|---|---|---|
1 | miR-150 | 2204.46 (+/−3768.76) | 734.17 (+/−1402.81) | 1.66 × 10−24 | c-Myb [40], ARBB2 [41] | [35,36] |
2 | miR-4491 | 1.24 (+/−2.97) | 0.50 (+/−3.06) | 1.87 × 10−15 | TRIM7 [42] | - |
3 | miR-155 | 992.76 (+/−1386.74) | 515.66 (+/−1180.80) | 2.17 × 10−25 | SOCS1 [43,44] | [35,36] |
4 | miR-5586 | 5.08 (+/−4.77) | 3.16 (+/−3.74) | 3.29 × 10−17 | - | - |
5 | miR-142 | 7184.13 (+/−18,169.54) | 4084.70 (+/−14,505.49) | 1.56 × 10−19 | PD-L1 [45] | [35,36] |
6 | miR-210 | 994.59 (+/−1418.87) | 1051.44 (+/−1527.88) | 0.166973729 | PTPN, HOXA1, TP53I11 [46] | - |
7 | miR-29c | 2861.48 (+/−2863.31) | 2195.79 (+/−2074.10) | 0.000214287 | B7-H3 [47] | [36] |
8 | miR-195 | 51.30 (+/−41.85) | 40.86 (+/−36.79) | 4.56 × 10−7 | PD-L1 [48,49,50] | - |
9 | miR-146a | 544.21 (+/−2503.58) | 354.89 (+/−1622.84) | 3.78 × 10−13 | IRAK1, TRAF6 [51] | [35,36] |
10 | miR-1307 | 1652.20 (+/−1937.24) | 1745.57 (+/−1999.00) | 0.016754954 | TRAF3 [52] | - |
Rank | miR Candidate | Mean High (+/−Std High) | Mean Low (+/−Std Low) | p Value | Angiogenesis-Related Target Genes/In Vitro (Selection) | Covered in Review Articles |
---|---|---|---|---|---|---|
1 | miR-21 | 305,318.37 (+/−139,932.32) | 226,877.88 (+/−143,668.68) | 1.81 × 10−27 | FASLG [53], KRIT1 [54] | [37,38] |
2 | miR-1245a | 3.78 (+/−4.81) | 1.68 (+/−3.34) | 2.39 × 10−44 | - | - |
3 | miR-214 | 48.98 (+/−49.57) | 28.67 (+/−63.28) | 1.89 × 10−48 | QKI [55], VEGFA [56] | - |
4 | miR-493 | 41.20 (+/−119.60) | 28.61 (+/−110.12) | 1.87 × 10−42 | MIF [57,58], ZEB2 [59], DKK2 [60] | - |
5 | miR-128-2 | 73.34 (+/−93.89) | 134.96 (+/−364.17) | 5.27 × 10−11 | VEGFC [61], RPS6KB1 [62] | [38] |
6 | miR-199a-1 | 1910.74 (+/−1744.58) | 1190.78 (+/−2028.65) | 1.26 × 10−36 | VEGFA, VEGFR1, VEGFR2, HGF, MMP2 [63], APOE [64] | [38] |
7 | miR-199a-2 | 3107.70 (+/−2798.14) | 1940.26 (+/−3134.63) | 2.47 × 10−35 | VEGFA [65], APOE [64] | [38] |
8 | miR-652 | 30.18 (+/−36.47) | 36.92 (+/−41.11) | 7.14 × 10−10 | VEGFA [66], PRRX1 [67] | - |
9 | miR-337 | 61.72 (+/−130.58) | 51.09 (+/−152.74) | 1.69 × 10−37 | - | - |
10 | miR-7-1 | 25.56 (+/−31.47) | 40.47 (+/−65.27) | 6.98 × 10−10 | KLF4 [68], RAF1 [69] | - |
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Marquardt, A.; Hartrampf, P.; Kollmannsberger, P.; Solimando, A.G.; Meierjohann, S.; Kübler, H.; Bargou, R.; Schilling, B.; Serfling, S.E.; Buck, A.; et al. Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors—A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures. Cancers 2023, 15, 392. https://doi.org/10.3390/cancers15020392
Marquardt A, Hartrampf P, Kollmannsberger P, Solimando AG, Meierjohann S, Kübler H, Bargou R, Schilling B, Serfling SE, Buck A, et al. Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors—A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures. Cancers. 2023; 15(2):392. https://doi.org/10.3390/cancers15020392
Chicago/Turabian StyleMarquardt, André, Philipp Hartrampf, Philip Kollmannsberger, Antonio G. Solimando, Svenja Meierjohann, Hubert Kübler, Ralf Bargou, Bastian Schilling, Sebastian E. Serfling, Andreas Buck, and et al. 2023. "Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors—A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures" Cancers 15, no. 2: 392. https://doi.org/10.3390/cancers15020392