Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

General Research Article

Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma

Author(s): Junzhi Liu, Huimin Li, Nannan Zhang, Qiuping Dong and Zheng Liang*

Volume 31, Issue 25, 2024

Published on: 29 April, 2024

Page: [4034 - 4055] Pages: 22

DOI: 10.2174/0109298673313281240425050032

conference banner
Abstract

Background: Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for exploring tumor cell dynamics. We aimed to explore the impact of disulfide cell death on the tumor immune microenvironment and immunotherapy in lung adenocarcinoma (LUAD).

Methods: We initially utilized pan-cancer transcriptomics to explore the expression, prognosis, and mutation status of genes related to disulfidptosis. Using the LUAD multi- -omics cohorts in the TCGA database, we explore the molecular characteristics of subtypes related to disulfidptosis. Employing various machine learning algorithms, we construct a robust prognostic model to predict immune therapy responses and explore the model's impact on the tumor microenvironment through single-cell transcriptome data. Finally, the biological functions of genes related to the prognostic model are verified through laboratory experiments.

Results: Genes related to disulfidptosis exhibit high expression and significant prognostic value in various cancers, including LUAD. Two disulfidptosis subtypes with distinct prognoses and molecular characteristics have been identified, leading to the development of a robust DSRS prognostic model, where a lower risk score correlates with a higher response rate to immunotherapy and a better patient prognosis. NAPSA, a critical gene in the risk model, was found to inhibit the proliferation and migration of LUAD cells.

Conclusion: Our research introduces an innovative prognostic risk model predicated upon disulfidptosis genes for patients afflicted with Lung Adenocarcinoma (LUAD). This model proficiently forecasts the survival rates and therapeutic outcomes for LUAD patients, thereby delineating the high-risk population with distinctive immune cell infiltration and a state of immunosuppression. Furthermore, NAPSA can inhibit the proliferation and invasion capabilities of LUAD cells, thereby identifying new molecules for clinical targeted therapy.

Keywords: Disulfidptosis, immune microenvironment, single cell, drug response, TCGA database, lung adenocarcinoma.

« Previous
[1]
Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer statistics, 2021. CA Cancer J. Clin., 2021, 71(1), 7-33.
[http://dx.doi.org/10.3322/caac.21654] [PMID: 33433946]
[2]
Topalian, S.L.; Hodi, F.S.; Brahmer, J.R.; Gettinger, S.N.; Smith, D.C.; McDermott, D.F.; Powderly, J.D.; Carvajal, R.D.; Sosman, J.A.; Atkins, M.B.; Leming, P.D.; Spigel, D.R.; Antonia, S.J.; Horn, L.; Drake, C.G.; Pardoll, D.M.; Chen, L.; Sharfman, W.H.; Anders, R.A.; Taube, J.M.; McMiller, T.L.; Xu, H.; Korman, A.J.; Jure-Kunkel, M.; Agrawal, S.; McDonald, D.; Kollia, G.D.; Gupta, A.; Wigginton, J.M.; Sznol, M. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med., 2012, 366(26), 2443-2454.
[http://dx.doi.org/10.1056/NEJMoa1200690] [PMID: 22658127]
[3]
Sharma, P.; Allison, J.P. The future of immune checkpoint therapy. Science, 2015, 348(6230), 56-61.
[http://dx.doi.org/10.1126/science.aaa8172] [PMID: 25838373]
[4]
Gibney, G.T.; Weiner, L.M.; Atkins, M.B. Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol., 2016, 17(12), e542-e551.
[http://dx.doi.org/10.1016/S1470-2045(16)30406-5] [PMID: 27924752]
[5]
Rizzo, A.; Cusmai, A.; Giovannelli, F.; Acquafredda, S.; Rinaldi, L.; Misino, A.; Montagna, E.S.; Ungaro, V.; Lorusso, M.; Palmiotti, G. Impact of proton pump inhibitors and histamine-2-receptor antagonists on non-small cell lung cancer immunotherapy: A systematic review and meta-analysis. Cancers, 2022, 14(6), 1404.
[http://dx.doi.org/10.3390/cancers14061404] [PMID: 35326555]
[6]
Mollica, V.; Rizzo, A.; Marchetti, A.; Tateo, V.; Tassinari, E.; Rosellini, M.; Massafra, R.; Santoni, M.; Massari, F. The impact of ECOG performance status on efficacy of immunotherapy and immune-based combinations in cancer patients: The MOUSEION-06 study. Clin. Exp. Med., 2023, 23(8), 5039-5049.
[http://dx.doi.org/10.1007/s10238-023-01159-1] [PMID: 37535194]
[7]
Zheng, H.; Wang, M.; Zhang, S.; Hu, D.; Yang, Q.; Chen, M.; Zhang, X.; Zhang, Y.; Dai, J.; Liou, Y.C. Comprehensive pan-cancer analysis reveals NUSAP1 is a novel predictive biomarker for prognosis and immunotherapy response. Int. J. Biol. Sci., 2023, 19(14), 4689-4708.
[http://dx.doi.org/10.7150/ijbs.80017] [PMID: 37781040]
[8]
Sharma, P.; Hu-Lieskovan, S.; Wargo, J.A.; Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell, 2017, 168(4), 707-723.
[http://dx.doi.org/10.1016/j.cell.2017.01.017] [PMID: 28187290]
[9]
Dall’Olio, F.G.; Rizzo, A.; Mollica, V.; Massucci, M.; Maggio, I.; Massari, F. Immortal time bias in the association between toxicity and response for immune checkpoint inhibitors: A meta-analysis. Immunotherapy, 2021, 13(3), 257-270.
[http://dx.doi.org/10.2217/imt-2020-0179] [PMID: 33225800]
[10]
Rizzo, A. Identifying optimal first-line treatment for advanced non-small cell lung carcinoma with high PD-L1 expression: A matter of debate. Br. J. Cancer, 2022, 127(8), 1381-1382.
[http://dx.doi.org/10.1038/s41416-022-01929-w] [PMID: 36064585]
[11]
Liu, X.; Nie, L.; Zhang, Y.; Yan, Y.; Wang, C.; Colic, M.; Olszewski, K.; Horbath, A.; Chen, X.; Lei, G.; Mao, C.; Wu, S.; Zhuang, L.; Poyurovsky, M.V.; James You, M.; Hart, T.; Billadeau, D.D.; Chen, J.; Gan, B. Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis. Nat. Cell Biol., 2023, 25(3), 404-414.
[http://dx.doi.org/10.1038/s41556-023-01091-2] [PMID: 36747082]
[12]
Zeng, D.; Ye, Z.; Shen, R.; Yu, G.; Wu, J.; Xiong, Y.; Zhou, R.; Qiu, W.; Huang, N.; Sun, L.; Li, X.; Bin, J.; Liao, Y.; Shi, M.; Liao, W. IOBR: Multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Front. Immunol., 2021, 12, 687975.
[http://dx.doi.org/10.3389/fimmu.2021.687975] [PMID: 34276676]
[13]
Jiang, A.; Meng, J.; Bao, Y.; Wang, A.; Gong, W.; Gan, X.; Wang, J.; Bao, Y.; Wu, Z.; Lu, J.; Liu, B.; Wang, L. Establishment of a prognosis prediction model based on pyroptosis-related signatures associated with the immune microenvironment and molecular heterogeneity in clear cell renal cell carcinoma. Front. Oncol., 2021, 11, 755212.
[http://dx.doi.org/10.3389/fonc.2021.755212] [PMID: 34804944]
[14]
Maeser, D.; Gruener, R.F.; Huang, R.S. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief. Bioinform., 2021, 22(6), bbab260.
[http://dx.doi.org/10.1093/bib/bbab260] [PMID: 34260682]
[15]
Yang, C.; Huang, X.; Li, Y.; Chen, J.; Lv, Y.; Dai, S. Prognosis and personalized treatment prediction in TP53 -mutant hepatocellular carcinoma: An in silico strategy towards precision oncology. Brief. Bioinform., 2021, 22(3), bbaa164.
[http://dx.doi.org/10.1093/bib/bbaa164] [PMID: 32789496]
[16]
Aran, D.; Looney, A.P.; Liu, L.; Wu, E.; Fong, V.; Hsu, A.; Chak, S.; Naikawadi, R.P.; Wolters, P.J.; Abate, A.R.; Butte, A.J.; Bhattacharya, M. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol., 2019, 20(2), 163-172.
[http://dx.doi.org/10.1038/s41590-018-0276-y] [PMID: 30643263]
[17]
Efremova, M.; Vento-Tormo, M.; Teichmann, S.A.; Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc., 2020, 15(4), 1484-1506.
[http://dx.doi.org/10.1038/s41596-020-0292-x] [PMID: 32103204]
[18]
Fu, J.; Li, K.; Zhang, W.; Wan, C.; Zhang, J.; Jiang, P.; Liu, X.S. Large-scale public data reuse to model immunotherapy response and resistance. Genome Med., 2020, 12(1), 21.
[http://dx.doi.org/10.1186/s13073-020-0721-z] [PMID: 32102694]
[19]
Jiang, P.; Gu, S.; Pan, D.; Fu, J.; Sahu, A.; Hu, X.; Li, Z.; Traugh, N.; Bu, X.; Li, B.; Liu, J.; Freeman, G.J.; Brown, M.A.; Wucherpfennig, K.W.; Liu, X.S. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med., 2018, 24(10), 1550-1558.
[http://dx.doi.org/10.1038/s41591-018-0136-1] [PMID: 30127393]
[20]
Chen, Z.; Luo, Z.; Zhang, D.; Li, H.; Liu, X.; Zhu, K.; Zhang, H.; Wang, Z.; Zhou, P.; Ren, J.; Zhao, A.; Zuo, Z. TIGER: A web portal of tumor immunotherapy gene expression resource. Genomics Proteomics Bioinformatics, 2022, 21(2), 337-348.
[PMID: 36049666]
[21]
Fu, Z.; Mowday, A.M.; Smaill, J.B.; Hermans, I.F.; Patterson, A.V. Tumour hypoxia-mediated immunosuppression: Mechanisms and therapeutic approaches to improve cancer immunotherapy. Cells, 2021, 10(5), 1006.
[http://dx.doi.org/10.3390/cells10051006] [PMID: 33923305]
[22]
Jayaprakash, P.; Vignali, P.D.A.; Delgoffe, G.M.; Curran, M.A. Hypoxia reduction sensitizes refractory cancers to immunotherapy. Annu. Rev. Med., 2022, 73(1), 251-265.
[http://dx.doi.org/10.1146/annurev-med-060619-022830] [PMID: 34699264]
[23]
Kopecka, J.; Salaroglio, I.C.; Perez-Ruiz, E.; Sarmento-Ribeiro, A.B.; Saponara, S.; De Las Rivas, J.; Riganti, C. Hypoxia as a driver of resistance to immunotherapy. Drug Resist. Updat., 2021, 59, 100787.
[http://dx.doi.org/10.1016/j.drup.2021.100787] [PMID: 34840068]
[24]
Ma, S.; Zhao, Y.; Lee, W.C.; Ong, L.T.; Lee, P.L.; Jiang, Z.; Oguz, G.; Niu, Z.; Liu, M.; Goh, J.Y.; Wang, W.; Bustos, M.A.; Ehmsen, S.; Ramasamy, A.; Hoon, D.S.B.; Ditzel, H.J.; Tan, E.Y.; Chen, Q.; Yu, Q. Hypoxia induces HIF1α-dependent epigenetic vulnerability in triple negative breast cancer to confer immune effector dysfunction and resistance to anti-PD-1 immunotherapy. Nat. Commun., 2022, 13(1), 4118.
[http://dx.doi.org/10.1038/s41467-022-31764-9] [PMID: 35840558]
[25]
Vignali, P.D.A.; DePeaux, K.; Watson, M.J.; Ye, C.; Ford, B.R.; Lontos, K.; McGaa, N.K.; Scharping, N.E.; Menk, A.V.; Robson, S.C.; Poholek, A.C.; Rivadeneira, D.B.; Delgoffe, G.M. Hypoxia drives CD39-dependent suppressor function in exhausted T cells to limit antitumor immunity. Nat. Immunol., 2023, 24(2), 267-279.
[http://dx.doi.org/10.1038/s41590-022-01379-9] [PMID: 36543958]
[26]
Zhang, H.; Cao, K.; Xiang, J.; Zhang, M.; Zhu, M.; Xi, Q. Hypoxia induces immunosuppression, metastasis and drug resistance in pancreatic cancers. Cancer Lett., 2023, 571, 216345.
[http://dx.doi.org/10.1016/j.canlet.2023.216345] [PMID: 37558084]
[27]
Chagas, V.S.; Groeneveld, C.S.; Oliveira, K.G.; Trefflich, S.; de Almeida, R.C.; Ponder, B.A.J.; Meyer, K.B.; Jones, S.J.M.; Robertson, A.G.; Castro, M.A.A. RTNduals : An R/Bioconductor package for analysis of co-regulation and inference of dual regulons. Bioinformatics, 2019, 35(24), 5357-5358.
[http://dx.doi.org/10.1093/bioinformatics/btz534] [PMID: 31250887]
[28]
D’Arcy, M.S. Cell death: A review of the major forms of apoptosis, necrosis and autophagy. Cell Biol. Int., 2019, 43(6), 582-592.
[http://dx.doi.org/10.1002/cbin.11137] [PMID: 30958602]
[29]
Li, X.; He, S.; Ma, B. Autophagy and autophagy-related proteins in cancer. Mol. Cancer, 2020, 19(1), 12.
[http://dx.doi.org/10.1186/s12943-020-1138-4] [PMID: 31969156]
[30]
Gong, Y.; Fan, Z.; Luo, G.; Yang, C.; Huang, Q.; Fan, K.; Cheng, H.; Jin, K.; Ni, Q.; Yu, X.; Liu, C. The role of necroptosis in cancer biology and therapy. Mol. Cancer, 2019, 18(1), 1-17.
[http://dx.doi.org/10.1186/s12943-019-1029-8] [PMID: 31122251]
[31]
Yu, P.; Zhang, X.; Liu, N.; Tang, L.; Peng, C.; Chen, X. Pyroptosis: Mechanisms and diseases. Signal Transduct. Target. Ther., 2021, 6(1), 128.
[http://dx.doi.org/10.1038/s41392-021-00507-5] [PMID: 33776057]
[32]
Jiang, X.; Stockwell, B.R.; Conrad, M. Ferroptosis: Mechanisms, biology and role in disease. Nat. Rev. Mol. Cell Biol., 2021, 22(4), 266-282.
[http://dx.doi.org/10.1038/s41580-020-00324-8] [PMID: 33495651]
[33]
Xie, J.; Yang, Y.; Gao, Y.; He, J. Cuproptosis: Mechanisms and links with cancers. Mol. Cancer, 2023, 22(1), 46.
[http://dx.doi.org/10.1186/s12943-023-01732-y] [PMID: 36882769]
[34]
Xia, L.; Oyang, L.; Lin, J.; Tan, S.; Han, Y.; Wu, N.; Yi, P.; Tang, L.; Pan, Q.; Rao, S.; Liang, J.; Tang, Y.; Su, M.; Luo, X.; Yang, Y.; Shi, Y.; Wang, H.; Zhou, Y.; Liao, Q. The cancer metabolic reprogramming and immune response. Mol. Cancer, 2021, 20(1), 1-21.
[http://dx.doi.org/10.1186/s12943-021-01316-8] [PMID: 33546704]
[35]
Nachef, M.; Ali, A.K.; Almutairi, S.M.; Lee, S.H. Targeting SLC1A5 and SLC3A2/SLC7A5 as a potential strategy to strengthen anti-tumor immunity in the tumor microenvironment. Front. Immunol., 2021, 12, 624324.
[http://dx.doi.org/10.3389/fimmu.2021.624324] [PMID: 33953707]
[36]
Tronik-Le Roux, D.; Daouya, M.; Jacquier, A.; Schenowitz, C.; Desgrandchamps, F.; Rouas-Freiss, N.; Carosella, E.D. The HLA-G immune checkpoint: A new immuno-stimulatory role for the α1-domain-deleted isoform. Cell. Mol. Life Sci., 2022, 79(6), 310.
[http://dx.doi.org/10.1007/s00018-022-04359-2] [PMID: 35596891]
[37]
Ono, M.; Fukuda, I.; Nagao, M.; Tomiyama, K.; Okazaki-Hada, M.; Shuto, Y.; Kobayashi, S.; Yamaguchi, Y.; Nagamine, T.; Nakajima, Y.; Inagaki-Tanimura, K.; Sugihara, H. HLA analysis of immune checkpoint inhibitor-induced and idiopathic isolated ACTH deficiency. Pituitary, 2022, 25(4), 615-621.
[http://dx.doi.org/10.1007/s11102-022-01231-1] [PMID: 35653047]
[38]
Fisher, J.; Doyle, A.; Graham, L.; Khakoo, S.; Blunt, M. Disruption of the NKG2A:HLA-E immune checkpoint axis to enhance NK cell activation against cancer. Vaccines, 2022, 10(12), 1993.
[http://dx.doi.org/10.3390/vaccines10121993] [PMID: 36560403]
[39]
Liu, X.; Song, J.; Zhang, H.; Liu, X.; Zuo, F.; Zhao, Y.; Zhao, Y.; Yin, X.; Guo, X.; Wu, X.; Zhang, H.; Xu, J.; Hu, J.; Jing, J.; Ma, X.; Shi, H. Immune checkpoint HLA-E:CD94-NKG2A mediates evasion of circulating tumor cells from NK cell surveillance. Cancer Cell, 2023, 41(2), 272-287.
[http://dx.doi.org/10.1016/j.ccell.2023.01.001] [PMID: 36706761]
[40]
Gravina, A.; Tediashvili, G.; Zheng, Y.; Iwabuchi, K.A.; Peyrot, S.M.; Roodsari, S.Z.; Gargiulo, L.; Kaneko, S.; Osawa, M.; Schrepfer, S.; Deuse, T. Synthetic immune checkpoint engagers protect HLA-deficient iPSCs and derivatives from innate immune cell cytotoxicity. Cell Stem Cell, 2023, 30(11), 1538-1548.
[http://dx.doi.org/10.1016/j.stem.2023.10.003]
[41]
Naranbhai, V.; Viard, M.; Dean, M.; Groha, S.; Braun, D.A.; Labaki, C.; Shukla, S.A.; Yuki, Y.; Shah, P.; Chin, K.; Wind-Rotolo, M.; Mu, X.J.; Robbins, P.B.; Gusev, A.; Choueiri, T.K.; Gulley, J.L.; Carrington, M. HLA-A*03 and response to immune checkpoint blockade in cancer: An epidemiological biomarker study. Lancet Oncol., 2022, 23(1), 172-184.
[http://dx.doi.org/10.1016/S1470-2045(21)00582-9] [PMID: 34895481]
[42]
Noer, J.B.; Talman, M.L.M.; Moreira, J.M.A. HLA class II histocompatibility antigen γ chain (CD74) expression is associated with immune cell infiltration and favorable outcome in breast cancer. Cancers, 2021, 13(24), 6179.
[http://dx.doi.org/10.3390/cancers13246179] [PMID: 34944801]
[43]
Daull, A.M.; Dubois, V.; Labussière-Wallet, H.; Venet, F.; Barraco, F.; Ducastelle-Lepretre, S.; Larcher, M.V.; Balsat, M.; Gilis, L.; Fossard, G.; Ghesquières, H.; Heiblig, M.; Ader, F.; Alcazer, V. Class I/Class II HLA evolutionary divergence ratio is an independent marker associated with disease-free and overall survival after allogeneic hematopoietic stem cell transplantation for acute myeloid leukemia. Front. Immunol., 2022, 13, 841470.
[http://dx.doi.org/10.3389/fimmu.2022.841470] [PMID: 35309346]
[44]
Shao, X.M.; Huang, J.; Niknafs, N.; Balan, A.; Cherry, C.; White, J.; Velculescu, V.E.; Anagnostou, V.; Karchin, R. HLA class II immunogenic mutation burden predicts response to immune checkpoint blockade. Ann. Oncol., 2022, 33(7), 728-738.
[http://dx.doi.org/10.1016/j.annonc.2022.03.013] [PMID: 35339648]
[45]
Stupia, S.; Heeke, C.; Brüggemann, A.; Zaremba, A.; Thier, B.; Kretz, J.; Sucker, A.; Philip, M.; Zelinskyy, G.; Ferrone, S.; Roesch, A.; Horn, S.; Hadaschik, E.; Schadendorf, D.; Trilling, M.; Dittmer, U.; Griewank, K.; Zhao, F.; Paschen, A. HLA Class II Loss and JAK1/2 deficiency coevolve in melanoma leading to CD4 T-cell and IFNγ cross-resistance. Clin. Cancer Res., 2023, 29(15), 2894-2907.
[http://dx.doi.org/10.1158/1078-0432.CCR-23-0099] [PMID: 37199727]
[46]
Chen, X.; Zhang, J.; Lei, X.; Yang, L.; Li, W.; Zheng, L.; Zhang, S.; Ding, Y.; Shi, J.; Zhang, L.; Li, J.; Tang, T.; Jia, W. CD1C is associated with breast cancer prognosis and immune infiltrates. BMC Cancer, 2023, 23(1), 129.
[http://dx.doi.org/10.1186/s12885-023-10558-2] [PMID: 36755259]
[47]
Bourdely, P.; Anselmi, G.; Vaivode, K.; Ramos, R.N.; Missolo-Koussou, Y.; Hidalgo, S.; Tosselo, J.; Nuñez, N.; Richer, W.; Vincent-Salomon, A.; Saxena, A.; Wood, K.; Lladser, A.; Piaggio, E.; Helft, J.; Guermonprez, P. Transcriptional and functional analysis of CD1c+ human dendritic cells identifies a CD163+ subset priming CD8+CD103+ T cells. Immunity, 2020, 53(2), 335-352.e8.
[http://dx.doi.org/10.1016/j.immuni.2020.06.002] [PMID: 32610077]
[48]
Schwarze, J.K.; Tijtgat, J.; Awada, G.; Cras, L.; Vasaturo, A.; Bagnall, C.; Forsyth, R.; Dufait, I.; Tuyaerts, S.; Van Riet, I.; Neyns, B. Intratumoral administration of CD1c (BDCA-1) + and CD141 (BDCA-3) + myeloid dendritic cells in combination with talimogene laherparepvec in immune checkpoint blockade refractory advanced melanoma patients: A phase I clinical trial. J. Immunother. Cancer, 2022, 10(9), e005141.
[http://dx.doi.org/10.1136/jitc-2022-005141] [PMID: 36113895]
[49]
Delgado-Arévalo, C.; Calvet-Mirabent, M.; Triguero-Martínez, A.; Vázquez de Luis, E.; Benguría-Filippini, A.; Largo, R.; Calzada-Fraile, D.; Popova, O.; Sánchez-Cerrillo, I.; Tsukalov, I.; Moreno-Vellisca, R.; de la Fuente, H.; Herrero-Beaumont, G.; Ramiro, A.; Sánchez-Madrid, F.; Castañeda, S.; Dopazo, A.; González Álvaro, I.; Martin-Gayo, E. NLRC4-mediated activation of CD1c+ DC contributes to perpetuation of synovitis in rheumatoid arthritis. JCI Insight, 2022, 7(22), e152886.
[http://dx.doi.org/10.1172/jci.insight.152886] [PMID: 36194479]
[50]
Xu, Z.; Peng, B.; Kang, F.; Zhang, W.; Xiao, M.; Li, J.; Hong, Q.; Cai, Y.; Liu, W.; Yan, Y.; Peng, J. The roles of drug metabolism-related ADH1B in immune regulation and therapeutic response of ovarian cancer. Front. Cell Dev. Biol., 2022, 10, 877254.
[http://dx.doi.org/10.3389/fcell.2022.877254] [PMID: 35756990]
[51]
Chida, K.; Oshi, M.; Roy, A.M.; Sato, T.; Endo, I.; Takabe, K. Pancreatic ductal adenocarcinoma with a high expression of alcohol dehydrogenase 1B is associated with less aggressive features and a favorable prognosis. Am. J. Cancer Res., 2023, 13(8), 3638-3649.
[PMID: 37693153]
[52]
Zhou, Y.; Yu, L.; Huang, P.; Zhao, X.; He, R.; Cui, Y.; Pan, B.; Liu, C. Identification of afatinib-associated ADH1B and potential small-molecule drugs targeting ADH1B for hepatocellular carcinoma. Front. Pharmacol., 2023, 14, 1166454.
[http://dx.doi.org/10.3389/fphar.2023.1166454] [PMID: 37229243]
[53]
Zhou, Z.; Jia, D.; Kwon, O.; Li, S.; Sun, H.; Roudier, M.P.; Lin, D.W.; True, L.; Morrissey, C.; Creighton, C.J.; Lee, J.K.; Xin, L. Androgen-regulated stromal complement component 7 (C7) suppresses prostate cancer growth. Oncogene, 2023, 42(32), 2428-2438.
[http://dx.doi.org/10.1038/s41388-023-02759-7] [PMID: 37400528]
[54]
Tian, B.; Yang, J.; Zhao, Y.; Ivanciuc, T.; Sun, H.; Wakamiya, M.; Garofalo, R.P.; Brasier, A.R. Central role of the NF-κB pathway in the Scgb1a1 -expressing epithelium in mediating respiratory syncytial virus-induced airway inflammation. J. Virol., 2018, 92(11), e00441-18.
[http://dx.doi.org/10.1128/JVI.00441-18] [PMID: 29593031]
[55]
Xu, M.; Yang, W.; Wang, X.; Nayak, D.K. Lung secretoglobin scgb1a1 influences alveolar macrophage-mediated inflammation and immunity. Front. Immunol., 2020, 11, 584310.
[http://dx.doi.org/10.3389/fimmu.2020.584310] [PMID: 33117399]
[56]
Huang, F.W.; Song, H.; Weinstein, H.N.W.; Xie, J.; Cooperberg, M.R.; Hicks, J.; Mummert, L.; De Marzo, A.M.; Sfanos, K.S. Club-like cells in proliferative inflammatory atrophy of the prostate. J. Pathol., 2023, 261(1), 85-95.
[http://dx.doi.org/10.1002/path.6149] [PMID: 37550827]
[57]
Kimura, S.; Yokoyama, S.; Pilon, A.L.; Kurotani, R. Emerging role of an immunomodulatory protein secretoglobin 3A2 in human diseases. Pharmacol. Ther., 2022, 236, 108112.
[http://dx.doi.org/10.1016/j.pharmthera.2022.108112] [PMID: 35016921]
[58]
Lu, X.; Wang, N.; Long, X.B.; You, X.J.; Cui, Y.H.; Liu, Z. The cytokine-driven regulation of secretoglobins in normal human upper airway and their expression, particularly that of uteroglobin-related protein 1, in chronic rhinosinusitis. Respir. Res., 2011, 12(1), 1-10.
[http://dx.doi.org/10.1186/1465-9921-12-28] [PMID: 21385388]
[59]
Yokoyama, S.; Nakayama, S.; Xu, L.; Pilon, A.L.; Kimura, S. Secretoglobin 3A2 eliminates human cancer cells through pyroptosis. Cell Death Discov., 2021, 7(1), 12.
[http://dx.doi.org/10.1038/s41420-020-00385-w] [PMID: 33452234]
[60]
Lee, H.; Park, B.C.; Soon Kang, J.; Cheon, Y.; Lee, S.; Jae Maeng, P. MAM domain containing 2 is a potential breast cancer biomarker that exhibits tumour-suppressive activity. Cell Prolif., 2020, 53(9), e12883.
[http://dx.doi.org/10.1111/cpr.12883] [PMID: 32707597]
[61]
Zhao, B.; Gong, W.; Ma, A.; Chen, J.; Velegraki, M.; Dong, H.; Liu, Z.; Wang, L.; Okimoto, T.; Jones, D.M.; Lei, Y.L.; Long, M.; Oestreich, K.J.; Ma, Q.; Xin, G.; Carbone, D.P.; He, K.; Li, Z.; Wen, H. SUSD2 suppresses CD8+ T cell antitumor immunity by targeting IL-2 receptor signaling. Nat. Immunol., 2022, 23(11), 1588-1599.
[http://dx.doi.org/10.1038/s41590-022-01326-8] [PMID: 36266363]
[62]
Guo, W.; Shao, F.; Sun, S.; Song, P.; Guo, L.; Xue, X.; Zhang, G.; Zhang, H.; Gao, Y.; Qiu, B.; Tan, F.; Gao, S.; He, J. Loss of SUSD2 expression correlates with poor prognosis in patients with surgically resected lung adenocarcinoma. J. Cancer, 2020, 11(7), 1648-1656.
[http://dx.doi.org/10.7150/jca.39319] [PMID: 32194777]
[63]
Moreno-Sanchez, P.M.; Scafidi, A.; Salvato, I. SUSD2-IL-2 receptor interaction hinders antitumoral CD8 + T-cell activity: Implications for cancer immunotherapy. Allergy, 2023, 78(11), 3035-3037.
[http://dx.doi.org/10.1111/all.15804] [PMID: 37401528]
[64]
Deng, B.; Chen, X.; Xu, L.; Zheng, L.; Zhu, X.; Shi, J.; Yang, L.; Wang, D.; Jiang, D. Chordin-like 1 is a novel prognostic biomarker and correlative with immune cell infiltration in lung adenocarcinoma. Aging (Albany NY), 2022, 14(1), 389-409.
[http://dx.doi.org/10.18632/aging.203814] [PMID: 35021154]
[65]
Atiakshin, D.; Kostin, A.; Trotsenko, I.; Samoilova, V.; Buchwalow, I.; Tiemann, M. Carboxypeptidase A3-A key component of the protease phenotype of mast cells. Cells, 2022, 11(3), 570.
[http://dx.doi.org/10.3390/cells11030570] [PMID: 35159379]
[66]
Tirelli, C.; Pesenti, C.; Miozzo, M.; Mondoni, M.; Fontana, L.; Centanni, S. The genetic and epigenetic footprint in idiopathic pulmonary fibrosis and familial pulmonary fibrosis: A state-of-the-art review. Diagnostics, 2022, 12(12), 3107.
[http://dx.doi.org/10.3390/diagnostics12123107] [PMID: 36553114]
[67]
Yang, Y.; Wu, J.; Yu, X.; Wu, Q.; Cao, H.; Dai, X.; Chen, H. SLC34A2 promotes cancer proliferation and cell cycle progression by targeting TMPRSS3 in colorectal cancer. Pathol. Res. Pract., 2022, 229, 153706.
[http://dx.doi.org/10.1016/j.prp.2021.153706] [PMID: 34929599]
[68]
Keerthivasan, S.; Senbabaoglu, Y.; Martinez-Martin, N.; Husain, B.; Verschueren, E.; Wong, A.; Yang, Y.A.; Sun, Y.; Pham, V.; Hinkle, T.; Oei, Y.; Madireddi, S.; Corpuz, R.; Tam, L.; Carlisle, S.; Roose-Girma, M.; Modrusan, Z.; Ye, Z.; Koerber, J.T.; Turley, S.J. Homeostatic functions of monocytes and interstitial lung macrophages are regulated via collagen domain-binding receptor LAIR1. Immunity, 2021, 54(7), 1511-1526.
[http://dx.doi.org/10.1016/j.immuni.2021.06.012]
[69]
Zhu, J.; Fan, Y.; Xiong, Y.; Wang, W.; Chen, J.; Xia, Y.; Lei, J.; Gong, L.; Sun, S.; Jiang, T. Delineating the dynamic evolution from preneoplasia to invasive lung adenocarcinoma by integrating single-cell RNA sequencing and spatial transcriptomics. Exp. Mol. Med., 2022, 54(11), 2060-2076.
[http://dx.doi.org/10.1038/s12276-022-00896-9] [PMID: 36434043]
[70]
Hu, Z.; Jin, X.; Hong, W.; Sui, Q.; Zhao, M.; Huang, Y.; Li, M.; Wang, Q.; Zhan, C.; Chen, Z. Dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma. Cell Oncol. (Dordr.), 2023, 46(5), 1351-1368.
[http://dx.doi.org/10.1007/s13402-023-00816-7] [PMID: 37079186]
[71]
Qi, J.; Sun, H.; Zhang, Y.; Wang, Z.; Xun, Z.; Li, Z.; Ding, X.; Bao, R.; Hong, L.; Jia, W.; Fang, F.; Liu, H.; Chen, L.; Zhong, J.; Zou, D.; Liu, L.; Han, L.; Ginhoux, F.; Liu, Y.; Ye, Y.; Su, B. Single-cell and spatial analysis reveal interaction of FAP+ fibroblasts and SPP1+ macrophages in colorectal cancer. Nat. Commun., 2022, 13(1), 1742.
[http://dx.doi.org/10.1038/s41467-022-29366-6] [PMID: 35365629]
[72]
Weidemann, S.; Böhle, J.L.; Contreras, H.; Luebke, A.M.; Kluth, M.; Büscheck, F.; Hube-Magg, C.; Höflmayer, D.; Möller, K.; Fraune, C.; Bernreuther, C.; Rink, M.; Simon, R.; Menz, A.; Hinsch, A.; Lebok, P.; Clauditz, T.; Sauter, G.; Uhlig, R.; Wilczak, W.; Steurer, S.; Burandt, E.; Krech, R.; Dum, D.; Krech, T.; Marx, A.; Minner, S.; Napsin, A. Napsin A expression in human tumors and normal tissues. Pathol. Oncol. Res., 2021, 27, 613099.
[http://dx.doi.org/10.3389/pore.2021.613099] [PMID: 34257582]
[73]
Berner, F.; Bomze, D.; Lichtensteiger, C.; Walter, V.; Niederer, R.; Hasan Ali, O.; Wyss, N.; Bauer, J.; Freudenmann, L.K.; Marcu, A.; Wolfschmitt, E.M.; Haen, S.; Gross, T.; Abdou, M.T.; Diem, S.; Knöpfli, S.; Sinnberg, T.; Hofmeister, K.; Cheng, H.W.; Toma, M.; Klümper, N.; Purde, M.T.; Pop, O.T.; Jochum, A.K.; Pascolo, S.; Joerger, M.; Früh, M.; Jochum, W.; Rammensee, H.G.; Läubli, H.; Hölzel, M.; Neefjes, J.; Walz, J.; Flatz, L. Autoreactive napsin A–specific T cells are enriched in lung tumors and inflammatory lung lesions during immune checkpoint blockade. Sci. Immunol., 2022, 7(75), eabn9644.
[http://dx.doi.org/10.1126/sciimmunol.abn9644] [PMID: 36054337]
[74]
Amiri, Z.; Heidari, A.; Navimipour, N.J.; Esmaeilpour, M.; Yazdani, Y. The deep learning applications in IoT-based bio- and medical informatics: A systematic literature review. Neural Comput. Appl., 2024, 36(11), 5757-5797.
[http://dx.doi.org/10.1007/s00521-023-09366-3]
[75]
Amiri, Z.; Heidari, A.; Darbandi, M.; Yazdani, Y.; Jafari Navimipour, N.; Esmaeilpour, M.; Sheykhi, F.; Unal, M. The personal health applications of machine learning techniques in the internet of behaviors. Sustainability , 2023, 15(16), 12406.
[http://dx.doi.org/10.3390/su151612406]
[76]
Heidari, A.; Javaheri, D.; Toumaj, S.; Navimipour, N.J.; Rezaei, M.; Unal, M. A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems. Artif. Intell. Med., 2023, 141, 102572.
[http://dx.doi.org/10.1016/j.artmed.2023.102572] [PMID: 37295902]
[77]
Aminizadeh, S.; Heidari, A.; Toumaj, S.; Darbandi, M.; Navimipour, N.J.; Rezaei, M.; Talebi, S.; Azad, P.; Unal, M. The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things. Comput. Methods Programs Biomed., 2023, 241, 107745.
[http://dx.doi.org/10.1016/j.cmpb.2023.107745] [PMID: 37579550]
[78]
Aminizadeh, S.; Heidari, A.; Dehghan, M.; Toumaj, S.; Rezaei, M.; Jafari Navimipour, N.; Stroppa, F.; Unal, M. Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artif. Intell. Med., 2024, 149, 102779.
[http://dx.doi.org/10.1016/j.artmed.2024.102779] [PMID: 38462281]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy