Expression Analysis of Ligand-Receptor Pairs Identifies Cell-to-Cell Crosstalk between Macrophages and Tumor Cells in Lung Adenocarcinoma
This article is part of Special Issue:
Xiaodong Yang,
Zhao An,
Zhengyang Hu,
Junjie Xi,
Chenyang Dai,
Yuming Zhu,
Xiaodong Yang
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China tongji.edu.cn
Search for more papers by this authorZhao An
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China tongji.edu.cn
Search for more papers by this authorZhengyang Hu
Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China fudan.edu.cn
Search for more papers by this authorCorresponding Author
Junjie Xi
Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China fudan.edu.cn
Search for more papers by this authorCorresponding Author
Chenyang Dai
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China tongji.edu.cn
Search for more papers by this authorYuming Zhu
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China tongji.edu.cn
Search for more papers by this authorXiaodong Yang,
Zhao An,
Zhengyang Hu,
Junjie Xi,
Chenyang Dai,
Yuming Zhu,
Xiaodong Yang
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China tongji.edu.cn
Search for more papers by this authorZhao An
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China tongji.edu.cn
Search for more papers by this authorZhengyang Hu
Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China fudan.edu.cn
Search for more papers by this authorCorresponding Author
Junjie Xi
Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China fudan.edu.cn
Search for more papers by this authorCorresponding Author
Chenyang Dai
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China tongji.edu.cn
Search for more papers by this authorYuming Zhu
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China tongji.edu.cn
Search for more papers by this authorAcademic Editor: Xiangyang Yu
Abstract
Background. Lung adenocarcinoma is one of the most commonly diagnosed malignancies worldwide. Macrophage plays crucial roles in the tumor microenvironment, but its autocrine network and communications with tumor cell are still unclear. Methods. We acquired single-cell RNA sequencing (scRNA-seq) (n = 30) and bulk RNA sequencing (n = 1480) samples of lung adenocarcinoma patients from previous literatures and publicly available databases. Various cell subtypes were identified, including macrophages. Differentially expressed ligand-receptor gene pairs were obtained to explore cell-to-cell communications between macrophages and tumor cells. Furthermore, a machine-learning predictive model based on ligand-receptor interactions was built and validated. Results. A total of 159,219 single cells (18,248 tumor cells and 29,520 macrophages) were selected in this study. We identified significantly correlated autocrine ligand-receptor gene pairs in tumor cells and macrophages, respectively. Furthermore, we explored the cell-to-cell communications between macrophages and tumor cells and detected significantly correlated ligand-receptor signaling pairs. We determined that some of the hub gene pairs were associated with patient prognosis and constructed a machine-learning model based on the intercellular interaction network. Conclusion. We revealed significant cell-to-cell communications (both autocrine and paracrine network) within macrophages and tumor cells in lung adenocarcinoma. Hub genes with prognostic significance in the network were also identified.
Conflicts of Interest
All authors have no conflicts of interest to declare.
Open Research
Data Availability
The datasets generated and/or analyzed during the current study are available from previous literatures listed in the references, public datasets, and the corresponding authors on reasonable request.
Supporting Information
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| jimr9589895-sup-0001-f1.zipapplication/x-compressed, 3 MB | Supplementary Materials Supplement Figure 1: A. The integration of single-cell data with Harmony shows the sample corresponding cohort (red cluster: samples from E-MTAB-6149; green cluster: samples from E-MTAB-6653; blue cluster: samples from previous literatures). B. Three scRNA-seq are well integrated in the first 2 dimensions after Harmony. C. Overview distribution of the 159,219 single cells from 18 lung adenocarcinoma samples and 7 normal tissue samples (red cluster: normal samples; turquoise cluster: tumor samples). Supplement Figure 2: Expression of the cell typing marker genes for identifying tumor cells, alveolar cells, and macrophages. Supplement Figure 3: A. Dot plot of the expression of marker genes for cell subtypes. B. Dot plot of the expression of marker genes for macrophages. Supplement Figure 4. A. Heatmap of gene expression in the Hallmark TGF-β signaling pathway stratified by cell types in the scRNA-seq. B. Heatmap of gene expression in the KEGG allograft rejection signaling pathway stratified by cell types in the scRNA-seq. C. Heatmap of gene expression in the KEGG antigen processing and presentation signaling pathway stratified by cell types in the scRNA-seq. Supplement Figure 5. A. GO analysis for selected ligand-receptor genes in the crosstalk from macrophages to lung adenocarcinoma cells. B. GO analysis for selected ligand-receptor genes in the crosstalk from lung adenocarcinoma cells to macrophages. Supplement Figure 6: Identified and sorted the key cell marker genes in normal epithelial cells, lung adenocarcinoma cells, and macrophages by flow cytometry. A, B. FOLR1+/EPCAM- cells accounted for larger proportions than FOLR1-/EPCAM+ in normal lung samples (0.30% vs 1.95%, 0.19 vs 1.32%) (X-axis: PE-conjugated mouse antihuman FOLR1, Y-axis: Alexa 647-conjugated mouse antihuman EPCAM). C, D. FOLR1-/EPCAM+ cells accounted for larger proportions than FOLR1+/EPCAM- in lung adenocarcinoma samples (10.4% vs 2.03%, 17.1 vs 1.47%) (X-axis: PE-conjugated mouse antihuman FOLR1, Y-axis: Alexa 647-conjugated mouse antihuman EPCAM). E, F. CD163+ cells (macrophages) accounted for 2.66% and 3.86% in normal lung samples (X-axis: Alexa 647-conjugated mouse antihuman CD163). G, H. CD163+ cells (macrophages) accounted for 6.86% and 6.65% in lung adenocarcinoma samples (X-axis: Alexa 647-conjugated mouse anti-human CD163). Supplement Figure 7: Associations of the expressions of selected ligand or receptor genes with macrophage infiltrating in lung adenocarcinoma of the TCGA cohort by TIMER database. A. TGFB1 (P < 0.05, Spearman’s ρ = 0.261). B. ENG (P < 0.05, Spearman’s ρ = 0.293). C. B2M (P < 0.05, Spearman’s ρ = 0.175). D. HLA-F (P > 0.05, Spearman’s ρ = −0.082). E. SELPLG (P < 0.05, Spearman’s ρ = 0.321). F. ITGB2 (P < 0.05, Spearman’s ρ = 0.293). G. TGM2 (P < 0.05, Spearman’s ρ = 0.273). H. AGRP (P < 0.05, Spearman’s ρ = 0.163). I. PTPRS (P < 0.05, Spearman’s ρ = 0.169). J. CD4 (P < 0.05, Spearman’s ρ = 0.43). Supplement Figure 8: Validations of the expression changes of top ligand or receptor genes in lung adenocarcinoma cells and tumor-associated macrophages. A. The mRNA relative expressions level of TGFB1, ENG, TGM2, TBXA2R, HSPG2, and PTPRS were significantly increased in lung adenocarcinoma cells than normal epithelial cells. B. The mRNA relative expressions of TGFB1, ENG, B2M, HLA-F, SELPLG, and ITGB2 were significantly increased in tumor-associated macrophages than macrophages. Supplement Method: The detailed methods of 10× scRNA-seq and data preprocessing. Supplement Table 1: Characteristics of the 21 LUAD patients included in this study for scRNA-seq analysis. Supplement Table 2: Baseline characteristics of enrolled patient cohorts from GEO and TCGA databases. Supplement Table 3: Identified significant ligand-receptor gene pairs in the cell-to-cell communications within tumor cells and macrophages in lung adenocarcinoma. |
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References
- 1 Bray F., Ferlay J., Soerjomataram I., Siegel R. L., Torre L. A., and Jemal A., Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: a Cancer Journal for Clinicians. (2018) 68, no. 6, 394–424, https://doi.org/10.3322/caac.21492, 2-s2.0-85053395052, 30207593.
- 2 Siegel R. L., Miller K. D., and Jemal A., Cancer statistics, 2019, CA: a Cancer Journal for Clinicians. (2019) 69, no. 1, 7–34, https://doi.org/10.3322/caac.21551, 2-s2.0-85059687888, 30620402.
- 3 Lu T., Yang X., Huang Y., Zhao M., Li M., Ma K., Yin J., Zhan C., and Wang Q., Trends in the incidence, treatment, and survival of patients with lung cancer in the last four decades, Cancer Management and Research. (2019) Volume 11, 943–953, https://doi.org/10.2147/CMAR.S187317, 2-s2.0-85060727509, 30718965.
- 4 Chen Y. P., Zhang Y., Lv J. W., Li Y. Q., Wang Y. Q., He Q. M., Yang X. J., Sun Y., Mao Y. P., Yun J. P., Liu N., and Ma J., Genomic analysis of tumor microenvironment immune types across 14 solid cancer types: immunotherapeutic implications, Theranostics. (2017) 7, no. 14, 3585–3594, https://doi.org/10.7150/thno.21471, 2-s2.0-85028504433, 28912897.
- 5 Jing X., Yang F., Shao C., Wei K., Xie M., Shen H., and Shu Y., Role of hypoxia in cancer therapy by regulating the tumor microenvironment, Molecular Cancer. (2019) 18, no. 1, https://doi.org/10.1186/s12943-019-1089-9, 31711497.
- 6 Yang X., Shi Y., Li M., Lu T., Xi J., Lin Z., Jiang W., Guo W., Zhan C., and Wang Q., Identification and validation of an immune cell infiltrating score predicting survival in patients with lung adenocarcinoma, Journal of Translational Medicine. (2019) 17, no. 1, https://doi.org/10.1186/s12967-019-1964-6, 2-s2.0-85069043336, 31286969.
- 7 Pitt J. M., Marabelle A., Eggermont A., Soria J. C., Kroemer G., and Zitvogel L., Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy, Annals of Oncology. (2016) 27, no. 8, 1482–1492, https://doi.org/10.1093/annonc/mdw168, 2-s2.0-84984941679, 27069014.
- 8 Pouniotis D. S., Plebanski M., Apostolopoulos V., and McDonald C. F., Alveolar macrophage function is altered in patients with lung cancer, Clinical and Experimental Immunology. (2006) 143, no. 2, 363–372, https://doi.org/10.1111/j.1365-2249.2006.02998.x, 2-s2.0-33644850788, 16412062.
- 9 Ohtaki Y., Ishii G., Nagai K., Ashimine S., Kuwata T., Hishida T., Nishimura M., Yoshida J., Takeyoshi I., and Ochiai A., Stromal macrophage expressing CD204 is associated with tumor aggressiveness in lung adenocarcinoma, Journal of Thoracic Oncology. (2010) 5, no. 10, 1507–1515, https://doi.org/10.1097/JTO.0b013e3181eba692, 2-s2.0-77958187950, 20802348.
- 10 Lavin Y., Kobayashi S., Leader A., Amir E. D., Elefant N., Bigenwald C., Remark R., Sweeney R., Becker C. D., Levine J. H., Meinhof K., Chow A., Kim-Shulze S., Wolf A., Medaglia C., Li H., Rytlewski J. A., Emerson R. O., Solovyov A., Greenbaum B. D., Sanders C., Vignali M., Beasley M. B., Flores R., Gnjatic S., Pe'Er D., Rahman A., Amit I., and Merad M., Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses, Cell. (2017) 169, no. 4, 750–765.e17, https://doi.org/10.1016/j.cell.2017.04.014, 2-s2.0-85018766073, 28475900.
- 11 Kulkarni A., Anderson A. G., Merullo D. P., and Konopka G., Beyond bulk: a review of single cell transcriptomics methodologies and applications, Current Opinion in Biotechnology. (2019) 58, 129–136, https://doi.org/10.1016/j.copbio.2019.03.001, 2-s2.0-85064071400, 30978643.
- 12 Azizi E., Carr A. J., Plitas G., Cornish A. E., Konopacki C., Prabhakaran S., Nainys J., Wu K., Kiseliovas V., Setty M., Choi K., Fromme R. M., Dao P., McKenney P. T., Wasti R. C., Kadaveru K., Mazutis L., Rudensky A. Y., and Pe'Er D., Single-cell map of diverse immune phenotypes in the breast tumor microenvironment, Cell. (2018) 174, no. 5, 1293–1308.e36, https://doi.org/10.1016/j.cell.2018.05.060, 2-s2.0-85048957607, 29961579.
- 13 Aoki T., Chong L. C., Takata K., Milne K., Hav M., Colombo A., Chavez E. A., Nissen M., Wang X., Miyata-Takata T., Lam V., Vigano E., Woolcock B. W., Telenius A., Li M. Y., Healy S., Ghesquiere C., Kos D., Goodyear T., Veldman J., Zhang A. W., Kim J., Saberi S., Ding J., Farinha P., Weng A. P., Savage K. J., Scott D. W., Krystal G., Nelson B. H., Mottok A., Merchant A., Shah S. P., and Steidl C., Single cell transcriptome analysis reveals disease-defining T cell subsets in the tumor microenvironment of classic Hodgkin lymphoma, Cancer Discovery. (2020) 10, no. 3, 406–421.
- 14 Lambrechts D., Wauters E., Boeckx B., Aibar S., Nittner D., Burton O., Bassez A., Decaluwe H., Pircher A., Van den Eynde K., Weynand B., Verbeken E., De Leyn P., Liston A., Vansteenkiste J., Carmeliet P., Aerts S., and Thienpont B., Phenotype molding of stromal cells in the lung tumor microenvironment, Nature Medicine. (2018) 24, no. 8, 1277–1289, https://doi.org/10.1038/s41591-018-0096-5, 2-s2.0-85049650563, 29988129.
- 15 Chen Z., Huang Y., Hu Z., Zhao M., Li M., Bi G., Zheng Y., Liang J., Lu T., Jiang W., Xu S., Zhan C., Xi J., Wang Q., and Tan L., Landscape and dynamics of single tumor and immune cells in early and advanced-stage lung adenocarcinoma, Clinical and Translational Medicine. (2021) 11, no. 3, article e350, https://doi.org/10.1002/ctm2.350.
- 16 Bolstad B. M., Irizarry R. A., Astrand M., and Speed T. P., A comparison of normalization methods for high density oligonucleotide array data based on variance and bias, Bioinformatics. (2003) 19, no. 2, 185–193, https://doi.org/10.1093/bioinformatics/19.2.185, 2-s2.0-0037316303, 12538238.
- 17 Irizarry R. A., Bolstad B. M., Collin F., Cope L. M., Hobbs B., and Speed T. P., Summaries of Affymetrix GeneChip probe level data, Nucleic Acids Research. (2003) 31, no. 4, article e15, 15e–115, https://doi.org/10.1093/nar/gng015, 2-s2.0-12344280017, 12582260.
- 18 Irizarry R. A., Hobbs B., Collin F., Beazer-Barclay Y. D., Antonellis K. J., Scherf U., and Speed T. P., Exploration, normalization, and summaries of high density oligonucleotide array probe level data, Biostatistics. (2003) 4, no. 2, 249–264, https://doi.org/10.1093/biostatistics/4.2.249, 2-s2.0-0142121516, 12925520.
- 19 Macosko E. Z., Basu A., Satija R., Nemesh J., Shekhar K., Goldman M., Tirosh I., Bialas A. R., Kamitaki N., Martersteck E. M., Trombetta J. J., Weitz D. A., Sanes J. R., Shalek A. K., Regev A., and McCarroll S. A., Highly parallel genome-wide expression profiling of individual cells using Nanoliter droplets, Cell. (2015) 161, no. 5, 1202–1214, https://doi.org/10.1016/j.cell.2015.05.002, 2-s2.0-84929684999, 26000488.
- 20 Zhang X., Lan Y., Xu J., Quan F., Zhao E., Deng C., Luo T., Xu L., Liao G., Yan M., Ping Y., Li F., Shi A., Bai J., Zhao T., Li X., and Xiao Y., CellMarker: a manually curated resource of cell markers in human and mouse, Nucleic Acids Research. (2019) 47, no. D1, D721–D728, https://doi.org/10.1093/nar/gky900, 2-s2.0-85059796739, 30289549.
- 21 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., and Bhattacharya M., Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage, Nature Immunology. (2019) 20, no. 2, 163–172, https://doi.org/10.1038/s41590-018-0276-y, 2-s2.0-85060111238, 30643263.
- 22 Yuan D., Tao Y., Chen G., and Shi T., Systematic expression analysis of ligand-receptor pairs reveals important cell-to-cell interactions inside glioma, Cell Communication and Signaling: CCS. (2019) 17, no. 1, https://doi.org/10.1186/s12964-019-0363-1, 2-s2.0-85066492229, 31118022.
- 23 Chen Z., Yang X., Bi G., Liang J., Hu Z., Zhao M., Li M., Lu T., Zheng Y., Sui Q., Yang Y., Zhan C., Jiang W., Wang Q., and Tan L., Ligand-receptor interaction atlas within and between tumor cells and T cells in lung adenocarcinoma, International Journal of Biological Sciences. (2020) 16, no. 12, 2205–2219, https://doi.org/10.7150/ijbs.42080, 32549766.
- 24 Ramilowski J. A., Goldberg T., Harshbarger J., Kloppmann E., Lizio M., Satagopam V. P., Itoh M., Kawaji H., Carninci P., Rost B., and Forrest A. R., A draft network of ligand-receptor-mediated multicellular signalling in human, Nature Communications. (2015) 6, no. 1, https://doi.org/10.1038/ncomms8866, 2-s2.0-84937934040, 26198319.
- 25 Finak G., McDavid A., Yajima M., Deng J., Gersuk V., Shalek A. K., Slichter C. K., Miller H. W., McElrath M. J., Prlic M., Linsley P. S., and Gottardo R., MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data, Genome Biology. (2015) 16, no. 1, https://doi.org/10.1186/s13059-015-0844-5, 2-s2.0-84951574149, 26653891.
- 26 Hanzelmann S., Castelo R., and Guinney J., GSVA: gene set variation analysis for microarray and RNA-seq data, BMC Bioinformatics. (2013) 14, no. 1, https://doi.org/10.1186/1471-2105-14-7, 2-s2.0-84872202078, 23323831.
- 27 Sun Y., Wu L., Zhong Y., Zhou K., Hou Y., Wang Z., Zhang Z., Xie J., Wang C., Chen D., Huang Y., Wei X., Shi Y., Zhao Z., Li Y., Guo Z., Yu Q., Xu L., Volpe G., Qiu S., Zhou J., Ward C., Sun H., Yin Y., Xu X., Wang X., Esteban M. A., Yang H., Wang J., Dean M., Zhang Y., Liu S., Yang X., and Fan J., Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma, Cell. (2021) 184, no. 2, 404–421.e16, https://doi.org/10.1016/j.cell.2020.11.041, 33357445.
- 28 Jin S., Guerrero-Juarez C. F., Zhang L., Chang I., Ramos R., Kuan C. H., Myung P., Plikus M. V., and Nie Q., Inference and analysis of cell-cell communication using CellChat, Nature Communications. (2021) 12, no. 1, https://doi.org/10.1038/s41467-021-21246-9, 33597522.
- 29 Xie Y., Zhang C., Hu X., Zhang C., Kelley S. P., Atwood J. L., and Lin J., Machine learning assisted synthesis of metal-organic Nanocapsules, Journal of the American Chemical Society. (2020) 142, no. 3, 1475–1481.
- 30 Kilic A., Goyal A., Miller J. K., Gjekmarkaj E., Tam W. L., Gleason T. G., Sultan I., and Dubrawksi A., Predictive utility of a machine learning algorithm in estimating mortality risk in cardiac surgery, The Annals of Thoracic Surgery. (2020) 109, no. 6, 1811–1819.
- 31 Polano M., Chierici M., Dal Bo M., Gentilini D., Di Cintio F., Baboci L., Gibbs D. L., Furlanello C., and Toffoli G., A pan-cancer approach to predict responsiveness to immune checkpoint inhibitors by machine learning, Cancers. (2019) 11, no. 10, https://doi.org/10.3390/cancers11101562, 2-s2.0-85073712701, 31618839.
- 32 Chen Z., Zhao M., Li M., Sui Q., Bian Y., Liang J., Hu Z., Zheng Y., Lu T., Huang Y., Zhan C., Jiang W., Wang Q., and Tan L., Identification of differentially expressed genes in lung adenocarcinoma cells using single-cell RNA sequencing not detected using traditional RNA sequencing and microarray, Laboratory Investigation. (2020) 100, no. 10, 1318–1329, https://doi.org/10.1038/s41374-020-0428-1.
- 33 Zeng D., Li M., Zhou R., Zhang J., Sun H., Shi M., Bin J., Liao Y., Rao J., and Liao W., Tumor microenvironment characterization in gastric cancer identifies prognostic and immunotherapeutically relevant gene signatures, Cancer Immunology Research. (2019) 7, no. 5, 737–750, https://doi.org/10.1158/2326-6066.CIR-18-0436, 2-s2.0-85065508481, 30842092.
- 34 Jiang Y., Zhang Q., Hu Y., Li T., Yu J., Zhao L., Ye G., Deng H., Mou T., Cai S., Zhou Z., Liu H., Chen G., Li G., and Qi X., ImmunoScore signature: a prognostic and predictive tool in gastric cancer, Annals of Surgery. (2018) 267, no. 3, 504–513, https://doi.org/10.1097/SLA.0000000000002116, 2-s2.0-85007174711.
- 35 Zhang J., Guan M., Wang Q., Zhang J., Zhou T., and Sun X., Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas, Briefings in Bioinformatics. (2020) 21, no. 3, 1080–1097, https://doi.org/10.1093/bib/bbz040, 31329830.
- 36 Sun X., Liu X., Xia M., Shao Y., and Zhang X. D., Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas, Journal of Translational Medicine. (2019) 17, no. 1, https://doi.org/10.1186/s12967-019-1908-1, 2-s2.0-85065865281, 31097021.
- 37 Dehne N., Mora J., Namgaladze D., Weigert A., and Brune B., Cancer cell and macrophage cross-talk in the tumor microenvironment, Current Opinion in Pharmacology. (2017) 35, 12–19, https://doi.org/10.1016/j.coph.2017.04.007, 2-s2.0-85019383098.
- 38 Cantelmo A. R., Conradi L. C., Brajic A., Goveia J., Kalucka J., Pircher A., Chaturvedi P., Hol J., Thienpont B., Teuwen L. A., Schoors S., Boeckx B., Vriens J., Kuchnio A., Veys K., Cruys B., Finotto L., Treps L., Stav-Noraas T. E., Bifari F., Stapor P., Decimo I., Kampen K., De Bock K., Haraldsen G., Schoonjans L., Rabelink T., Eelen G., Ghesquiere B., Rehman J., Lambrechts D., Malik A. B., Dewerchin M., and Carmeliet P., Inhibition of the glycolytic activator PFKFB3 in endothelium induces tumor vessel normalization, impairs metastasis, and improves chemotherapy, Cancer Cell. (2016) 30, no. 6, 968–985, https://doi.org/10.1016/j.ccell.2016.10.006, 2-s2.0-85004144063, 27866851.
- 39 Banerjee D., Gorlick R., Liefshitz A., Danenberg K., Danenberg P. C., Danenberg P. V., Klimstra D., Jhanwar S., Cordon-Cardo C., Fong Y., Kemeny N., and Bertino J. R., Levels of E2F-1 expression are higher in lung metastasis of colon cancer as compared with hepatic metastasis and correlate with levels of thymidylate synthase, Cancer Research. (2000) 60, no. 9, 2365–2367, 10811110.
- 40 Macerelli M., Ganzinelli M., Gouedard C., Broggini M., Garassino M. C., Linardou H., Damia G., and Wiesmuller L., Can the response to a platinum-based therapy be predicted by the DNA repair status in non-small cell lung cancer?, Cancer Treatment Reviews. (2016) 48, 8–19, https://doi.org/10.1016/j.ctrv.2016.05.004, 2-s2.0-84973624944, 27262017.
- 41 Gordan J. D., Thompson C. B., and Simon M. C., HIF and c-Myc: sibling rivals for control of cancer cell metabolism and proliferation, Cancer Cell. (2007) 12, no. 2, 108–113, https://doi.org/10.1016/j.ccr.2007.07.006, 2-s2.0-34547580590, 17692803.
- 42 Staudt N. D., Jo M., Hu J., Bristow J. M., Pizzo D. P., Gaultier A., VandenBerg S. R., and Gonias S. L., Myeloid cell receptor LRP1/CD91 regulates monocyte recruitment and angiogenesis in tumors, Cancer Research. (2013) 73, no. 13, 3902–3912, https://doi.org/10.1158/0008-5472.CAN-12-4233, 2-s2.0-84880054952, 23633492.
- 43 Chen F., Yuan J., Yan H., Liu H., and Yin S., Chemokine receptor CXCR3 correlates with decreased M2 macrophage infiltration and favorable prognosis in gastric cancer, BioMed Research International. (2019) 2019, 8, 6832867, https://doi.org/10.1155/2019/6832867, 2-s2.0-85067007960.
- 44 Van den Bossche J., O'Neill L. A., and Menon D., Macrophage Immunometabolism: where are we (going)?, Trends in Immunology. (2017) 38, no. 6, 395–406, https://doi.org/10.1016/j.it.2017.03.001, 2-s2.0-85017144052, 28396078.
- 45 Zhang Q., Lou Y., Bai X. L., and Liang T. B., Immunometabolism: a novel perspective of liver cancer microenvironment and its influence on tumor progression, World Journal of Gastroenterology. (2018) 24, no. 31, 3500–3512, https://doi.org/10.3748/wjg.v24.i31.3500, 2-s2.0-85052134887, 30131656.
- 46 Dyck L. and Lynch L., Cancer, obesity and immunometabolism - connecting the dots, Cancer Letters. (2018) 417, 11–20, https://doi.org/10.1016/j.canlet.2017.12.019, 2-s2.0-85039722570, 29253522.
- 47 Mazumdar C., Driggers E. M., and Turka L. A., The untapped opportunity and challenge of immunometabolism: a new paradigm for drug discovery, Cell Metabolism. (2020) 31, no. 1, 26–34, https://doi.org/10.1016/j.cmet.2019.11.014, 31839485.
- 48 Newman A. M., Liu C. L., Green M. R., Gentles A. J., Feng W., Xu Y., Hoang C. D., Diehn M., and Alizadeh A. A., Robust enumeration of cell subsets from tissue expression profiles, Nature Methods. (2015) 12, no. 5, 453–457, https://doi.org/10.1038/nmeth.3337, 2-s2.0-84928927858, 25822800.
- 49 Li B., Severson E., Pignon J. C., Zhao H., Li T., Novak J., Jiang P., Shen H., Aster J. C., Rodig S., Signoretti S., Liu J. S., and Liu X. S., Comprehensive analyses of tumor immunity: implications for cancer immunotherapy, Genome Biology. (2016) 17, no. 1, https://doi.org/10.1186/s13059-016-1028-7, 2-s2.0-84983347823, 27549193.
- 50 Alizadeh A. A., Aranda V., Bardelli A., Blanpain C., Bock C., Borowski C., Caldas C., Califano A., Doherty M., Elsner M., Esteller M., Fitzgerald R., Korbel J. O., Lichter P., Mason C. E., Navin N., Pe'Er D., Polyak K., Roberts C. W., Siu L., Snyder A., Stower H., Swanton C., Verhaak R. G., Zenklusen J. C., Zuber J., and Zucman-Rossi J., Toward understanding and exploiting tumor heterogeneity, Nature Medicine. (2015) 21, no. 8, 846–853, https://doi.org/10.1038/nm.3915, 2-s2.0-84938799837, 26248267.
- 51 Kim N., Kim H. K., Lee K., Hong Y., Cho J. H., Choi J. W., Lee J. I., Suh Y. L., Ku B. M., Eum H. H., Choi S., Choi Y. L., Joung J. G., Park W. Y., Jung H. A., Sun J. M., Lee S. H., Ahn J. S., Park K., Ahn M. J., and Lee H. O., Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma, Nature Communications. (2020) 11, no. 1, https://doi.org/10.1038/s41467-020-16164-1, 32385277.
- 52 Cheng J., Zhang J., Wu Z., and Sun X., Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19, Briefings in Bioinformatics. (2021) 22, no. 2, 988–1005, https://doi.org/10.1093/bib/bbaa327, 33341869.
