Pan-cancer Analysis Reveals SRC May Link Lipid Metabolism and Macrophages

1. Background

Cancer is the second most frequent cause of death globally and has become a significant public health concern ( 1 ). Recent studies have revealed that Tyrosine-Protein Kinase Src (SRC) -family protein tyrosine kinases (SFKs), a subfamily of non-receptor tyrosine kinases, is one of the most potential target for cancer treatment. The SFKs are the most prominent non-receptor tyrosine kinases, including Tyrosine-Protein Kinase Lyn (LYN), Tyrosine-Protein Kinase Fyn (FYN), Tyrosine-Protein Kinase Lck (LCK), Tyrosine-Protein Kinase Hck (HCK), Tyrosine-Protein Kinase Fgr (FGR), Tyrosine-Protein Kinase Blk (BLK), Tyrosine-Protein Kinase Yrk (YRK), Tyrosine-Protein Kinase Yes (YES), and SRC ( 2 ). SRC, also known as c-SRC, can be activated by multiple signal transduction pathways ( 3 ), such as the activated SRC kinase could phosphorylate the tyrosine residues of the corresponding target proteins, including MAPK, STAT, PI3K/AKT, and EGFR. In humans, the SRC has a multifaceted effect on cell morphology, adhesion, invasion, proliferation, and differentiation ( 4 ). The SRC protein that are abnormally activated has been linked to many tumors and studied to be closely related to the progression of multiple tumors ( 5 ). The specific mechanism through which SRC induce tumor morphology include promoting the proliferation of cancer cells, triggering metastasis, and inducing angiogenesis ( 6 ).

The tumor microenvironment (TME) is the surrounding microenvironment in which tumor cells exist and comprises of non-tumorous cells, cytokines, chemokines, and extracellular matrix ( 7 , 8 ). The non-tumorous cells mainly include vascular endothelial cells, lymphatic endothelial cells, innate immune cells (Tumor-associated macrophages (TAMs), Dendritic cells (DCs), natural killer (NK) cells), acquired immune cells (T and B lymphocytes), and various stromal cells, such as cancer-associated fibroblasts (CAFs) ( 7 , 8 ). The TME is a determinant of tumor heterogeneity and interactions within these cells can restructure the TME to regulate tumor progression ( 9 ). A constant tumor antigen stimulation and activation of immune system lead to the exhaustion or alteration of relevant effector cells in TME to the extent that these cells have defective function or even assist in tumor growth, resulting in an immunosuppressive microenvironment ( 8 ). Therefore, the correlation studies between immune cell infiltration and clinical outcome of tumor will help in finding drug targets to improve patients’ survival.

Several studies have confirmed that tumor immuno-therapy combined with an autologous dendritic vaccine targeting T lymphocytes can improve the prognosis of patients with metastatic prostate cancer ( 9 , 10 ). The tumor-promoting role of glioma-associated macrophages (GAMs), regulatory T cells (Treg), and the inactivation of natural killer (NK) cells in the TME weakens the anti-tumor effect and is closely related to the formation of glioma immunosuppressive microenvironment ( 7 , 11 ). Knowledge of the proper functioning of infiltered immune cells will help in designing effective measures and strategies that will positively impact anti-tumour immunotherapy.

2. Objectives

The “basket clinical trial” is a treatment strategy under the new understanding of “treating different diseases with the same treatment.” Patients who have a common altered molecular pathway for multiple diseases are treated under “basket clinical trial” ( 12 ). Generally, SRC kinase is hyperactivated in many types of cancer, including colon cancer and breast cancer, thus becomes a promising target for pan-cancer treatment. However, the mechanism underlying the hyperactivation of SRC in multiple cancers has not been understood fully. It has been hypothesized that this commonly expressed gene interacts with immune infiltration and may be a potential treatment target considering basket trials. Therefore, the present study aims to reveal role of SRC gene in pan-cancer, and potential pharmaceutical predictions. This study uses bioinformatic tools to analyze the relationship between SRC genes, the immune microenvironment, and the mechanisms regulating cancer progression.

3. Materials and Methods

3.1. The Relationship Between SRC and Survival in Pan-Cancer

Kaplan-Meier Plotter ( 13 ) was used to analyze the effect of SRC gene expression on the overall survival (OS) and relapse-free survival (RFS) of 20 cancer types, including kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), pancreatic adenocarcinoma (PAAD), testicular germ cell tumor (TGCT), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), bladder urothelial carcinoma (BLCA), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), esophageal car-cinoma (ESCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pheo-chromocytoma and paraganglioma (PCPG), pancreatic adenocarcinoma (PAAD), and Sarcoma (SARC). Next, log-rank test was used to compare the OS and Recurrence-Free Survival (RFS) between low-and high-SRC expression groups. A p-value < 0.05 was considered statistically significant. The expression profile and clinical information of cancers were sourced from TCGA database.

3.2. Immune Infiltration Analysis of Pan-Cancer

The TIMER2.0 database ( 14 ) was applied to analyze tumor purity and the immune infiltration of neutrophils, macrophages, dendritic cells, CD8⁺ T cells, CD4⁺ T cells, and B cells. Correlation among SRC expressions and tumor purity and immune infiltration of these six immune cell types were calculated. The CIBERSORT algorithm was used to ensure their relationship and to evaluate the infiltration of neutrophils, macrophages, dendritic cells, CD8⁺ T cells, CD4⁺ T cells, and B cells, followed by their correlation analysis. A p-value < 0.05 was considered significantly correlated. Similarly, gene expression data of cancer samples were downloaded from TCGA database.

3.3. Analysis of SRC Co-Expressed Genes in Pan-Cancer

The LinkedOmics database ( 15 ) identified genes that co-expressed with SRC in different cancer using Pearson’s correlation and were displayed in the heatmaps. Metascape online tool () was used to analyse the biological function of the SRC co-expressed genes. The STRING database () helped explore the interactions of SRC co-expressed genes, and Cytoscape software was used to construct and visualise the protein-protein interaction (PPI) network. Moreover, the MCODE plug-in was applied to screen hub modules in the PPI network. The clusterProfiler package analysed the biological function of genes in hub modules. Next, we chose the interesting candidate genes based on their biological function, and the correlation between their expressions and immune infiltration was calculated.

3.4. Statistical Analysis

R software (version 3.6.1) was used to conduct all statistical analyses. Log-rank test was used for comparing the OS and RFS between low- and high-SRC expression group. Correlations between gene expressions and proportion of immune cell infiltration were calculated by Spearman method. is seen as the significant difference.

4. Results

4.1. The Expression of SRC Is Related to OS and RFS in 11 Cancer Types

By Kaplan-Meier Plotter, we observed that patients with higher expressions of SRC had significantly better OS in BLCA, STAD, and THCA, while patients in KIRC, KIRP, LIHC, PAAD, TGCT, THYM, and UCEC had significantly worse OS (Fig. 1A-1J). Moreover, the patients in ESCA, KIRP, LIHC, PAAD, and THCA groups were observed to have markedly worse RFS with increased SRC expression (Fig. 1K-1O). This indicates that SRC may play an important role in the prognosis of BLCA, STAD, THCA, ESCA, KIRC, KIRP, LIHC, PAAD, TGCT, THYM, and UCEC.

Figure 1.Survival analysis with SRC expression in diverse cancer types using Kaplan-Meier (KM) database. Overall Survival (OS) KM plots stating SRC a good prognosis in A) Bladder Urothelial Carcinoma (BLCA), B) Stomach Adenocarcinoma (STAD), C) Thyroid Carcinoma (THCA). SRC as a bad prognosis in D) Kidney Renal Clear Cell Carcinoma (KIRC), E) Kidney Renal Papillary Cell Carcinoma (KIRP), F) Liver Hepatocellular Carcinoma (LIHC), G) Pancreatic Adenocarcinoma (PAAD), H) Testicular Germ Cell Tumor (TGCT), I) Thymoma (THYM), and J) Uterine Corpus Endometrial Carcinoma (UCEC). Recurrence-Free Survival (RFS) KM plot in K) Esophageal Carcinoma (ESCA), L) Kidney Renal Papillary Cell Carcinoma (KIRP), M) Liver Hepatocellular Carcinoma (LIHC), N) Pancreatic adenocarcinoma (PAAD) and O) Thyroid Carcinoma (THCA).

4.2. The Expression of SRC Is Closely Correlated with M1 Macrophages in Multiple Cancers

Thus, we detected the relationship between SRC and immune cell infiltration in BLCA, STAD, THCA, ESCA, KIRC, KIRP, LIHC, PAAD, TGCT, THYM, and UCEC. Interestingly, both TIMER2.0 (Fig. 2A) CIBERSORT (Fig. 2B) databases revealed that SRC expressions in LIHC, TGCT, THCA were positively correlated with macrophage infiltration (p<0.05). However, for THYM, we found conflicted results that the expression of SRC was found to be negatively correlated with macrophage infiltration by TIMER, but positively correlated by CIBERSORT. Moreover, according to TIMER2.0, CD4⁺ T cells and Dendritic Cell also demonstrated similar correlation patterns in LIHC, TGCT, THCA (r<0) and THYM (r<0) significantly.

Figure 2.Correlation of SRC expression with immune infiltration analysis. Correlation of SRC expression with immune infiltration levels in the A) TIMER database and B) CIBERSORT database. Correlation of SRC expression with infiltration of M1 macrophages level in the C) TIMER and D) CIBERSORT database.

Correlation studies between the expressions of SRC and marker genes of monocytes/M1/M2/ TAMs ( 16 ) were conducted to demonstrate the relationship between SRC and macrophages further. We found that in LIHC, TGCT, THCA and THYM, the expression of SRC was positively correlated with all the four macrophage subtypes using TIMER2.0 database (Fig. 2C) and GEPIA2 database (Fig. 2D), especially M1 macrophages (LIHC: IRF5, r = 0.37/ 0.34, PTGS2, r= 0.298/ 0.29; TGCT: NOS2, r = 0.52/ 0.5, IRF5, r = 0.53/ 0.38, PTGS2, r = 0.46/ 0.3; THCA: IRF5, r = 0.44/ 0.32; THYM: NOS2, r = 0.21/ 0.46, IRF5, r = 0.5/ 0.43, PTGS2, r = 0.52/ 0.26), indicating that SRC may contribute to the M1 macrophage polarisation in LIHC, TGCT, THCA, and THYM.

4.3. SRC Co-Expressed Genes Are Associated with Metabolism

Next, we investigated the genes that co-expressed with SRC in LIHC, TGCT, THCA, and THYM. We observed that the top 50 genes positively or negatively correlated with SRC in LIHC, TGCT, THCA, and THYM and were shown in the heatmaps (Fig. 3 and Table S1-S4).

Figure 3.SRC co-expressed genes in pan-cancers.A) negatively, and B) positively correlated SRC and co-expressed genes in LIHC as analysed by Pearson test (LinkedOmics). C) negatively, and D. positively correlated SRC and co-expressed genes in TCGT as analysed by Pearson test (LinkedOmics). E) negatively, and F) positively correlated SRC and co-expressed genes in THCA as analysed by Pearson test (LinkedOmics). G) negatively, and H) positively correlated SRC and co-expressed genes in THYM as analysed by Pearson test (LinkedOmics).

The results of Metascape inferred that SRC co-expressed genes were mainly enriched in the biological functions associated with metabolism, including positive regulation of ion and anion transport, circulatory system process, lipid metabolic process, lipid biosynthesis process, and lipid metabolism (Fig. 4A). Next, the top 100 SRC co-expressed genes in LIHC, TGCT, and THCA and the top 200 SRC co-expressed genes in THYM were analysed using the STRING database to construct the PPI network (Fig. 4B). Further, module analysis was performed by MCODE plug-in which resulted in extraction of four hub modules from the PPI network (Fig. 4B). Functional enrichment analysis showed that genes in module 1 were significantly enriched into peroxisomal protein transport, peroxisomal lipid metabolism, and 7-oxo-C and 7-beta-HC pathways. Genes in module 2 were mainly involved in vesicle-mediated transport, exocytosis, post-translational protein phosphorylation, and complement and coagulation cascades. However, module 3 genes were closely associated with endocytosis, and module 4 genes participated in lipid synthesis and metabolism (Fig. 4C).

Figure 4.Network of the analysis of the co-expressed genes.A) Top ranking hits after an enrichment analysis (Metascape) against biological functions. B) PPI network of SRC co-expressed genes and the module as identified by MCODE. C) Functional enrichment analysis for the modules. The correlations between macrophage infiltration and 4 co-expression gene with SRC in diverse cancer types using the D) TIMER and E) CIBERSORT algorithm. The correlations between M1, M2 macrophage and four co-expression genes with SRC in diverse cancer types. F) LIHC, G) TGCT, H) THCA, and I) THYM. NOS2, IRF5, and PTGS2 are the marker genes for M1 macrophages. CS163, VSIG4, and MS4A4A are the marker genes for M1 macrophages.

4.4. SRC May Link Lipid Metabolism and Macrophage Polarisation in Cancer

Functional enrichment studies of SRC and co-expressed genes indicated that lipid metabolism has an important role in LIHC, TGCT, THCA, and THYM. Considering the relationship between SRC and macrophages, we calculated the correlations between macrophages and SRC co-expressed genes (Nudix Hydrolase 19 (NUDT19), Protein Tyrosine Phosphatase Receptor Type J (PTPRJ), DAB Adaptor Protein 2 (DAB2), and CDP-Diacylglycerol Synthase 1 (CDS1)) involved in lipid metabolism.

We observed that the expressions of NUDT19, PTPRJ, DAB2 and CDS1 were significantly correlated with the infiltration of macrophages (Fig. 4D), i.e. CDS1-macrophage in THCA (cor = 0.385), DAB2-macrophage in THCA (cor = 0.594), NUDT19-macrophage in TGCT (cor = 0.321) and PTPRJ-macrophage in THCA (cor = 0.555) using TIMER. Similarly, CIBERSORT algorithm revealed that the expressions of NUDT19, PTPRJ, DAB2 and CDS1 has moderate correlations with macrophage infiltration in LIHC, TGCT, THCA or THYM (Fig. 4E), such as CDS1-macrophage in THYM (cor = 0.4731), DAB2-macrophage in TGCT (cor = 0.6142), NUDT19-macrophage in THYM (cor = 0.4717) and PTPRJ-macrophage in THYM (cor = 0.4751). Further correlation analysis demonstrated that these four SRC- and lipid-metabolism-related genes had a close relationship with M1 or M2 polarisation in LIHC, TGCT, THCA, and THYM (Fig. 4F-4I).

5. Discussion

Usually, the SRC is predominantly inactive in the cells. The activated SRC kinase is activated by phosphorylation tyrosine residues of the corresponding target protein, thus activating corresponding signaling pathways, including MAPK, STAT, PI3K / AKT and EGFR.

These pathways are mostly related to metastasis and the proliferation of tumour cells. This study therefore firstly evaluated the prognosis in various cancers and for cancer patients. There was a significant difference in the OS of the SRC expression group among patients in 11 types of cancers. Under the conception of basket trials, it is hypothesized that the common role in the prognosis of SRC in multiple cancers might imply a common immune infiltration. Subsequently, the correlation analysis of immune infiltration found that the expression of the SRC gene is related to M1 macrophages in four cancers (LIHC, TGCT, THCA, THYM). Following this statement, these four cancer groups that strongly correlated with M1 macrophages were taken further for correlation studies with SRC co-expressed genes to understand the response mechanisms of biological signals in special physiological states such as cancers. The results indicate that genes co-expressed with the SRC gene may promote tumour growth by regulating lipid metabolism.

It seemed a consensus that upregulated expression of the SRC is a risk for the patients’ survival ( 2 , 17 , 18 ). In contrast, our results have suggested that higher SRC expression was a good prognosis indicator in BLCA, STAD, and THCA, which conflicts with the previous studies ( 19 - 21 ). To further explore the possible reason for the prognosis, we observed that SRC was positively associated with macrophages, especially M1 macrophages in LIHC, TGCT, THCA, and THYM. Our results were consistent with earlier reports that the SRC gene is involved massively in macrophages’ activation ( 22 ). SRC gene and its elevated levels have been reported in LIHC ( 2 ), TGCT ( 18 , 23 ), THYM ( 24 ), and THCA ( 25 ). The elevated SRC expression has been associated with the poor prognosis of LIHC patients ( 26 ). Upregulation of SRC or c-SRC is linked to the proliferation, migration, and invasion of TGCT cells ( 2 , 18 ). For patients with THYM, the higher SRC gene is reported as a risk for survival ( 24 , 27 ). Generally, TAMs are, referred to as macrophages in the solid TME, appear to be an essential bridge between inflammation and tumorigenesis, which is SRC kinase-dependent ( 28 ). The inflammation is supposed to drive the elevation of the SRC expression and other family members. The studies have demonstrated that SRC upregulates the production of multiple cytokines, such as IL-6, required for inflammation and cancer progression in inflammatory macrophages, especially the TAMs ( 28 ). This promoting effect of SRC on cytokines could aggregate malignant cells, resulting in tumor size growth. The increased level of SRC in macrophages might be seen as a biomarker for tumors growth (LIHC, TGCT, and THYM), and high tumor specificity for the SRC gene in macrophages would be effective for these cancers’ diagnosis and treatment ( 2 ). In contrast, our results showed that THCA (Fig. 1C) is associated with good prognosis as compared to previous reports indicating higher expression of SRC gene association with poor OS ( 20 ). The clinical data from The Cancer Genome Atlas (TCGA) showed that most patients had been treated with pharmaceuticals and radiation, which might have changed the immune system and even TME ( 29 ). Thus, profiles of the SRC gene and the cells in the TME are changed subsequently. Since the SRC gene in THCA suggests a poor prognosis with RFS, we intend to believe SRC was a risk indicator for the patients with THCA. In TIMER2.0 we have found that, CD4⁺ T cells and Dendritic Cells (DCs) are also positively correlated with levels of SRC expression in LIHC, TGCT, THCA and negatively correlated with it in THYM. The T-cells that express the CD4 antigen are also known as CD4⁺ T-cells. It plays an important role in determining how immune responses develop ( 30 ). SRC gene is reported to asscociated with CD4⁺ T cells ( 31 ). DC as an antigen-presenting cell, is capable of inducing naive T cell activation and effector differentiation. As SRC family is involved in dendritic cell responses, it is deduced that SRC gene might participate in DCs’ activities ( 32 , 33 ).

Next, we identified the co-expressed genes with SRC and found that these genes are mainly involved in metabolisms such as lipid metabolic process, lipid biosynthesis process, and lipid metabolism, in LIHC, TGCT, THCA, and THYM. Cancer cells are always linked to high demands for energy and material basis for rapid tumor growth. Metabolic reprogramming underlies macrophage activation, and TAMs often undergo metabolic reprogramming to ensure their survival and malignant tumor progression ( 34 ). It has been reported that purine metabolism is deregulated in liver cancer ( 35 ). Aberrant metabolism of steroids and Vitamin D is suggested to be involved in the progression of TGCT ( 36 ).

Similarly, enhanced metabolism of fatty acid and rapid aerobic glycolysis help in the survival of THCA cells ( 37 ). Moreover, it has been reported that intervention strategies that changed the metabolism of thymoma patients led to the increase in the active life of patients by three years, which is way longer than the similarly affected patients ( 26 ). Hence, there is consideration to studying altered metabolism to treat these cancers.

Subsequently, we constructed a protein interaction network and extracted key modules. We revealed that genes in key modules are closely related to lipid metabolism. Metabolic reprogramming resulting in alterations in lipid production has been reported in TGCT ( 38 ), THYM ( 39 ), and THCA ( 40 ). De novo lipogenesis re-activated or enhanced by cancer cells has been widely reported to resolve the lack of lipid resources ( 40 - 42 ). The dysregulation or alterations of lipid metabolism affects the growth of primary tumors and mediates tumor progression and metastasis in cancer ( 42 ). Therefore, targeting metabolic pathways has been considered as novel therapy in cancer.

By extracting the NUDT19, PTPRJ, DAB2, and CDS1 genes that co-expressed with SRC involved in lipid metabolism, we observed that they are closely related to the polarization of M1 and M2 macrophages. So far, the interactions among these genes are rarely reported. However, some functions of the four genes in lipid metabolism are found. SRC phosphorylates Lipin 1, catalyzing the conversion of fatty acids taken up by cells into glycerolipids, especially phospholipids. Studies have shown that phosphorylation of Lipin 1 could accelerate cancer cell growth and promote tumor progression and metastasis in mouse models. The phosphorylation level of Lipin 1 was significantly increased in clinical breast cancer samples and has been shown to be closely related to the tumor size, lymph node metastasis, tumor recurrence time, and patient survival ( 43 ). PTPRJ is suggested to positively regulate SRC and drive oncogenic signaling ( 44 ). In addition, DAB2 has been shown to regulate the MAPK pathway and SRC activity ( 45 ). As NUDT19, PTPRJ, and CDS1 are co-expressed with SRC, the involvement of them in lipid metabolism by regulating SRC activity via the aforementioned pathways are thus inferred.

The interaction between lipid metabolism and macro-phages is crucial for tumor cell growth. DAB2 has premetastatic activity and are highly expressed in tumor-infiltrating TAMs ( 46 ) for the premetastatic activity of TAMs. Higher expression of DAB2 in TAMs in patients with cancer correlates with a worse prognosis. It is then inferred that DAB2 might promote M2 polarization. It has been found that enhanced fatty acid oxidation capacity of TAMs in LIHC promotes the activation of M2 macrophages ( 47 ). The researchers also found that hepatocellular carcinoma-derived TAMs promoted IL-1β secretion by generating reactive oxygen species (ROS), thereby supporting the invasive ability of hepatoma cells in vitro and blocking fatty acid oxidation which could attenuate the effect of TAMs ( 48 ).

In thyroid cancer, TAM inhibits the synthesis of sphingomyelin and phosphatidylethanolamine and ROS production, thus attenuating the tumor-promoting activity of TAMs ( 42 ). Similarly, in THCA, TAMs are found with the increased synthesis of sphingomyelin and phosphatidylethanolamine along with increased M2-like macrophage phenotype markers and ROS expression, which contribute to the maintenance of genomic instability ( 41 ). There are few reports on the reprogramming of phospholipid metabolism of TAMs in TGCT and THYM, but the interaction between lipid metabolism and activation of TAMs is still a research hotspot for researchers.

The growth of the tumor requires a large volume of nutrients that results in nutrient-poor TME. The TME that is glucose-deficient has impaired T-cell function and anti-tumor immune responses. Thus, tumors evade immune surveillance. The nutrient-poor TME could result in DAB2-mediated (possibly other co-expressed genes NUDT19, PTPRJ, and CDS1) upregulation of SRC and, subsequently, metabolic reprogramming. Through reprogramming, TAMs avoid participating in nutrient “scramble” in the nutrient-poor microenvironment on the one hand and “passively” changes the functional phenotype and polarize TAMs towards the M2 type with pro-tumor effects on the other hand.

We demonstrated that the studies on SRC gene’s association with macrophages polarization and lipid metabolism and the targeting of metabolism might be a treatment strategy for the pan-cancer. The results deduced by our study need further investigation and validation based on the experiments in molecular biology. Moreover, our primary focus was on a single gene (SRC) and we failed to find the key regulators of SRC upregulation in pan-cancers. Future studies in pan-cancer should examine the SRC gene profiles first in vivo that should be integrated with epigenetic analysis and include clinical cases.

6. Conclusion

A series of online database, including TIMER, CIBERSORT and Cytoscape software and its plug-in MCODE were applied to construct in silico work for SRC expression in Pan-cancer. The analysis decipher that SRC had a different prognostic role in pan-cancer and is related to macrophages infiltration and involved in lipid metabolism. In conclusion, our study has reported that SRC is associated with prognosis in multiple cancer types and may affect macrophage polarization through lipid metabolism. It has been established that different tumors have different molecular types, and there are varied immune cell infiltrations in the TME. The malignant phenotype of tumor cells is relatively uniform and the enrichment degree of various immune cells often determines the characteristics of TME, which further affects the response to targeted immunotherapy. Thus, our results indicate that cancer treatment targeting metabolism, such as reducing metabolism of fatty acid might be a novel direction for LIHC, TGCT, THCA, and THYM therapy, with the idea of basket trials.

Acknowledgements

We acknowledge TCGA and GEO database for provid-ing their platforms and contributors for uploading their meaningful datasets.

References

1. Chong Y, Dai X, Fang G, Wu R, Zhao L, Ma X, et al. Palladium concave nanocrystals with high-index facets accelerate ascorbate oxidation in cancer treatment. Nature Communicat. 2018; 9(1):4861. DOI
2. Wang T, Jin H, Hu J, Li X, Ruan H, Xu H, et al. COL4A1 promotes the growth and metastasis of hepatocellular carcinoma cells by activating FAK-Src signaling. J Exp Clin Cancer Res. 2020; 39(1):148. DOI
3. Solomon VR, Lee H. Quinoline as a privileged scaffold in cancer drug discovery. Curr Med Chem. 2011; 18(10):1488-1508. DOI
4. Oyinlade O, Wei S, Kammers K, Liu S, Wang S, Ma D, et al. Analysis of KLF4 regulated genes in cancer cells reveals a role of DNA methylation in promoter- enhancer interactions. Epigenetics. 2018; 13(7):751-768. DOI
5. Martellucci S, Clementi L, Sabetta S, Mattei V, Botta L, Angelucci A. Src Family Kinases as Therapeutic Targets in Advanced Solid Tumors: What We Have Learned so Far. Cancers (Basel). 2020; 12(6)DOI
6. Schenone S, Brullo C, Musumeci F, Botta M. Novel dual Src/Abl inhibitors for hematologic and solid malignancies. Expert Opin Investig Drugs. 2010; 19(8):931-945. DOI
7. Wang JJ, Lei KF, Han F. Tumor microenvironment: recent advances in various cancer treatments. Eur Rev Med Pharmacol Sci. 2018; 22(12):3855-3864. DOI
8. Turley SJ, Cremasco V, Astarita JL. Immunological hallmarks of stromal cells in the tumour microenvironment. Nat Rev Immunol. 2015; 15(11):669-682. DOI
9. Beer TM, Armstrong AJ, Rathkopf DE, Loriot Y, Sternberg CN, Higano CS, et al. Enzalutamide in metastatic prostate cancer before chemotherapy. N Engl J Med. 2014; 371(5):424-433. DOI
10. Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D, et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med. 2015; 21(8):938-945. DOI
11. Zhang Y, Han X, Nie G. Responsive and activable nanomedicines for remodeling the tumor microenvironment. Nature Protocols. 2021; 16(1):405-430. DOI
12. Carroll MJ, Parent CR, Page D, Kreeger PK. Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting. BMC Cancer. 2019; 19(1):1025. DOI
13. Lánczky A, Győrffy B. Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J Med Internet Res. 2021; 23(7):e27633. DOI
14. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017; 77(21):e108-e110. DOI
15. Vasaikar SV, Straub P, Wang J, Zhang B. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res. 2018; 46(D1):D956-D963. DOI
16. Liao X, Bu Y, Xu Z, Jia F, Chang F, Liang J, et al. WISP1 Predicts Clinical Prognosis and Is Associated With Tumor Purity, Immunocyte Infiltration, and Macrophage M2 Polarization in Pan-Cancer. Front Genet. 2020; 11:502. DOI
17. Irby RB, Yeatman TJ. Role of Src expression and activation in human cancer. Oncogene. 2000; 19(49):5636-5642. DOI
18. Leonetti E, Gesualdi L, Scheri KC, Dinicola S, Fattore L, Masiello MG, et al. c-Src Recruitment is Involved in c-MET-Mediated Malignant Behaviour of NT2D1 Non-Seminoma Cells. Int J Mol Sci. 2019; 20(2):320. DOI
19. Qayyum T, Fyffe G, Duncan M, McArdle PA, Hilmy M, Orange C, et al. The interrelationships between Src, Cav-1 and RhoGD12 in transitional cell carcinoma of the bladder. Br J Cancer. 2012; 106(6):1187-1195. DOI
20. Beadnell TC, Nassar KW, Rose MM, Clark EG, Danysh BP, Hofmann MC, et al. Src-mediated regulation of the PI3K pathway in advanced papillary and anaplastic thyroid cancer. Oncogenesis. 2018; 7(2):23. DOI
21. Yang Y, Bai ZG, Yin J, Wu GC, Zhang ZT. Role of c-Src activity in the regulation of gastric cancer cell migration. Oncol Rep. 2014; 32(1):45-49. DOI
22. Byeon SE, Yi Y-S, Oh J, Yoo BC, Hong S, Cho JY. The role of Src kinase in macrophage-mediated inflammatory responses. Mediators Inflamm. 2012; 2012:512926. DOI
23. Rosas-Plaza X, de Vries G, Meersma GJ, Suurmeijer AJ, Gietema JA, van Vugt MA, et al. Dual mTORC1/2 inhibition sensitizes testicular cancer models to cisplatin treatment. Molecular Cancer Therapeutics. 2020; 19(2):590-601.
24. Chuah C, Lim TH, Lim AST, Tien SL, Lim CH, Soong R, et al. Dasatinib Induces a Response in Malignant Thymoma. J Clin Oncolog. 2006; 24(34):e56-e58. DOI
25. Kim WG, Guigon CJ, Fozzatti L, Park JW, Lu C, Willingham MC, et al. SKI-606, an Src inhibitor, reduces tumor growth, invasion, and distant metastasis in a mouse model of thyroid cancer. Clin Cancer Res. 2012; 18(5):1281-1290. DOI
26. Walker S, Wankell M, Ho V, White R, Deo N, Devine C, et al. Targeting mTOR and Src restricts hepatocellular carcinoma growth in a novel murine liver cancer model. PLoS One. 2019; 14(2):e0212860. DOI
27. Chen Y, Gharwan H, Thomas A. Novel Biologic Therapies for Thymic Epithelial Tumors. Frontiers in Oncology. 2014; 4DOI
28. Liu ST, Pham H, Pandol SJ, Ptasznik A. Src as the link between inflammation and cancer. Frontiers in physiol. 2014; 4:416. DOI
29. Zemek RM, Chin WL, Nowak AK, Millward MJ, Lake RA, Lesterhuis WJ. Sensitizing the Tumor Microenvironment to Immune Checkpoint Therapy. Frontiers in Immunol. 2020; 11DOI
30. Joshi RN, Fernandes SJ, Shang M-M, Kiani NA, Gomez-Cabrero D, Tegnér J, et al. Phosphatase inhibitor PPP1R11 modulates resistance of human T cells toward Treg-mediated suppression of cytokine expression. J Leukoc Biol. 2019; 106(2):413-430. DOI
31. Wu X, Shao F, Yang Y, Gu L, Zheng W, Wu X, et al. Epigallocatechin-3-gallate sensitizes IFN-γ-stimulated CD4+ T cells to apoptosis via alternative activation of STAT1. Int Immunopharmacol. 2014; 23(2):434-441. DOI
32. Dallari S, Macal M, Loureiro ME, Jo Y, Swanson L, Hesser C, et al. Src family kinases Fyn and Lyn are constitutively activated and mediate plasmacytoid dendritic cell responses. Nature communicat. 2017; 8:14830. DOI
33. Yi Z, Li L, Matsushima GK, Earp HS, Wang B, Tisch R. A novel role for c-Src and STAT3 in apoptotic cell-mediated MerTK-dependent immunoregulation of dendritic cells. Blood. 2009; 114(15):3191-3198. DOI
34. Tanaka K, Sasayama T, Nagashima H, Irino Y, Takahashi M, Izumi Y, et al. Glioma cells require one-carbon metabolism to survive glutamine starvation. Acta Neuropathol Commun. 2021; 9(1):16. DOI
35. Su W-J, Lu P-Z, Wu Y, Kalpana K, Yang C-K, Lu G-D. Identification of Key Genes in Purine Metabolism as Prognostic Biomarker for Hepatocellular Carcinoma. Frontiers in Oncology. 2021; 10:583053. DOI
36. Blomberg Jensen M, Jørgensen A, Nielsen JE, Steinmeyer A, Leffers H, Juul A, et al. Vitamin D metabolism and effects on pluripotency genes and cell differentiation in testicular germ cell tumors in vitro and in vivo. Neoplasia (New York, NY). 2012; 14(10):952-963. DOI
37. Lu J, Zhang Y, Sun M, Ding C, Zhang L, Kong Y, et al. Multi-Omics Analysis of Fatty Acid Metabolism in Thyroid Carcinoma. Frontiers in Oncolog. 2021; 11:737127. DOI
38. Batool A, Karimi N, Wu X-N, Chen S-R, Liu Y-X. Testicular germ cell tumor: a comprehensive review. Cell Mol Life Sci. 2019; 76(9):1713-1727. DOI
39. Phillips MCL, Murtagh DKJ, Sinha SK, Moon BG. Managing Metastatic Thymoma With Metabolic and Medical Therapy: A Case Report. Frontiers in Oncolog. 2020; 10DOI
40. von Roemeling CA, Copland JA. Targeting lipid metabolism for the treatment of anaplastic thyroid carcinoma. Expert Opin Ther Targets. 2016; 20(2):159-166. DOI
41. Rabold K, Aschenbrenner A, Thiele C, Boahen CK, Schiltmans A, Smit JWA, et al. Enhanced lipid biosynthesis in human tumor-induced macrophages contributes to their protumoral characteristics. J Immunother Cancer. 2020; 8(2)DOI
42. Liu B, Bai C. Regulatory Mechanisms of Coicis Semen on Bionetwork of Liver Cancer Based on Network Pharmacology. Biomed Res Int. 2020; 2020:5860704. DOI
43. Song L, Liu Z, Hu HH, Yang Y, Li TY, Lin ZZ, et al. Proto-oncogene Src links lipogenesis via lipin-1 to breast cancer malignancy. Nat Commun. 2020; 11(1):5842. DOI
44. Chabot C, Spring K, Gratton J-P, Elchebly M, Royal I. New role for the protein tyrosine phosphatase DEP-1 in Akt activation and endothelial cell survival. Mol Cell Biol. 2009; 29(1):241-253. DOI
45. Reddy SS, Connor TE, Weeber EJ, Rebeck W. Similarities and differences in structure, expression, and functions of VLDLR and ApoER2. Mol Neurodegener. 2011; 6:30. DOI
46. Marigo I, Trovato R, Hofer F, Ingangi V, Desantis G, Leone K, et al. Disabled Homolog 2 Controls Prometastatic Activity of Tumor-Associated Macrophages. Cancer Discov. 2020; 10(11):1758-1773. DOI
47. Wu L, Zhang X, Zheng L, Zhao H, Yan G, Zhang Q, et al. RIPK3 Orchestrates Fatty Acid Metabolism in Tumor-Associated Macrophages and Hepatocarcinogenesis. Cancer Immunol Res. 2020; 8(5):710-721. DOI
48. Zhang Q, Wang H, Mao C, Sun M, Dominah G, Chen L, et al. Fatty acid oxidation contributes to IL-1β secretion in M2 macrophages and promotes macrophage-mediated tumor cell migration. Mol Immunol. 2018; 94:27-35. DOI