The Multi‑staged Gene Expression Profling Reveals Potential Genes And The Critical Pathways in Kidney Cancer

Abstract

Cancer is among the highly complex disease and renal cell carcinoma is the sixth-leading cause of cancer death. To understand complex diseases such as cancer, diabetes, and kidney diseases, high-throughput data are generated at a large scale and it has helped in the research and diagnostic advancement. However, unraveling meaningful information from such large datasets for the comprehensive and minute understanding of cell phenotypes and disease pathophysiology remains a trivial challenge, and also the molecular events leading to disease onset and progression are not well understood. With this goal, we have collected gene expression datasets from publicly available datasets which are for two different stages (I and II) for renal cell carcinoma the TCGA and cBioPortal databases have been utilized for clinical relevance understanding. In this work, we have applied a computational approach to unravel the differentially expressed genes, and their networks for the enriched pathways. Based on our results, we conclude that among the most dominantly altered pathways for renal cell carcinoma, are PI3K-Akt, Foxo, endocytosis, MAPK, Tight junction, cytokine cytokine receptor interaction pathways and the major source of alteration for these pathways are MAP3K13, CHAF1A, FDX1, ARHGAP26, ITGBL1, C10orf118, MTO1, LAMP2, STAMBP, DLC1, NSMAF, YY1, TPGS2, SCARB2, PRSS23, SYNJ1, CNPPD1, PPP2R5E. In terms of clinical significance, there are a large number of differentially expressed genes that appears to be playing critical roles in survival.

Click here to know Herba Cistanche and the Cistanche benefits

Introduction

Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults, responsible for approximately 90–95% of cases and it is one of the leading causes of cancer death. Its occurrence shows mainly male predominance over women with a ratio of 1.5:1. RCC, a kidney cancer originates in the lining of the proximal convoluted tubule which is the part of the very small tubes in the kidney and transports primary urine1,2. High-throughput data is created at a large scale to understand complex diseases like cancer, and it has aided in research and diagnostic advancement3–6. However, extracting useful knowledge from such vast datasets for a complete and detailed understanding of cell phenotypes and disease pathophysiology remains a difficult task, and the molecular events that contribute to disease initiation and progression are still poorly understood7–9. The advancement of the post-genomics period has resulted in a huge amount of "big data" in biological sciences, which has led to a multitude of interdisciplinary applications in recent decades5,10. Several biological databases house various types of datasets. TCGA, oncoming, nephrosis, and GEO (gene expression omnibus) are the most widely used databases in biological sciences11. These databases mainly GEO store vast amounts of datasets related to cancer, diabetes, and other biological problems8,12–16.

The identification of pathogenetically distinct tumor types poses a significant challenge in the treatment of complex diseases (especially cancer)17–19. The improvement in tumor classification always helps in the improvement of therapeutic approaches20,21. In target-specific therapy, effectiveness can be maximized while toxicity is reduced by using enhanced classification. To access biological datasets from these databases previously, a variety of tools/approaches were used. For molecular classification of cancer Golub TR et al., 22 have divided cancer classification into two challenges class discovery and class prediction.

Several oncogenes and tumor suppressor genes that are changed in RCC, resulting in pathway dysregulation, need to be identified and investigated further23–25. Copy number, gene sequencing, expression pattern, and methylation in primary RCC are all possible avenues for achieving this goal. With continued breakthroughs in omics technology, the application of molecular markers for early diagnosis and prognosis deserves further attention1,2,26–30.

We have selected the RCC dataset with samples from two stages (stages I and II) to understand how gene expression patterns vary and how altered gene expression patterns lead to possible changes in the respective inferred functions as tumor stage I to II changes and from Affymetrix platforms (U133A to U133B). Different cancer stages help in describing where cancer could be located, how far it has spread, and whether it is affecting other parts of the body31–33. Healthy tissue usually contains many different types of cells grouped. If cancer looks similar to healthy tissue and contains different cell groupings, it is called a differentiated or low-grade tumor and when the cancerous tissue looks very different from the healthy tissue, it is termed a poorly differentiated or high-grade tumor. Cancer’s grade may help the clinician to predict how quickly cancer will spread. In general, the lower the tumor’s grade, the better the prognosis. Different types of cancer have different methods for assigning cancer grades 7,34–37. In general, it is very hard to detect most cancers at an early stage so the main focus was on exploring the gene expression pattern alterations and their functional consequences further to avoid biasedness, we have incorporated the TCGA dataset also which has the samples from all the grades.

Here, we have selected a dataset from gene expression omnibus (GEO) where the samples are from humans with two tumor stages (I and II). We have organized the samples in the order such as stage I normal versus tumor and stage II normal versus tumor for the Affymetrix platforms U133A and U133B and analyzed the tumor samples concerning their respective controls (normal sample of the same stage) for the gene expression alterations and evolved functions with the increase in tumor percentage. Based on our work, we conclude that irrespective of the tumor stage PI3K-Akt, Foxo, endocytosis, MAPK, Tight junction, cytokine-cytokine receptor interaction pathways, and the major source of alteration for these pathways are MAP3K13, CHAF1A, FDX1, ARHGAP26, ITGBL1, C10orf118, MTO1, LAMP2, STAMBP, DLC1, NSMAF, YY1, TPGS2, SCARB2, PRSS23, SYNJ1, CNPPD1, PPP2R5E. In addition, we have also studied the clinical significance and observed that there is a large number of differentially expressed genes that appears to be playing critical roles in survival such as ARHGAP6, TGM4, CD248, SLC13A3, EPO, PARD6A, CLCA2, UBE2S, ERAL1, FGFR1, MRVI1, DYNC1I2, CDCA7.

Cistanche supplement

Discussion

Renal cell carcinoma is one of the most common cancers, and it is one of the leading causes of cancer death14,15,41. In terms of therapy and diagnosis, therapeutic and clinical outcomes differ between the individuals with even close similarity in clinical and pathological characteristics (tumor type, grades, and stages) and despite tremendous efforts to identify molecular biomarkers (prognostic and predictive) and with improved precision compared to clinical and pathological predictors only a few molecular tests have been introduced into oncological practice29. So it is important to understand and unravel different levels (such as gene expression pattern, epigenetics, protein expression) of diversities in cancer42,43. We gathered the previously published dataset for this purpose and conducted a detailed and precise study ranging from gene expression profiling to functional changes, including networks mapped from the human protein network database.

Our work leads to the conclusion that irrespective of the tumor stage PI3K-Akt, Foxo, endocytosis, MAPK, Tight junction, cytokine-cytokine receptor interaction pathways, and the major source of alteration for these pathways are MAP3K13, CHAF1A, FDX1, ARHGAP26, ITGBL1, C10orf118, MTO1, LAMP2, STAMBP, DLC1, NSMAF, YY1, TPGS2, SCARB2, PRSS23, SYNJ1, CNPPD1, PPP2R5E. Networks of DEGs for the enriched pathways show that there is a large number of genes from a few specific pathways that are altered such as Ras signaling pathways, immune systems, Wnt, hippo, and Akt pathways Here, we observe that critical pathways altered in RCC are wnt, hippo, regulation of actin cytoskeleton, ECM, infection and inflammation, metabolic, and more cancer-related pathways. From the mapped network, we observe that the highly connected genes infer the potential pathways, or in other words the top-ranked genes based on connectivity refer to those pathways which are directly or indirectly associated either with RCC or other types of cancer.

In terms of clinical significance, we looked at the rate of mutations for the top-ranked genes (based on fold change) and patients’ survival for changes in gene expression, with Kaplan–Meier plots indicating clinical significance. We conclude that a large number of differentially expressed genes tend to be potentially important in terms of survival, with ARHGAP6, TGM4, CD248, SLC13A3, EPO, PARD6A, CLCA2, UBE2S, ERAL1, FGFR1, MRVI1, DYNC1I2, CDCA7 among the genes chosen. Using the publicly available datasets, we have investigated gene expression profiling for renal cell carcinoma. The previous work has focused on selected genes and pathways. Here, we have investigated the list of critical pathways and the genes which appear to be clinically highly significant in the case of renal cell carcinoma. These clinically significant genes lead to potential alteration in PI3K-Akt, fox, endocytosis, MAPK, tight junction, and cytokine-cytokine receptor interaction pathways. Our work will help in diagnosing renal cell carcinoma patients because here, we have presented the differentially expressed genes, their inferred pathways, and the clinical impact of the selective genes. Since our finding is from an overall perspective including clinical relevance so this study will help in the future for diagnostics also.

This work also appears to be more unique in comparison to the previous study we potentially explored grades I and II of RCC and further explored the clinical relevance. Healthy tissue usually contains many different types of cells grouped together and if cancer looks similar to healthy tissue and contains different cell groupings, it is called a differentiated or low-grade tumor when the cancerous tissue looks very different from the healthy tissue, it is termed as a poorly differentiated or high-grade tumor. Cancer’s grade may help the clinician to predict how quickly cancer will spread. In general, the lower the tumor’s grade, the better the prognosis. Different types of cancer have different methods to assign cancer grades 7,34–37 and the different tumor stages could help in describing the severity, tumor propagation speed, and its impact on the other organs31–33. In general, it is very hard to detect most of the cancers at an early stage so the main focus was on exploring the gene expression pattern alterations and its functional consequences further to avoid biasedness, we have incorporated the TCGA dataset also which has the samples from all the grades. Further, we have also investigated the expression of these clinically relevant genes by using protein atlas 44–48. We observe that most of these genes are expressed in the case of RCC and act as biomarkers and only TGM4 and GGN were not expressed. This study will be an important step in the understanding of early-stage tumor propagation and also will be helpful for the clinical aspects.

Citsanche tubulosa

Methods

Here, the GSE6344 dataset was used for the study which contains the samples of stage I and II of gene expression for tumor kidney cancer30,38. In the first step, we selected the raw expression dataset GSE6344 and processed it until normalization and log2 values of all mapped genes were achieved, as shown in Fig. 1a of the workflow. These 40 samples in this dataset were 5 normal and 5 tumor for two stages I and II from U133A and U133B platforms. We have compared the tumor samples with standard samples of the respective stages and platforms for differential gene expression analysis, yielding four DEGs lists.

In short, the basic steps involved in the entire study are raw file processing, intensity calculation, and normalization. For normalization49–51, GCRMA52–56, RMA, and EB are the most commonly used approaches. Here, we have used EB for raw intensity normalization. After normalization, we proceed to our goal which is to understand the gene expression patterns14,57 and their inferred functions57,58.

To prepare the list of DEGs and analysis, we have our in-built codes. The samples were placed into two groups such as COVID-19 positive and negative and then normal and tumor samples. The selection criteria were placed by the fold change and p-values which have been calculated and for the selection of genes as differentially expressed the threshold of fold changes and p-values applied were± 2 and 0.05, respectively and then KEGG database59–61 were used for pathway analysis and for which there is our code designed62. In summary, for differential gene expression prediction and statistical analysis, MATLAB2017 functions (e.g., most matte) were applied, and further for pathway analysis, we used KEGG61 database62–65.

For generating the DEGs network, FunCoup2.066 has been used for all the networks throughout the work, and cytoscape67 has been used for network visualization. For most of our coding and calculations, MATLAB has been used 62–65. Furthermore, the FunCoup2.066 database and Cytoscape and its applications68 were used for network visualization to understand the network and the connectivity of the genes within the network of DEGs69,70. The basic concept of the FunCoup network database is that it predicts four different classes of functional coupling associations such as protein complexes, protein–protein physical interactions, metabolic, and signaling pathways66. MATLAB 2017b codes and the command line codes have been used for future plotting and during analysis. For the network level analysis such as the number of connectivity per gene and the genes belonging to different numbers of pathways, the codes have been written in MATLAB and finally, it has been plotted also by the codes written in MATLAB64,65. For Venn diagram plotting, a freely available web server was used 72–74.

Cistanche capsules

Conclusions

Based on our findings, we conclude that PI3K-Akt, Foxo, endocytosis, MAPK, Tight junction, and cytokine receptor interaction pathways are among the most commonly altered pathways in renal cell carcinoma and that MAP3K13, CHAF1A, FDX1, ARHGAP26, ITGBL1, C10orf118, MTO1, LAMP2, STAMBP, DLC1, NSMAF, YY1, TPGS2, SCARB2, PRSS23, SYNJ1, CNPPD1, and PPP2R5E are the major sources of alteration for these pathways. Wnt, hippo, actin cytoskeleton control, ECM, infection and inflammation, metabolic, and other cancer-related pathways are among the most important pathways altered in RCC. ARHGAP6, TGM4, CD248, SLC13A3, EPO, PARD6A, CLCA2, UBE2S, ERAL1, FGFR1, MRVI1, DYNC1I2, CDCA7 are some of the genes that were chosen after survival study.

References

1. Cairns, P. Renal cell carcinoma. Cancer Biomark 9, 461–473 (2010).

2. Hsieh, J. J. et al. Renal cell carcinoma. Nat. Rev. Dis. Primers. 3, 1–19 (2017).

3. Swanton, C. Cancer evolution: The final frontier of precision medicine. Ann. Oncol. 25, 549–551 (2014).

4. Hiley, C., de Bruin, E. C., McGranahan, N. & Swanton, C. Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine. Genome Biol. 15, 453 (2014).

5. Werner, H. M. J., Mills, G. B. & Ram, P. T. Cancer systems biology: a peek into the future of patient care. Nat. Rev. Clin. Oncol 11, 167–176 (2014).

6. Wang, E. Understanding genomic alterations in cancer genomes using an integrative network approach. Cancer Lett. 340, 261–269 (2013).

7. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: Te next generation. Cell 144, 646–674 (2011).

8. Wang, Y. et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671–679 (2005).

9. Gaulton, K. J. et al. Genetic fine mapping and genomic annotation. Nat. Genet. 47, 1415–1425 (2015).

10. Hornberg, J. J., Bruggeman, F. J., Westerhof, H. V. & Lankelma, J. Cancer: A systems biology disease. Biosystems 83, 81–90 (2006).

11. Yuan, Y. et al. AssessingClinicalUtilityCancerGenomicProteomicDatTumorTypes2014NatBiotech. Nat. Biotechnol. 1–11 (2014). https://doi.org/10.1038/nbt.2940

12. Li, B. & Li, J. Z. A general framework for analyzing tumor subclonal using SNP array and DNA sequencing data. Genome Biol. 15, 473 (2014). https://doi.org/10.1186/s13059-014-0473-4

13. Rybak, A. P., Bristow, R. G. & Kapoor, A. Prostate cancer stem cells: deciphering the origins and pathways involved in prostate tumorigenesis and aggression. Oncotarget 6, 1900–1919 (2015).

14. Lapointe, J. et al. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc. Natl. Acad. Sci. U.S.A. 101, 811–816 (2004).

15. Roth, R. B. et al. Gene expression analyses reveal molecular relationships among 20 regions of the human CNS. Neurogenetics 7, 67–80 (2006).

16. Ko, J.-H. et al. Expression profiling of ion channel genes predicts clinical outcomes in breast cancer. Mol. Cancer 12, 106 (2013).

17. Aparicio, S. & Mardis, E. Tumor heterogeneity: next-generation sequencing enhances the view from the pathologist’s microscope. Genome Biol. 15, 463 (2014). https://doi.org/10.1186/s13059-014-0463-6

18. Navin, N. E. Tumor evolution in response to chemotherapy: Phenotype versus genotype. Cell Rep. 6, 417–419 (2014).

19. Strandmann, von, E. P., Reinartz, S., Wager, U. & Müller, R.,. Tumor-host cell interactions in ovarian cancer: Pathways to therapy failure. Trends Cancer 3, 137–148 (2017).

20. Yap, T. A., Swanton, C. & de Bono, J. S. Personalization of prostate cancer prevention and therapy: Are clinically qualified biomarkers in the Horizon? EPMA J. 3, 3 (2012).

21. Jia, Z. et al. Diagnosis of prostate cancer using differentially expressed genes in the stroma. Can. Res. 71, 2476–2487 (2011).

22. Golub, T. R. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

23. Vogelstein, B. & Kinzler, K. W. Cancer genes and the pathways they control. Nat. Med. 10, 789–799 (2004).

24. Murai, M. & Oya, M. Renal cell carcinoma: Etiology, incidence, and epidemiology. Curr. Opin. Urol. 14, 229–233 (2004).

25. Terris, M., Klaassen, Z. & Kabaria, R. Renal cell carcinoma: Links and risks. IJNRD 45. https://doi.org/10.2147/IJNRD.S75916 (2016).

26. Tiwari, P., Kumar, L., Singh, G., Seth, A. & Tulkar, S. Renal cell cancer: Clinicopathological profile and survival outcomes. Indian J. Med. Paediatr. Oncol. 39, 23 (2018).

27. Navai, N. & Wood, C. G. Environmental and modifiable risk factors in renal cell carcinoma. Urol. Oncol. 30, 220–224 (2012).

28. Maruschke, M. et al. Expression profiling of metastatic renal cell carcinoma using gene set enrichment analysis. Int. J. Urol. 21, 46–51 (2013).

29. Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).

30. Tun, H. W. et al. Pathway signature and cellular differentiation in clear cell renal cell carcinoma. PLoS ONE 5, e10696 (2010).

31. Cheung, K. J. & Ewald, A. J. A collective route to metastasis: Seeding by tumor cell clusters. Science 352, 167–169 (2016).

32. Jackson, T., Koh, G. Y. & Zheng, X. A continuous model of angiogenesis: Initiation, extension, and maturation of new blood vessels modulated by vascular endothelial growth factor, angiopoietins, platelet-derived growth factor-B, and pericytes. DCDS-B 18, 1109–1154 (2013).

33. Reis, P. P. et al. A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence. BMC Cancer 11, 437–511 (2011).

34. Fraser, M. et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature 1–22. https://doi.org/10.1038/nature20788 (2017).

35. Penault-Llorca, F. & Radosevic-Robin, N. Biomarkers of residual disease after neoadjuvant therapy for breast cancer. Nat. Rev. Clin. Oncol. 1–17. https://doi.org/10.1038/nrclinonc.2016.1 (2016).

36. Liu, J. et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173, 400-416.e11 (2018).

37. Suzuki, H. et al. Mutational landscape and clonal architecture in grade II and III gliomas. Nat. Genet. 1–14 (2015). https://doi.org/ 10.1038/ng.3273

38. Gumz, M. L. et al. Secreted frizzled-related protein 1 loss contributes to the tumor phenotype of clear cell renal cell carcinoma. Clin. Cancer Res. 13, 4740–4749 (2007).

39. Ellrott, K. et al. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst. 6, 271-281.e7 (2018).

40. Tibodeau, B. J. et al. Characterization of clear cell renal cell carcinoma by gene expression pro. Urol. Oncol. 1–9 (2015). https:// doi.org/10.1016/j.urolonc.2015.11.001

41. Ross, D. T. et al. Systematic variation in gene expression patterns in human cancer cell lines. Nat. Genet. 24, 227–235 (2000).

42. Swanton, C. Intratumor heterogeneity: Evolution through space and time. Can. Res. 72, 4875–4882 (2012).

43. Zhang, J. et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 346, 256–259 (2014).

44. Sjöstedt, E. et al. An atlas of the protein-coding genes in the human, pig, and mouse brains. Science 367 (2020).

45. Uhlén, M. et al. A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol. Cell. Proteomics 4, 1920–1932 (2005).

46. Uhlén, M. et al. A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science 366, (2019).

47. Uhlén, M. et al. A pathology atlas of the human cancer transcriptome. Science 357, (2017).

48. Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

49. Quackenbush, J. Microarray data normalization and transformation. Nat. Genet. 32, 496–501 (2002).

50. Simon, R. Microarray-based expression profiling and informatics. Curr. Opin. Biotechnol. 19, 26–29 (2008).

51. Ideker, T., Torsson, V., Siegel, A. F. & Hood, L. E. Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data. J. Comput. Biol. 7, 805–817 (2000).

52. Reimers, M. Making informed choices about microarray data analysis. PLoS Comput. Biol. 6, e1000786 (2010).

53. Chen, K.-H. et al. Gene selection for cancer identification: A decision tree model empowered by particle swarm optimization algorithm. BMC Bioinf. 15, 1–10 (2014).

54. Bild, A. H. et al. Integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer. Breast Cancer Res. 11, R55 (2009).

55. Salomonis, N. et al. GenMAPP 2: New features and resources for pathway analysis. BMC Bioinformatics 8, 217 (2007).

56. Girke, T. Microarray analysis. 1–42 https://faculty.ucr.edu/~tgirke/HTML_Presentations/Manuals/Microarray/arrayBasics.pdf (2011).

57. Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545 (2005).

58. Mi, H., Poudel, S., Muruganujan, A., Casagrande, J. T. & Tomas, P. D. PANTHER version 10: Expanded protein families and functions, and analysis tools. Nucleic Acids Res. 44, D336–D342 (2016).

59. Kanehisa, M. et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480–D484 (2007).

60. Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M. & Hirakawa, M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38, D355–D360 (2009).

61. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2011).

62. Eldakhakhny, B. M., Sadoun, Al, H., Choudhry, H. & Mobashir, M. In-Silico Study of immune system associated genes in case of type-2 diabetes with insulin action and resistance, and/or obesity. Front. Endocrinol. 12, 1–10 (2021).

63. Warsi, M. K., Kamal, M. A., Baeshen, M. N., Izhari, M. A. & Mobashir, M. Comparative study of gene expression profiling unravels functions associated with the pathogenesis of dengue infection. Curr. Pharmaceut. Des. 26(41), 5293–5299 https://doi.org/10.2174/ 1381612826666201106093148 (2020).

64. Kamal, M. A. et al. Gene expression profiling and clinical relevance unravel the role of hypoxia and immune signaling genes and pathways in breast cancer: Role of hypoxia and immune signaling genes in breast cancer. Jima 1, (2020).

65. Krishnamoorthy, P. K. P. et al. Informatics in Medicine Unlocked. Inf. Med. Unlocked 20, 100422 (2020).

66. Alexeyenko, A. & Sonnhammer, E. L. L. Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Res. 19, 1107–1116 (2009).

67. Okawa, S., Angarica, V. E., Lemischka, I., Moore, K. & del Sol, A. A differential network analysis approach for lineage-specific prediction in stem cell subpopulations. npj Syst Biol Appl 1–8 (2015). https://doi.org/10.1038/npjsba.2015.12

68. Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

69. Mobashir, M., Schraven, B., & Beyer, T. Simulated evolution of signal transduction networks. PloS one 7(12), e50905. https://doi. org/10.1371/journal.pone.0050905 (2012).

70. Mobashir, M., Madhusudhan, T., Isermann, B., Beyer, T. & Schraven, B. Negative interactions and feedback regulations are required for transient cellular response. Sci. Rep. 4, 3718. https://doi.org/10.1038/srep03718 (2014).

71. Kanehisa, M. Te KEGG resource for deciphering the genome. Nucleic Acids Res. 32, 277D – 280 (2004).

72. Helmi, N., Alammari, D. & Mobashir, M. Role of potential COVID-19 immune system associated genes and the potential pathways linkage with type-2 diabetes. Comb. Chem. High Throughput Screen. https://doi.org/10.2174/1386207324666210804124416 (2021).

73. Bajrai, L. H. et al. Understanding the role of potential pathways and their components including hypoxia and the immune system in case of oral cancer. Sci. Rep. 11(1), 19576. https://doi.org/10.1038/s41598-021-98031-7 (2021).

74. Bajrai, L. H. et al. Gene Expression Profiling of Early Acute Febrile Stage of Dengue Infection and Its Comparative Analysis With Streptococcus pneumoniae Infection. Front. Cell. Infect. Microbiol. 11, 707905. https://doi.org/10.3389/fcimb.2021.707905 (2021).

Hamed Ishaq Khouja 1, Ibraheem MohammedAshankyty 1, Leena Hussein Bajrai 2,3, P. K. Praveen Kumar 4, Mohammad Amjad Kamal 5,6,7, Ahmad Firoz 8 & Mohammad Mobashir 9

1 Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.

2 Special Infectious Agents Unit‑BSL3, King Fahad Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.

3 Biochemistry Department, Sciences College, King Abdulaziz University, Jeddah, Saudi Arabia.

4 Department of Biotechnology, Sri Venkateswara College of Engineering, Sriperumbudur 602105, India.

5 West China School of Nursing/Institutes for Systems Genetics, Frontiers Science Center for Disease‑Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.

6 King Fahd Medical Research Center, King Abdulaziz University, P. O. Box 80216, Jeddah 21589, Saudi Arabia.

7 Enzymoics, Novel Global Community Educational Foundation, 7 Peterlee Place, Hebersham, NSW 2770, Australia.

8 Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.

9 SciLifeLab, Department of Oncology and Pathology, Karolinska Institutet, Box 1031, 171 21 Stockholm, Sweden.