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Review Article - (2022) Volume 13, Issue 9

Immune Gene Prognostic Signature by Translational Research

Rajan Arora*
 
Department of Infectious Diseases, University of Edinburgh, UK
 
*Correspondence: Rajan Arora, Department of Infectious Diseases, University of Edinburgh, UK, Email:

Received: 01-Sep-2022, Manuscript No. Iptb- 22-13097; Editor assigned: 05-Sep-2022, Pre QC No. Iptb- 22-13097; Reviewed: 19-Sep-2022, QC No. Iptb- 22-13097; Revised: 23-Sep-2022, Manuscript No. Iptb- 22-13097 (R); Published: 30-Sep-2022

Abstract

Massive data sets and the development of artificial intelligence algorithms provide fresh perspectives and options for predicting cancer patients' unique mortality risks. The goal of the recently completed research was to construct an artificial intelligence survival prediction system for stomach cancer patients who were disease-free [1]. The artificial intelligence survival predicting system was created using the multi-task logistic regression algorithm, the cox survival regression method, and the random survival forest algorithm [2]. A transcription factor regulatory network of immune genes was created using 19 transcription factors and 70 immune genes [3]. Fourteen immunological genes were identified as prognostic indicators using multivariate Cox regression. Using these immune genes, a predictive signature for stomach cancer was created [4]. According to epidemiological data, gastric cancer is one of the most common digestive malignant tumours and the second highest cause of tumor-related fatalities, with fatalities in the prognosis for people with stomach cancer remained poor despite improvements in early screening, diagnosis, and therapies that somewhat lowered death [5]. Clinical factors that contribute to improve the prognosis of high risk GC patients include early detection of high mortality high risk GC patients and more accurate, customised therapy [6].

Keywords

COVID-19; Influenza; Respiratory syncytial virus; Immunological landscape

Introduction

Therefore, accurate and reliable individual mortality risk prediction is crucial for maximising the effectiveness of each patient's treatment. Precision medicine has advanced significantly in recent years [7]. Individual mortality risk may be predicted using precise medical prediction methods at various time periods, and the effectiveness for various However, specific treatment demands have not been able to be met by precision medical prediction methods for forecasting mortality risk of gastric cancer patients. Advances in bioinformatics gave precision medical research on carcinogenesis and progression a major boost [8]. To investigate the innate biological regulatory systems and probable routes for carcinogenesis and progression, bioinformatics is useful. Studies have increasingly concentrated in recent years on the critical function of the immune microenvironment in the development and spread of tumours [9]. A prognostic signature was created by Jiang et al. to forecast the prognosis of patients with stomach cancer. A predictive profile based on immune genes was created by Yang et al. to forecast the overall survival of GC patients [10]. This prognostic signature, however, was only suitable for use in clinical settings and did not provide a mathematical formula. Therefore, consequently, it is important to provide specialised, precise medical diagnostic methods for the early detection of stomach cancer with a high mortality risk [11]. Precision medical predictive algorithms can assist doctors identify individuals with high mortality risk early on and can give a personalised mortality risk prediction. Recently, based on genetic information for various malignancies, our team successfully built a number of precision medical forecasting tools. The advancement of artificial intelligence algorithms in recent years has given tumour prognostic prediction research new options [12]. Predictive and prognostic model accuracy has been increased using the multi-task logistic regression algorithm, Cox survival regression algorithm, and random survival forest algorithm [13]. As a result, current research focused on developing an artificial intelligence survival prediction system and investigating possible immune regulatory mechanisms for GC prognosis. For estimating the danger of an individual's death at various times [14]. Model dataset, which included mRNAs from 32 normal specimens and 375 GC specimens, was retrieved from the TCGA database. After transferring patients with follow-up data of less than one month, 265 GC patients were included. Dataset for validation was taken from the GEO database. Two hundred and seventy-nine patients and 19,765 mRNAs were included in the GSE62254 dataset [15]. According to the Gencode.v29 background file, probe IDs were converted to recognised gene symbols. The R package "edger" was used to conduct differentially expressed analysis between GC samples and normal samples. Trimmed mean of M values was used to normalise the original data. P 0.05 and log2 were used as the cutoff values for analysis of differentially expressed genes. Immunology provided the immune genes. In molecular biology, transcription factors play a significant role in controlling the development and spread of tumours. Three hundred and eighteen transcription factors were found in the Cist Rome Cancer database to investigate possible regulatory connections between transcription factors and immune genes. Using the Tumor Immune Estimation Resource database, associations between immune genes and tumor-infiltrating immune cells were investigated. The Tumor Immune Estimation Resource database's tumour infiltrating immune cell dataset, which included samples and original values of six tumour infiltrating immune cells B cell, CD4 Tell, CD8 cell, Neutrophil, Macrophage, and Dendritic), demonstrated the correlation between tumour infiltrating immune cells and immune genes. Supplementary Fig. 10 showed the association between immune genes and tumor-infiltrating immune cells. Subgroup analysis showed that there was no discernible variation in immune gene expression. 14 immune genes that are closely associated to the prognosis of gastric cancer were found in the current investigation. These immune genes might develop into useful prognostic biomarkers and potential tumour therapeutic targets.

Discussion

To better understand the probable molecular regulatory mechanisms of carcinogenesis and progression, the current study built a transcription factor regulatory network comprising immune genes. A predictive signature for DFS of GC tor A was created and confirmed in the current study, and it includes DNA catabolic process, endonucleolytic activity, temperature homeostasis, and negative control of cytokine production. Tissue homeostasis, epithelial cell development, and epithelial cell morphogenesis are the three primary biological processes of V-set and immunoglobulin domain containing 1. The main biological function of ferritin family member 1 include cell polarity formation or maintenance, adenoidal-type cell movement, and is the positive regulation of the cytokine-mediated signalling pathway and the negative control of immune system function and viral response. Gap junction protein, beta 6, 30 kDa, is primarily involved in the biological processes of cellular glucose homeostasis, reaction to molecule of bacterial origin, and ageing. The development of a polarised epithelium, ossification, and retinoid metabolic process are the three main biological processes of glycan. Response to viruses, defensive responses to viruses, and defence responses to other organisms are the three main biological processes of interferon-induced protein 44-like. Regulation of cell morphogenesis involved in differentiation, retinoid metabolic process, and isoprenoid metabolic process are three important biological processes that low density lipoprotein receptor related protein 8 regulates. Extrinsic apoptotic signalling pathway through death domain receptors, vasculature, and fibrinogen beta chain is the primary biological action of this protein. Built a network of immune gene transcription factor regulation. The possible contribution of immune genes to the development and spread of tumours was shown by this regulatory network. Interleukin 25 was substantially associated with the prognosis of gastric cancer following radical resection and was produced by tumor-infiltrating macrophages.

Conclusion

By boosting the transforming growth factor beta/bone morphogenetic protein pathway, macrophages may increase the invasiveness of gastric cancer cells. The prognosis and lymph node metastases of gastric cancer were linked to high expression of CD8 + T cells [50]. A poor prognosis for gastric cancer was substantially linked with a high regulatory T cells to CD8 + T cells ratio. High CD8 + T cell infiltration led to an increase in programmed death ligand 1 and a reduction in survival rate. CD8+ T lymphocytes may be stimulated by tumour antigen. Cession 14 immune genes that are closely associated to the prognosis of gastric cancer were found in the current investigation. These immune genes might develop into useful prognostic biomarkers and potential tumour therapeutic targets. To better understand the probable molecular regulatory mechanisms of carcinogenesis and progression, the current study built a transcription factor regulatory network comprising immune genes. A predictive signature for DFS of GC tor A was created and confirmed in the current study, and it includes DNA catabolic process, endonucleolytic activity, temperature homeostasis, and negative control of cytokine production. Tissue homeostasis, epithelial cell development, and epithelial cell morphogenesis are the three primary biological processes of V-set and immunoglobulin domain containing. The primary biological function of member 1 of the ferritin family cell polarity formation or maintenance, epithelial cell movement, and adenoidal-type cell migration. Positive control of collagen metabolism, ageing, and regulation of collagen metabolism are three of resisting’s main biological functions. Negative control of immune system function, viral response, and positive regulation of the cytokine-mediated signalling pathway are the three primary biological processes of the NLR family, CARD domaincontaining protein 5. Gap junction protein, beta 6, 30 kDa, is primarily involved in the biological processes of cellular glucose homeostasis, reaction to molecule of bacterial origin, and ageing. The development of a polarised epithelium, ossification, and retinoid metabolic process are the three main biological processes of glycan 3. Response to viruses, protection against viruses, and defence against viruses are the three main biological processes of interferon-induced protein 44-like 160 immunological genes were identified by univariate Cox regression as prognostic indicators for GC. Gene Survival Analysis Screen System, a highly accurate medical predicting tool, was created to investigate the prognostic significance of these 160 immune genes in various subgroups. It was given the Gene Survival Analysis Screen System. Univariate Cox regression revealed 160 immunological genes as predictive indicators for GC. A crucial link in the molecular regulation cascade is the transcription factor. The current study conducted correlation analysis to better comprehend the regulatory link between transcription factors and immune genes.

Acknowledgement

None

Conflict of Interest

None

References

  1. Sung H, Ferlay J, Siegel RL (2021)Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71: 209-249.
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  3. Emens LA, Ascierto PA, Darcy PK (2017) Science Direct Cancer immunotherapy: opportunities and challenges in the rapidly evolving clinical landscape. Eur J 81: 116-129.
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  9. Disparities A, Gross CP, Page P (2004) Equal proportion to the cancer disease. Primary Care 291: 2720-2726.
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  13. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL, et al. (2018) Artificial intelligence in radiology. Nat Rev Cancer 18: 500-510.
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  15. Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380: 1347-1358.
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  27. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL, et al. (2018) Artificial intelligence in radiology. Nat Rev Cancer 18: 500-510.
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  29. Ghosh A (2019) Artificial intelligence using open source BI-RADS data exemplifying potential future use. J Am Coll Radiol 16: 64-72.
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Keywords

COVID-19; Influenza; Respiratory syncytial virus; Immunological landscape

Introduction

Therefore, accurate and reliable individual mortality risk prediction is crucial for maximising the effectiveness of each patient's treatment. Precision medicine has advanced significantly in recent years [7]. Individual mortality risk may be predicted using precise medical prediction methods at various time periods, and the effectiveness for various However, specific treatment demands have not been able to be met by precision medical prediction methods for forecasting mortality risk of gastric cancer patients. Advances in bioinformatics gave precision medical research on carcinogenesis and progression a major boost [8]. To investigate the innate biological regulatory systems and probable routes for carcinogenesis and progression, bioinformatics is useful. Studies have increasingly concentrated in recent years on the critical function of the immune microenvironment in the development and spread of tumours [9]. A prognostic signature was created by Jiang et al. to forecast the prognosis of patients with stomach cancer. A predictive profile based on immune genes was created by Yang et al. to forecast the overall survival of GC patients [10]. This prognostic signature, however, was only suitable for use in clinical settings and did not provide a mathematical formula. Therefore, consequently, it is important to provide specialised, precise medical diagnostic methods for the early detection of stomach cancer with a high mortality risk [11]. Precision medical predictive algorithms can assist doctors identify individuals with high mortality risk early on and can give a personalised mortality risk prediction. Recently, based on genetic information for various malignancies, our team successfully built a number of precision medical forecasting tools. The advancement of artificial intelligence algorithms in recent years has given tumour prognostic prediction research new options [12]. Predictive and prognostic model accuracy has been increased using the multi-task logistic regression algorithm, Cox survival regression algorithm, and random survival forest algorithm [13]. As a result, current research focused on developing an artificial intelligence survival prediction system and investigating possible immune regulatory mechanisms for GC prognosis. For estimating the danger of an individual's death at various times [14]. Model dataset, which included mRNAs from 32 normal specimens and 375 GC specimens, was retrieved from the TCGA database. After transferring patients with follow-up data of less than one month, 265 GC patients were included. Dataset for validation was taken from the GEO database. Two hundred and seventy-nine patients and 19,765 mRNAs were included in the GSE62254 dataset [15]. According to the Gencode.v29 background file, probe IDs were converted to recognised gene symbols. The R package "edger" was used to conduct differentially expressed analysis between GC samples and normal samples. Trimmed mean of M values was used to normalise the original data. P 0.05 and log2 were used as the cutoff values for analysis of differentially expressed genes. Immunology provided the immune genes. In molecular biology, transcription factors play a significant role in controlling the development and spread of tumours. Three hundred and eighteen transcription factors were found in the Cist Rome Cancer database to investigate possible regulatory connections between transcription factors and immune genes. Using the Tumor Immune Estimation Resource database, associations between immune genes and tumor-infiltrating immune cells were investigated. The Tumor Immune Estimation Resource database's tumour infiltrating immune cell dataset, which included samples and original values of six tumour infiltrating immune cells B cell, CD4 Tell, CD8 cell, Neutrophil, Macrophage, and Dendritic), demonstrated the correlation between tumour infiltrating immune cells and immune genes. Supplementary Fig. 10 showed the association between immune genes and tumor-infiltrating immune cells. Subgroup analysis showed that there was no discernible variation in immune gene expression. 14 immune genes that are closely associated to the prognosis of gastric cancer were found in the current investigation. These immune genes might develop into useful prognostic biomarkers and potential tumour therapeutic targets.

Discussion

To better understand the probable molecular regulatory mechanisms of carcinogenesis and progression, the current study built a transcription factor regulatory network comprising immune genes. A predictive signature for DFS of GC tor A was created and confirmed in the current study, and it includes DNA catabolic process, endonucleolytic activity, temperature homeostasis, and negative control of cytokine production. Tissue homeostasis, epithelial cell development, and epithelial cell morphogenesis are the three primary biological processes of V-set and immunoglobulin domain containing 1. The main biological function of ferritin family member 1 include cell polarity formation or maintenance, adenoidal-type cell movement, and is the positive regulation of the cytokine-mediated signalling pathway and the negative control of immune system function and viral response. Gap junction protein, beta 6, 30 kDa, is primarily involved in the biological processes of cellular glucose homeostasis, reaction to molecule of bacterial origin, and ageing. The development of a polarised epithelium, ossification, and retinoid metabolic process are the three main biological processes of glycan. Response to viruses, defensive responses to viruses, and defence responses to other organisms are the three main biological processes of interferon-induced protein 44-like. Regulation of cell morphogenesis involved in differentiation, retinoid metabolic process, and isoprenoid metabolic process are three important biological processes that low density lipoprotein receptor related protein 8 regulates. Extrinsic apoptotic signalling pathway through death domain receptors, vasculature, and fibrinogen beta chain is the primary biological action of this protein. Built a network of immune gene transcription factor regulation. The possible contribution of immune genes to the development and spread of tumours was shown by this regulatory network. Interleukin 25 was substantially associated with the prognosis of gastric cancer following radical resection and was produced by tumor-infiltrating macrophages.

Conclusion

By boosting the transforming growth factor beta/bone morphogenetic protein pathway, macrophages may increase the invasiveness of gastric cancer cells. The prognosis and lymph node metastases of gastric cancer were linked to high expression of CD8 + T cells [50]. A poor prognosis for gastric cancer was substantially linked with a high regulatory T cells to CD8 + T cells ratio. High CD8 + T cell infiltration led to an increase in programmed death ligand 1 and a reduction in survival rate. CD8+ T lymphocytes may be stimulated by tumour antigen. Cession 14 immune genes that are closely associated to the prognosis of gastric cancer were found in the current investigation. These immune genes might develop into useful prognostic biomarkers and potential tumour therapeutic targets. To better understand the probable molecular regulatory mechanisms of carcinogenesis and progression, the current study built a transcription factor regulatory network comprising immune genes. A predictive signature for DFS of GC tor A was created and confirmed in the current study, and it includes DNA catabolic process, endonucleolytic activity, temperature homeostasis, and negative control of cytokine production. Tissue homeostasis, epithelial cell development, and epithelial cell morphogenesis are the three primary biological processes of V-set and immunoglobulin domain containing. The primary biological function of member 1 of the ferritin family cell polarity formation or maintenance, epithelial cell movement, and adenoidal-type cell migration. Positive control of collagen metabolism, ageing, and regulation of collagen metabolism are three of resisting’s main biological functions. Negative control of immune system function, viral response, and positive regulation of the cytokine-mediated signalling pathway are the three primary biological processes of the NLR family, CARD domaincontaining protein 5. Gap junction protein, beta 6, 30 kDa, is primarily involved in the biological processes of cellular glucose homeostasis, reaction to molecule of bacterial origin, and ageing. The development of a polarised epithelium, ossification, and retinoid metabolic process are the three main biological processes of glycan 3. Response to viruses, protection against viruses, and defence against viruses are the three main biological processes of interferon-induced protein 44-like 160 immunological genes were identified by univariate Cox regression as prognostic indicators for GC. Gene Survival Analysis Screen System, a highly accurate medical predicting tool, was created to investigate the prognostic significance of these 160 immune genes in various subgroups. It was given the Gene Survival Analysis Screen System. Univariate Cox regression revealed 160 immunological genes as predictive indicators for GC. A crucial link in the molecular regulation cascade is the transcription factor. The current study conducted correlation analysis to better comprehend the regulatory link between transcription factors and immune genes.

Acknowledgement

None

Conflict of Interest

None

References

  1. Sung H, Ferlay J, Siegel RL (2021)Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71: 209-249.
  2. Indexed at, Google Scholar

  3. Emens LA, Ascierto PA, Darcy PK (2017) Science Direct Cancer immunotherapy: opportunities and challenges in the rapidly evolving clinical landscape. Eur J 81: 116-129.
  4. Indexed at, Google Scholar

  5. Ahnen DJ, Wade SW, Jones WF (2014) The increasing incidence of young-onset colorectal cancer: a call to action. Mayo Clin Proc 89: 216-224.
  6. Indexed at, Crossref, Google Scholar

  7. Verma V, Sprave T, Haque W (2018) A systematic review of the cost and cost-effectiveness studies of immune checkpoint inhibitors 11 Medical and Health Sciences 1112 Oncology and Carcinogenesis. J Immunother  Cancer 6:  1-15:
  8. Indexed at, Crossref, Google Scholar

  9. Disparities A, Gross CP, Page P (2004) Equal proportion to the cancer disease. Primary Care 291: 2720-2726.
  10. Indexed at, Google Scholar

  11. Dong J, Geng Y, Lu D (2020) Clinical trials for artificial intelligence in cancer diagnosis: a cross-sectional study of registered trials in ClinicalTrials. gov Front Oncol 10: 1-6.
  12. Indexed at, Google Scholar

  13. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL, et al. (2018) Artificial intelligence in radiology. Nat Rev Cancer 18: 500-510.
  14. Indexed at, Google Scholar

  15. Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380: 1347-1358.
  16. Indexed at, Google Scholar

  17. Wang F, Casalino LP, Khullar D (2019) Deep learning in medicine – promise, progress, and challenges. JAMA Intern Med 179: 293-294.
  18. Indexed at, Google Scholar

  19. Wiens J, Saria S, Sendak M (2019) Do no harm: a roadmap for responsible machine learning for health care. Nat Med 25: 1337-1340.
  20. Indexed at, Google Scholar

  21. Handelman GS, Kok HK, Chandra RV (2018) Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. AJR Am J Roentgenol 212: 38-43.
  22. Indexed at, Google Scholar

  23. Jha S, Topol EJ (2016) Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316: 2353-2354.
  24. Indexed at, Google Scholar

  25. Bi WL, Hosny A, Schabath MB (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69: 127-157.
  26. Indexed at, Google Scholar

  27. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL, et al. (2018) Artificial intelligence in radiology. Nat Rev Cancer 18: 500-510.
  28. Indexed at, Google Scholar

  29. Ghosh A (2019) Artificial intelligence using open source BI-RADS data exemplifying potential future use. J Am Coll Radiol 16: 64-72.
  30. Indexed at, Google Scholar

Citation: Arora R (2022) Immune Gene Prognostic Signature by Translational Research. Transl Biomed, Vol. 13 No. 9: 252.