&lt;p&gt;“High Treg” Inflammations Promote (Most) Non-Hematologic Cancers While “Low Treg” Inflammations Promote Lymphoid Cancers&lt;/p&gt;

Elkoshi, Zeev

doi:10.2147/jir.s249384

Cited by 7 publications

(11 citation statements)

References 84 publications

(122 reference statements)

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“…The association between “low Treg” pathogens and autoimmune diseases was discussed in earlier publications. 8 , 9 Here we discuss the association between “low Treg” pathogens and MPNs.…”

Section: Diseases Driven By Piks Are (Mostly) Associated With “Low Trmentioning

confidence: 99%

“… 126 It has been shown that lymphoma starts as a “low Treg” disease, and can dwell in this state for several years, before aggressive disease develops. 9 In addition, EBV is involved in the etiology of nasopharyngeal carcinoma and gastric carcinoma. 126 Tumor-infiltrating Tregs correlate with a poor prognosis in gastric cancer but no statistically significant correlation was observed for oropharyngeal cancer.…”

Section: Pathogens That Activate Both Aiks and Piks Can Induce Both “mentioning

confidence: 99%

“… 145 Non-Hodgkin’s lymphoma is a “low Treg” disease during the long indolent stage of the disease. 9 Type-2 diabetes is a “low Treg” disease. 146 …”

Section: By Activating Anti-inflammatory Kinases Alcohol Consumptionmentioning

confidence: 99%

“…8 In addition, it explains why “high Treg” inflammation promotes many solid cancers while “low Treg” inflammation promotes lymphomas. 9 …”

Section: Introductionmentioning

confidence: 99%

“…This explains the association of most solid cancers with “high Treg” pathogens and of autoimmune diseases with “low Treg” pathogens, which was reported in earlier publications. 8 , 9 Moreover, pathogens that activate both AIKs and PIKs may induce both cancers and autoimmune diseases.…”

Section: Introductionmentioning

confidence: 99%

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The Binary Classification of Protein Kinases

Elkoshi¹

2021

JIR

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In an earlier publication a binary model for chronic diseases classification has been proposed. According to the model, chronic diseases were classified as “high Treg” or “low Treg” diseases, depending on whether the immune response is anti- or pro-inflammatory and assuming that regulatory T cells are major determinants of the response. It turned out that most cancers are “high Treg” diseases, while autoimmune diseases are “low Treg”. This paper proposes a molecular cause for this binary response. The mechanism proposed depends on the effect of protein kinases on the immune system. Thus, protein kinases are classified as anti- or pro-inflammatory kinases depending on whether they drive “high Treg” or “low Treg” diseases. Observations reported in the earlier publication can be described in terms of anti-inflammatory kinase (AIK) or pro-inflammatory kinase (PIK) activity. Analysis of literature data reveals that the two classes of kinases display distinctive properties relating to their interactions with pathogens and environmental factors. Pathogens that promote Treg activity (“high Treg” pathogens) activate AIKs, while pathogens that suppress Treg activity (“low Treg” pathogens) activate PIKs. Diseases driven by AIKs are associated with “high Treg” pathogens while those diseases driven by PIKs are associated with “low Treg” pathogens. By promoting the activity of AIKs, alcohol consumption increases the risk of “high Treg” cancers but decreases the risk of some “low Treg” autoimmune diseases. JAK1 gain-of-function mutations are observed at high frequencies in autoimmune diseases while JAK1 loss-of-function mutations are observed at high frequencies in cancers with high tumor-infiltrating Tregs. It should also be noted that the corresponding two classes of protein kinase inhibitors are mutually exclusive in terms of their approved therapeutic indications. There is no protein kinase inhibitor that is approved for the treatment of both autoimmune diseases and “high Treg” cancers. Although there are exceptions to the conclusions presented above, these conclusions are supported by the great bulk of published data. It therefore seems that the binary division of protein kinases is a useful tool for elucidating (at the molecular level) many distinctive properties of cancers and autoimmune diseases.

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Section: Diseases Driven By Piks Are (Mostly) Associated With “Low Trmentioning

confidence: 99%

Section: Pathogens That Activate Both Aiks and Piks Can Induce Both “mentioning

confidence: 99%

“… 145 Non-Hodgkin’s lymphoma is a “low Treg” disease during the long indolent stage of the disease. 9 Type-2 diabetes is a “low Treg” disease. 146 …”

Section: By Activating Anti-inflammatory Kinases Alcohol Consumptionmentioning

confidence: 99%

“…8 In addition, it explains why “high Treg” inflammation promotes many solid cancers while “low Treg” inflammation promotes lymphomas. 9 …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Binary Classification of Protein Kinases

Elkoshi¹

2021

JIR

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circ_SPEF2 Regulates the Balance of Treg Cells by Regulating miR-16-5p/BACH2 in Lymphoma and Participates in the Immune Response

Zhou,

Xu,

Chen

et al. 2023

Tissue Eng Regen Med

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Reproducible and opposing gut microbiome signatures distinguish autoimmune diseases and cancers: a systematic review and meta-analysis

Islam

Tran

et al. 2022

Microbiome

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Background The gut microbiome promotes specific immune responses, and in turn, the immune system has a hand in shaping the microbiome. Cancer and autoimmune diseases are two major disease families that result from the contrasting manifestations of immune dysfunction. We hypothesized that the opposing immunological profiles between cancer and autoimmunity yield analogously inverted gut microbiome signatures. To test this, we conducted a systematic review and meta-analysis on gut microbiome signatures and their directionality in cancers and autoimmune conditions. Methodology We searched PubMed, Web of Science, and Embase to identify relevant articles to be included in this study. The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements and PRISMA 2009 checklist. Study estimates were pooled by a generic inverse variance random-effects meta-analysis model. The relative abundance of microbiome features was converted to log fold change, and the standard error was calculated from the p-values, sample size, and fold change. Results We screened 3874 potentially relevant publications. A total of 82 eligible studies comprising 37 autoimmune and 45 cancer studies with 4208 healthy human controls and 5957 disease cases from 27 countries were included in this study. We identified a set of microbiome features that show consistent, opposite directionality between cancers and autoimmune diseases in multiple studies. Fusobacterium and Peptostreptococcus were the most consistently increased genera among the cancer cases which were found to be associated in a remarkable 13 (+0.5 log fold change in 5 studies) and 11 studies (+3.6 log fold change in 5 studies), respectively. Conversely, Bacteroides was the most prominent genus, which was found to be increased in 12 autoimmune studies (+0.2 log fold change in 6 studies) and decreased in six cancer studies (−0.3 log fold change in 4 studies). Sulfur-metabolism pathways were found to be the most frequent pathways among the member of cancer-increased genus and species. Conclusions The surprising reproducibility of these associations across studies and geographies suggests a shared underlying mechanism shaping the microbiome across cancers and autoimmune diseases.

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<p>“High Treg” Inflammations Promote (Most) Non-Hematologic Cancers While “Low Treg” Inflammations Promote Lymphoid Cancers</p>

Cited by 7 publications

References 84 publications

The Binary Classification of Protein Kinases

The Binary Classification of Protein Kinases

circ_SPEF2 Regulates the Balance of Treg Cells by Regulating miR-16-5p/BACH2 in Lymphoma and Participates in the Immune Response

Reproducible and opposing gut microbiome signatures distinguish autoimmune diseases and cancers: a systematic review and meta-analysis

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