exostosin glycosyltransferase 1 (EXT1) : Machine learning discoveries of 2nd order synergy in Meningiomas

Background : Exostosin-1 is a protein that encoded by the EXT1 which is one of the two endoplasmic reticulum-resident type II transmembrane glycosyltransferase, that is involved in the chain elongation step of heparan sulfate biosynthesis. Mutations in this gene cause the type I form of multiple exostoses. It is also implicated in Langer-Giedion syndrome (tricho-rhino-phalangeal syndrome type II, TRPS II), hereditary multiple exostoses, osteochondromas, breast and prostate cancer, hypertrichosis, mental retardation, epilepsy, non-small cell lung carcinoma, gastric cancer, uterine corpus endometrial carcinoma, Sjo ̈gren’s syndrome, Zika virus (ZIKV) infection and osteoarthritis, to name a few. However, its role in meningiomas has not yet been determined completely. Meningiomas are the most common intracranial primary neoplasm in adults. Patel et al. [1] analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) using clinical, gene expression, and sequencing data and using unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. Further, these groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Issue : Increasing evidence point to the fact that meningioma classification and grading, that is based on histopathology does not always accurately predict tumor aggressiveness and recurrence behaviour and knowledge of the underlying biology of the treatment resistant meningiomas and the impact of ge- netic alterations in these tumors, is lacking. At the current stage more genomic studies are required to unravel the role of other genes and their interations with other genetic factors. Resolution : In a recently published work Sinha [2], a frame work of a search engine was developed which can rank combinations of factors (genes/proteins) in a signaling pathway. Adapting this search engine to the Meningioma dataset, i present here 2nd order combinations of EXT1, some of which have been known to exist via wet lab experiments, but many are yet to be tested. The reveals combinations might help oncologists/biologists test possible hypotheses that might be the causing factors in meningioma. Further, in my limited grasp, if proven true, the combinations revealed by the search engine might pave way for development of gene based therapies aimed at resolving pathological issues related to meningiomas.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-30
DOI
https://doi.org/10.5281/zenodo.16599850
Primary Topic
Archaeology and ancient environmental studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

exostosin glycosyltransferase 1 (EXT1) : Machine learning discoveries of 2nd order synergy in Meningiomas

Zenodo (CERN European Organization for Nuclear Research)
Archaeology and ancient environmental studies
article

exostosin glycosyltransferase 1 (EXT1) : Machine learning discoveries of 2nd order synergy in Meningiomas

article en

Abstract

Background : Exostosin-1 is a protein that encoded by the EXT1 which is one of the two endoplasmic reticulum-resident type II transmembrane glycosyltransferase, that is involved in the chain elongation step of heparan sulfate biosynthesis. Mutations in this gene cause the type I form of multiple exostoses. It is also implicated in Langer-Giedion syndrome (tricho-rhino-phalangeal syndrome type II, TRPS II), hereditary multiple exostoses, osteochondromas, breast and prostate cancer, hypertrichosis, mental retardation, epilepsy, non-small cell lung carcinoma, gastric cancer, uterine corpus endometrial carcinoma, Sjo ̈gren’s syndrome, Zika virus (ZIKV) infection and osteoarthritis, to name a few. However, its role in meningiomas has not yet been determined completely. Meningiomas are the most common intracranial primary neoplasm in adults. Patel et al. [1] analyzed 160 tumors from all 3 World Health Organization (WHO) grades (I through III) using clinical, gene expression, and sequencing data and using unsupervised clustering analysis identified 3 molecular types (A, B, and C) that reliably predicted recurrence. Further, these groups did not directly correlate with the WHO grading system, which classifies more than half of the tumors in the most aggressive molecular type as benign. Issue : Increasing evidence point to the fact that meningioma classification and grading, that is based on histopathology does not always accurately predict tumor aggressiveness and recurrence behaviour and knowledge of the underlying biology of the treatment resistant meningiomas and the impact of ge- netic alterations in these tumors, is lacking. At the current stage more genomic studies are required to unravel the role of other genes and their interations with other genetic factors. Resolution : In a recently published work Sinha [2], a frame work of a search engine was developed which can rank combinations of factors (genes/proteins) in a signaling pathway. Adapting this search engine to the Meningioma dataset, i present here 2nd order combinations of EXT1, some of which have been known to exist via wet lab experiments, but many are yet to be tested. The reveals combinations might help oncologists/biologists test possible hypotheses that might be the causing factors in meningioma. Further, in my limited grasp, if proven true, the combinations revealed by the search engine might pave way for development of gene based therapies aimed at resolving pathological issues related to meningiomas.

Zenodo (CERN European Organization for Nuclear Research)
Good health and well-being
Openalex Percentile: Top 69%
Archaeology and ancient environmental studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.