Evidence map›Paper›PMID 35039759›Full record

ArticleOxidative medicine and cellular longevity2022

Association between Arsenic Level, Gene Expression in Asian Population, and In Vitro Carcinogenic Bladder Tumor.

Sonalika Singhal, Nathan A Ruprecht, Donald Sens, Kouhyar Tavakolian, Kevin L Gardner, Sandeep K Singhal

Open access · hybridAbstract read
In one paragraph

Article in Oxidative medicine and cellular longevity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 38% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Sonalika SinghalDepartment of Pathology, School of Medicine and Health Sciences, University of North Dakota, ND, USA.
Nathan A RuprechtDepartment of Biomedical Engineering, School of Electrical Engineering and Computer Science, University of North Dakota, ND, USA.
Donald SensDepartment of Pathology, School of Medicine and Health Sciences, University of North Dakota, ND, USA.
Kouhyar TavakolianDepartment of Biomedical Engineering, School of Electrical Engineering and Computer Science, University of North Dakota, ND, USA.
Kevin L GardnerDepartment of Pathology and Cell Biology, Columbia University Irvine Medical Center, New York, NY, USA.
Sandeep K SinghalDepartment of Pathology, School of Medicine and Health Sciences, University of North Dakota, ND, USA.ORCID https://orcid.org/0000-0002-3486-067X
University of North Dakota · USColumbia University · US

Funding

Research CoresP20GM103442 · NIGMS · UNIVERSITY OF NORTH DAKOTA · PI Donald A. Sens · 2012 to 2026
$53.0M
Tracking and Evaluation CoreU54GM128729 · NIGMS · UNIVERSITY OF NORTH DAKOTA · PI BASSON, MARC D. · 2018 to 2022
$20.3M
NIGMS NIH HHS P20 GM103442NIGMS NIH HHS U54 GM128729
6 · The paper itself

Abstract

The IARC classified arsenic (As) as "carcinogenic to humans." Despite the health consequences of arsenic exposure, there is no molecular signature available yet that can predict when exposure may lead to the development of disease. To understand the molecular processes underlying arsenic exposure and the risk of disease development, this study investigated the functional relationship between high arsenic exposure and disease risk using gene expression derived from human exposure. In this study, a three step analysis was employed: (1) the gene expression profiles obtained from two diverse arsenic-exposed Asian populations were utilized to identify differentially expressed genes associated with arsenic exposure in human subjects, (2) the gene expression profiles induced by arsenic exposure in four different myeloma cancer cell lines were used to define common genes and pathways altered by arsenic exposure, and (3) the genetic profiles of two publicly available human bladder cancer studies were used to test the significance of the common association of genes, identified in step 1 and step 2, to develop and validate a predictive model of primary bladder cancer risk associated with arsenic exposure. Our analysis shows that arsenic exposure to humans is mainly associated with organismal injury and abnormalities, immunological disease, inflammatory disease, gastrointestinal disease, and increased rates of a wide variety of cancers. In addition, arsenic exerts its toxicity by generating reactive oxygen species (ROS) and increasing ROS production causing the imbalance that leads to cell and tissue damage (oxidative stress). Oxidative stress activates inflammatory pathways leading to transformation of a normal cell to tumor cell specifically; there is significant evidence of the advancing changes in oxidative/nitrative stress during the progression of bladder cancer. Therefore, we examined the relation of differentially expressed genes due to exposure of arsenic in human and bladder cancer and developed a bladder cancer risk prediction model. In this study, integrin-linked kinase (ILK) was one of the most significant pathways identified between both arsenic exposed population which plays a key role in eliciting a protective response to oxidative damage in epidermal cells. On the other hand, several studies showed that arsenic trioxide (ATO) is useful for anticancer therapy although the mechanisms underlying its paradoxical effects are still not well understood. ATO has shown remarkable efficacy for the treatment of multiple myeloma; therefore, it will be helpful to understand the underlying cancer biology by which ATO exerts its inhibitory effect on the myeloma cells. Our study found that MAPK is one of the most active network between arsenic gene and ATO cell line which is involved in indicative of oxidative/nitrosative damage and well associated with the development of bladder cancer. The study identified a unique set of 147 genes associated with arsenic exposure and linked to molecular mechanisms of cancer. The risk prediction model shows the highest prediction ability for recurrent bladder tumors based on a very small subset (NKIRAS2, AKTIP, and HLA-DQA1) of the 147 genes resulting in AUC of 0.94 (95% CI: 0.744-0.995) and 0.75 (95% CI: 0.343-0.933) on training and validation data, respectively.

Indexed as

ArsenicAsian PeopleHumansTranscriptomeUrinary Bladder NeoplasmsArsenic

Identifiers

PMID35039759
PMCPMC8760535
OpenAlexW4205222629

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.