Evidence map›Paper›PMID 37398275›Full record

ArticlemedRxiv : the preprint server for health sciences2024

TGIRT-seq of Inflammatory Breast Cancer Tumor and Blood Samples Reveals Widespread Enhanced Transcription Impacting RNA Splicing and Intronic RNAs in Plasma.

Dennis Wylie, Xiaoping Wang, Jun Yao, Hengyi Xu, Elizabeth A Ferrick-Kiddie, Toshiaki Iwase, Savitri Krishnamurthy, Naoto T Ueno, Alan M Lambowitz

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Dennis WylieDepartments of Molecular Biosciences and Oncology, University of Texas at Austin, Austin, TX 78712.ORCID 0000-0003-0380-3549
Xiaoping WangDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030.ORCID 0000-0003-4511-8638
Jun YaoDepartments of Molecular Biosciences and Oncology, University of Texas at Austin, Austin, TX 78712.ORCID 0000-0002-1232-1587
Hengyi XuDepartments of Molecular Biosciences and Oncology, University of Texas at Austin, Austin, TX 78712.ORCID 0000-0001-9809-4882
Elizabeth A Ferrick-KiddieDepartments of Molecular Biosciences and Oncology, University of Texas at Austin, Austin, TX 78712.ORCID 0000-0003-0088-5315
Toshiaki IwaseDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030.ORCID 0000-0002-5302-2292
Savitri KrishnamurthyMorgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, TX 77030.ORCID 0000-0001-9030-4136
Naoto T UenoDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030.ORCID 0000-0002-0166-7275
Alan M LambowitzDepartments of Molecular Biosciences and Oncology, University of Texas at Austin, Austin, TX 78712.ORCID 0000-0001-6036-2423
The University of Texas at Austin · USThe University of Texas MD Anderson Cancer Center · USUniversity of Hawaiʻi at Mānoa · US

Funding

Group II Intron and Related Reverse TranscriptasesR35GM136216 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI ALAN M. LAMBOWITZ · 2020 to 2026
$6.0M
Development of a novel therapy targeting the tumor microenvironment in inflammatory breast cancerR01CA258523 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI UENO, NAOTO T. · 2022 to 2025
$3.2M
NCI NIH HHS R01 CA258523NIGMS NIH HHS R35 GM136216
6 · The paper itself

Abstract

Inflammatory breast cancer (IBC) is the most aggressive and lethal breast cancer subtype but lacks unequivocal genomic differences or robust biomarkers that differentiate it from non-IBC. Here, Thermostable Group II intron Reverse Transcriptase RNA-sequencing (TGIRT-seq) revealed myriad differences in tumor samples, Peripheral Blood Mononuclear Cells (PBMCs), and plasma that distinguished IBC from non-IBC patients and healthy donors across all tested receptor-based subtypes. These included numerous differentially expressed protein-coding gene and non-coding RNAs in all three sample types, a granulocytic immune response in IBC PBMCs, and over-expression of antisense RNAs, suggesting wide-spread enhanced transcription in both IBC tumors and PBMCs. By using TGIRT-seq to quantitate Intron-exon Depth Ratios (IDRs) and mapping reads to both genome and transcriptome reference sequences, we developed methods for parallel analysis of transcriptional and post-transcriptional gene regulation. This analysis identified numerous differentially and non-differentially expressed protein-coding genes in IBC tumors and PBMCs with high IDRs, the latter reflecting rate-limiting RNA splicing that negatively impacts mRNA production. Mirroring gene expression differences in tumors and PBMCs, over-represented protein-coding gene RNAs in IBC patient plasma were largely intronic RNAs, while those in non-IBC patients and healthy donor plasma were largely mRNA fragments. Potential IBC biomarkers in plasma included T-cell receptor pre-mRNAs and intronic, LINE-1, and antisense RNAs. Our findings provide new insights into IBC and set the stage for monitoring disease progression and response to treatment by liquid biopsy. The methods developed for parallel transcriptional and post-transcriptional gene regulation analysis have potentially broad RNA-seq and clinical applications.

Indexed as

eosinophilsgranulocyteshypertranscriptionneutrophilsretroelementsreverse transcriptaseRNA sequencingT cell receptors

Identifiers

PMID37398275
PMCPMC10312853
OpenAlexW4378757586

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.