Page 28 - ITPS-8-1

INNOSC Theranostics and
Pharmacological Sciences Transcriptome-based RNA sequencing

Table 2. (Continued)
S. No. Purpose Study pattern Sample type Methodology Justification Year of References
study
5 Transcriptomic To identify HBEGF+ Synovium Thirteen datasets, Fibroblasts in the synovial 2022 50
RNA-seq was used fibroblasts and ascertain including RNA- fluid are believed to play a
in this study to the number of HBEGF+ seq and single-cell crucial role in controlling
address RA remission fibroblasts in various transcriptomics, were joint homeostasis in RA.
mechanisms, joint conditions (health, used to analyze synovial Using transcriptomic
provide predictive K/BxN serum transfer tissue in 102 patients RNA-seq, the study found
biomarkers, and arthritis (STA), and STA with arthritis, comparing that HBEGF+ fibroblasts
gain a deeper remission), the study gene expression between contribute to RA remission
understanding of the used two single-cell RNA HBEGF+ and HBEGF− and that HBEGF may
role played by distinct sequencing datasets of fibroblasts. be a novel biomarker for
fibroblast populations mouse synovial cells. predicting RA progression.
in the RA process.
6 To ascertain The study was designed Synovium The study examined RNA- To improve models for 2022 51
whether gene–gene to collect 10,537 seq data from 94 patients predicting treatment
interactions in a experimentally confirmed with RA who started response in RA, the
network analysis of gene–gene interactions methotrexate-based study included a unique,
synovial samples using four carefully csDMARD therapy after 6 potent method known
obtained during selected route libraries. months, evaluating gene– as transcripts micRNA-
the early phases After characterizing gene interactions through seq, which leverages
of RA using histologically defined rigorous regression physiologically significant
RNA-seq could pathotypes in early RA analysis. gene–gene interactions
contribute to our using synovial RNA-seq, through gene interaction
understanding of we extracted particular networks.
the pathophysiology gene–gene interaction
of RA and improve networks and utilized
treatment response these synovial-related
in prediction gene–gene networks
models. to predict the response
to methotrexate-based
disease-modifying
antirheumatic drug
(DMARD) therapy. Next,
by statistically evaluating
each network with robust
linear regression models,
the study revealed the
differential interactions
within each network.
7 The aim of this RNA-seq was used Synovium Samples of synovial tissue This study identified and 2022 52
study was to to determine the were collected from nine validated DEGs in synovial
gain a complete transcriptomic patterns patients with RA. Total RNA tissue samples from
understanding of synovial tissue was then extracted from the patients with RA using
of the patterns of specimens from nine synovial tissue. Total RNA transcriptomic RNA-
expression across patients with RA who samples with RIN > 7.0 seq. It also highlighted
the genome in were members of the and 28S/18S ≥ 0.7 were the activity of a subset
synovial tissue East Asian community. subjected to RNA-seq. Then, of chemokine genes and
samples from All identified genes libraries were constructed provided novel insights
patients with RA to were examined using using TruSeq Stranded into the molecular
identify potential gene set enrichment mRNA LT Sample Prep mechanisms of RA
mechanisms analysis (GSEA), and Kit. The Illumina HiSeq pathogenesis. Finally,
regulating the onset DE-seq was used to ×10 platform was used for it identified potential
and progression identify differentially assembling the libraries. The targets for screening and
of RA. expressed genes (DEGs). accuracy of the RNA-seq treatment.
Quantitative real-time technique in detecting
PCR (qRT–PCR) was DEGs was evaluated, and
used to verify the most the expression levels of the
important hub genes. 10 identified hub genes were
quantified using qRT–PCR.
(Cont'd...)
Volume 8 Issue 1 (2025) 22 doi: 10.36922/itps.4449