ISSN 0006-2979, Biochemistry (Moscow), 2025, Vol. 90, No. 6, pp. 683-699 © Pleiades Publishing, Ltd., 2025.
Russian Text © The Author(s), 2025, published in Biokhimiya, 2025, Vol. 90, No. 6, pp. 733-751.
683
REVIEW
Transcriptional Biomarkers in the Diagnosis
of Genetic Disorders: Opportunities, Challenges,
and Prospects for Application
Lidia N. Nefedova
1,a
* and Tatiana N. Krasnova
2
1
Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia
2
Faculty of Fundamental Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia
a
e-mail: nefedova@mail.bio.msu.ru
Received February 11, 2025
Revised April 17, 2025
Accepted May 21, 2025
Abstract— Quantitative analysis of gene transcription is widely used across various fields of biology and, in
particular, in medicine, it serves as a tool for diagnostics and transcriptomic profiling of diseases. In recent
years, transcriptome analysis methods based on large-scale next-generation sequencing have become widely
adopted. Transcriptomic studies enable the identification of cellular processes that are active at specific time
points, the investigation of transcriptome dynamics in different tissues or physiological states (such as during
ontogenesis or adaptive responses) and the detection of differentially expressed genes in pathological con-
ditions. A pronounced change in the transcription level of one or more genes under pathological conditions
may be sufficient for diagnosis, serving as a transcriptional biomarker of disease. However, in some cases,
altered transcription levels may indicate the presence of mutations, including those leading to disruption of
splicing, activation of mobile elements, or pseudogenes. This review discusses cases in which transcription-
al changes can provide insights into the genetic causes of disease, as well as the challenges that must be
considered when using transcription as a diagnostic marker. In the future, specialized targeted panels based
on transcriptome analysis are expected to be used not only as diagnostic and prognostic tools, but also as
predictors of structural genomic abnormalities, thereby contributing to the development of novel strategies
for effective disease treatment.
DOI: 10.1134/S0006297925600383
Keywords: genetic diseases, transcription, biomarkers
* To whom correspondence should be addressed.
INTRODUCTION
Genetic disorders are caused by dysfunctions of
the cellular genetic apparatus, often resulting from
mutations in specific genes. To date, several thousand
genes associated with more than 7500 monogenic
inherited diseases [1] have been described, and the
Human Gene Mutation Database (HGMD) contains in-
formation on over 200,000 pathogenic gene variants
(alleles) [2]. Nevertheless, the causes of many diseas-
es (mainly polygenic and multifactorial ones) remain
unclear.
A major obstacle in identifying the causes of
polygenic diseases is the substantial genetic diversity
of the human population. The 1000 Genomes Project
revealed that individual genomes differ from the ref-
erence genome by approximately 4.1 to 5 million sin-
gle nucleotide polymorphisms (SNPs) [3]. The dbSNP
database (https://www.ncbi.nlm.nih.gov/snp/) contains
information on over 25 million distinct SNPs, of which
only about 300,000 are located in exons, while the
others are found in non-coding DNA regions and are
most likely functionally neutral. According to the Ex-
ome Aggregation Consortium (ExAC) Project, which
provides data on exome sequences from over 60,000
individuals, the SNPs’ majority of coding regions are
also predicted to be functionally neutral[4]. However,
in practice, predicting which of the identified genet-
ic variants are neutral and which may contribute to
disease development is a complex task. Some neutral
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Fig. 1. Environmental and molecular-genetic mechanisms that may influence gene transcription levels. Designations: LOF,
loss-of-function; GOF, gain-of-function; NMD, nonsense-mediated mRNA decay; TF, transcription factor; ME, mobile element.
variants may exhibit pathogenicity only under certain
conditions, including environmental influences and/or
interactions with other genetic variants.
To identify associations between SNPs and poly-
genic diseases, researchers use methods based on
linking candidate genetic variants with mutant phe-
notypes, including genome-wide association studies
(GWAS) [5]. GWAS typically involve searching for cor-
relations between genotype variants (that is usually
SNPs) and certain disease. This approach enables the
estimation of disease risk (genetic predisposition) in
individuals carrying specific gene variants (i.e., partic-
ular SNPs) compared to individuals who do not carry
these variants.
On the other hand, gene dysfunction may result
not from structural alterations in the gene itself, but
from disruptions in its expression. Therefore, one ap-
proach to study mutant phenotypes involves analyz-
ing changes in the transcription of specific candidate
genes or performing a global analysis of alterations at
the transcriptome level. Transcription is an indicator
of gene function that is difficult to interpret. First, it
represents an initial (but not the only) stage in the
regulation of gene expression. Following transcription,
additional regulatory mechanisms become activated.
Disruptions at any stage of gene expression regulation,
not necessarily at the transcriptional level, can affect
gene function and lead to phenotypic changes. Second,
transcription is a complex multistep process that in-
volves both epigenetic regulation (chromatin remod-
eling, histone protein modifications, DNA methylation)
and direct transcriptional regulation itself. The latter
occurs through cis-regulatory gene sequences, such as
enhancers, silencers, or insulators, as well as through
trans-regulation by transcription factors (TFs). Third,
transcription is significantly influenced by environ-
ment, which may include both external factors (abi-
otic or biotic) and internal ones (cell or tissue type,
age, sex, and other physiological parameters) (Fig. 1).
Nevertheless, in some cases, transcription can
serve as an informative indicator of specific struc-
tural alterations in the genome. Figure 1 shows the
main molecular-genetic factors that may affect gene
transcription levels: mutations, particularly those that
alter splicing, as well as the transcriptional activity of
mobile elements and pseudogenes.
To determine how gene transcription changes un-
der the influence of various genetic and non-genetic
factors, large-scale transcriptome analysis is now com-
monly used. Transcriptomic studies (RNA sequencing,
RNA-seq) enable to identify which cellular processes
are active at the time of RNA extraction [6]. One of
the key advantages of RNA sequencing is its ability to
reveal transcriptome dynamics across different tissues
or states, such as during ontogenesis or physiological
adaptation. It can also be used for comparative tran-
scriptome analysis of biomedical samples obtained
from diseased and healthy tissues.
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The quality and predictive power of transcriptom-
ic studies are influenced by numerous factors, since
transcription is a labile process that enables cells to
rapidly adapt to external and internal, environmen-
tal or physiological changes. Some genes (for exam-
ple, housekeeping genes) are transcribed in a stable
and condition-independent manner, showing minimal
variation in transcription levels. Others are highly
sensitive to environmental changes and exhibit sub-
stantial variability in transcription depending on the
conditions.
A pronounced change in gene transcription levels
under pathological conditions may be sufficient for
diagnosis, serving as a transcriptional biomarker of
disease. However, transcriptomic studies alone are
not sufficient to fully understand the mechanisms
underlying transcriptional alterations. In this context,
combining genome-wide association studies (GWASs)
with transcriptome-wide association studies (TWASs)
has proven to be an effective approach [7]. The first
approach (GWAS) focuses on identifying associations
between SNPs and pathological genotypes, while the
second (TWAS) aims to detect associations between
gene transcription levels and disease, as well as to
search for gene networks whose function is disrupted
in disease. In the following sections, we will examine
in detail the circumstances under which transcription
levels may be useful for elucidating the genetic basis
of disease, and the factors that must be taken into
account when using transcription as a diagnostic tool.
We will also discuss the challenges that complicate the
interpretation of transcriptomic data.
GENE TRANSCRIPTION AS A BIOMARKER
OF GENETIC DISORDERS: OPPORTUNITIES
Transcription levels can serve as indicators
of both loss-of-function (LOF) and gain-of-func-
tion (GOF) mutations. LOF mutations include: non-
sense-mediated mRNA decays (NMDs), which com-
pletely stop protein synthesis; missense mutations
that negatively affect protein activity or stability;
mutations in regulatory regions that impair gene ex-
pression[8]. Approximately one-third of mutant genes
identified in monogenic and cancer-related disorders
carry nonsense or frameshift mutations that result in
the formation of premature termination codons [9].
The decay of mRNAs containing nonsense mutations
is a cellular process that eliminates mRNA transcripts
carrying premature stop codons, thereby preventing
the synthesis of truncated and potentially danger-
ous proteins [10]. This mechanism ensures that only
mRNAs capable of producing full-length proteins are
translated. By reducing the levels of defective mRNAs,
NMD decreases the expression of truncated proteins,
some of which may exert dominant-negative effects,
and results in the suppression of mutant allele tran-
scription.
It is known that approximately 10% of patients
with cystic fibrosis are homozygous for a mutation in
the CFTR gene that introduces a premature termina-
tion codon (PTC). At the same time, the mRNA levels
of CFTR contain a nonsense mutation are significantly
reduced compared to wild-type CFTR mRNA not only
in homozygous patients but also in healthy hetero-
zygous carriers of the mutation [11]. It has been re-
vealed that CFTR mRNA levels in cell lines harboring
various nonsense mutations (Y122X, G542X, R1162X,
and W1282X) are decreased by 50-80% relative to
wild-type CFTR mRNA levels in the parental cell lines
[12-14]. Other examples of transcript reduction due to
NMD mechanism involve the following genes: JAG1 (in
Alagille syndrome), DYRK1A (in intellectual disability),
and ZIC2 (in holoprosencephaly); the involvement of
the above-mentioned genes in haploinsufficiency dis-
orders has been reported. To systematically investigate
factors determining the efficiency of NMD in cancer, a
database of somatic nonsense mutations in genes from
9769 cancer patients was constructed and integrated
with mRNA expression data from The Cancer Genome
Atlas (TCGA) [15]. Thus, decreased gene transcript lev-
els may indicate the presence of nonsense mutations
that affect transcription.
It is important to note that LOF mutations have
different phenotypic consequences depending on
whether a gene’s function is dose-dependent or dose-
independent. Recessive LOF mutations are typically
found in genes encoding metabolic enzymes, suggest-
ing that their function is not dependent on gene dos-
age. Genes with limited variability in transcript lev-
els are known as dosage-sensitive genes [16]. Genes
encoding regulatory proteins, transcription factors,
receptors, or their ligands are dosage-sensitive. LOF
mutations in these genes are usually dominant, since
a single functional allele expression performance is
insufficient to maintain normal operation. Functions,
provided by the housekeeping genes, are also dosage-
sensitive, and LOF mutations in these genes are gen-
erally dominant [17, 18].
Gain-of-function (GOF) mutations can lead to var-
ious alterations at the protein level, such as consti-
tutive synthesis, substrate change, disruption of tar-
get-binding specificity, or protein aggregation [2]. All
of the above make GOF mutations critically important
in the development and progression of various cancer
types. While most LOF mutations are localized within
structured protein domains, GOF mutations are more
frequently found in intrinsically disordered regions
(IDRs) of proteins [19]. As a result, mutations in IDRs
often disrupt molecular interactions and consequently
affect signaling pathway function.
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More than 90% of all variants associated with
genetic diseases have been shown to be located in
non-coding regions of the genome [20]. However, mu-
tations in non-coding regions can affect gene func-
tion by disrupting the interaction between TFs and
their binding sites in regulatory regions of genes [21].
As a result, LOF mutation may occur due to the loss
of a binding site, or GOF mutation may arise due to
the emergence of a new binding site (Fig. 1).
In the study by Fuxman Bass et al. [22], the au-
thors investigated the impact of point mutations in
non-coding regions on transcription factor (TF) binding
to enhancers using a yeast-based system: most of the
mutations resulted in a loss of interaction, but sever-
al dozen led to enhanced binding. Another example
demonstrating how mutations in non-coding regions
can affect gene function is provided by mutations in
the promoter of the telomerase reverse transcriptase
(TERT) gene, which are frequently observed in vari-
ous types of cancer [23]. Two independent mutations
in the TERT promoter were identified that generated
de novo consensus binding motifs for the ETS tran-
scription factor, leading to a 2-4-fold increase in the
transcriptional activity of the gene [23].
Pathogenic GOF mutations in the STAT1 gene are
typically associated with elevated levels of phosphor-
ylated STAT1 transcription factor, as well as increased
transcription level of the gene itself. In the study by
Zimmerman etal. [24], STAT1 mRNA levels were ana-
lyzed in blood cells from healthy donors and patients
carrying GOF mutations in the STAT1 following in-
duction with interferons γ and α. The results showed
that the median transcription levels of STAT1 were
approximately three times higher in patients than in
healthy donors.
Changes in transcriptional levels may indicate
the presence of a mutation in an enhancer [25]. Most
genes are regulated by more than one enhancer, and
many enhancers control the transcription of multi-
ple genes [26, 27]. Systematic analyses have provided
evidence for the emergence of new gene functions
through enhancer “reprogramming” during evolution,
which occurs via the acquisition of novel transcrip-
tion factor binding sites [28]. Super-enhancers are
large clusters of enhancers, and disease-associated
SNPs have been shown to be particularly enriched
in super-enhancers of oncogenes in cancer cells [29].
For example, mutations in the super-enhancer of the
TAL1 gene, which is associated with T cell acute lym-
phoblastic leukemia, result in the emergence of MYB
transcription factor binding site, leading to TAL1 gene
overexpression in the tumor [30].
Mutations in non-coding regions can also af-
fect gene expression by disrupting interactions with
microRNAs. For example, a mutation in the non-
coding region of the E2F1:MIR136-5p locus disrupts
microRNA-mediated regulation, resulting in increased
activity of the E2F1 oncogene in colorectal cancer [31].
The SomamiR 2.0 database (http://compbio.uthsc.edu/
SomamiR) contains data on somatic GOF mutations
identified in cancer that potentially alter interactions
between microRNAs and competing endogenous RNAs
(ceRNAs), including mRNAs, circular RNAs (circRNAs),
and long non-coding RNAs (lncRNAs) [32].
Thus, changes in gene transcription levels may
indicate LOF or GOF mutations occurring not only
within the coding sequences of these genes but also in
regulatory regions, such as transcription factor bind-
ing sites or enhancers.
Transcription level as an indicator of muta-
tions contributing to splicing defects. Splicing-dis-
rupting mutations are a common cause of monogen-
ic diseases. It has been estimated that up to 60% of
all pathogenic SNPs may lead to splicing defects [33].
Point mutations within exons can disrupt the function
of exonic splicing enhancers (ESEs) or produce new
exonic splicing silencers (ESSs). It has been found that
approximately 10% of ~5000 known pathogenic mis-
sense variants result in exon skipping [34]. If exon
skipping does not result in a frameshift, the transcript
may be translated into protein, and the pathogenici-
ty of such an event is not always evident [35]. Con-
versely, a frameshift can provide to the formation of
a stop codon, triggering NMD mechanism (Fig. 1).
Since splicing is highly tissue-specific [36], the tis-
sue selection for analysis is of critical importance in
mutation detection. A mRNA of the same gene can be
spliced differently across tissues. Age-related changes
in splicing include alternative splicing of aging- related
genes, as well as alterations in the expression levels
of core spliceosome genes and splicing regulatory fac-
tors [37].
Transcription and splicing are closely intercon-
nected. Splicing provides feedback on transcription
initiation, influencing the gene’s transcriptional pro-
file. A recently described phenomenon, exon-mediat-
ed activation of transcription starts (EMATS), demon-
strates that splicing of internal exons can regulate
transcription initiation and activate cryptic promoters
[38]. Genes containing EMATS have been shown to be
linked to numerous genetic diseases (neurodevelop-
mental disorders, immunodeficiency, cancer, deafness,
and others) [39]. Thus, transcription can be modulat-
ed via the efficiency of internal exon splicing, and
changes in transcription levels may indicate splicing
mutations (Fig. 1).
Transcription of mobile elements as a marker
of genetic disorders. Mobile elements (MEs) are an
essential component of the human genome, accounting
for approximately half of its content, with the majority
presented by retrotransposons [40]. ME transposition
is a potentially deleterious operation that can lead
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to genomic rearrangements. In addition, rearrange-
ments may also result from recombination process-
es between ME copies. Although many MEs have lost
their transpositional activity over the course of evolu-
tion, individual copies of Alu, L1, SVA, and human en-
dogenous retroviruses (HERVs) remain transcriptional-
ly active, and some of these copies are still capable of
transposition within the human genome [41].
Altered transcriptional profiles of mobile ele-
ments and their impact on nearby genes have been
observed in various diseases, including cancer and
neurodegenerative disorders [42]. Through their own
promoters, MEs can either suppress or enhance the
expression of neighboring genes (Fig. 1). For exam-
ple, in Hodgkin’s lymphoma, transcription of the pro-
to-oncogene CSF1R is initiated from the long terminal
repeat (LTR) of a THE1B element, a member of the
MaLR LTR-retrotransposon family [43]. Activation of
THE1B transcription may serve as a potential diagnos-
tic and/or prognostic marker for Hodgkin’s lymphoma.
The RNA sequencing data from The Cancer Ge-
nome Atlas (http://cancergenome.nih.gov/) have been
used to quantitatively assess ME expression in col-
orectal cancer. As a result, ME expression was shown
to function as a prognostic marker for patients with
colorectal cancer [44].
Many human epithelial cancers, especially those
associated with TP53 mutations, are characterized by
elevated expression of L1 [45, 46]. Increased L1 ex-
pression has been reported in ovarian, esophageal,
colorectal, lung, breast, and pancreatic cancers, and
it correlates with disease severity [47, 48]. The data
evidence that L1 expression analysis can be applied
as a prognostic tool.
Approximately 80% of all long non-coding RNAs
(lncRNAs) in the human transcriptome contain MEs’
sequences [49]. Evidence suggests that ME-derived
lncRNAs are involved in melanoma progression
[50], contribute to tumor progression, metastasis, or
chemoresistance in breast cancer [51], pancreatic can-
cer, and hepatocellular carcinoma [52].
The examples described above demonstrate that
the analysis of transcriptional activity of MEs can
use as a diagnostic and prognostic marker for var-
ious cancers. Moreover, MEs are also markers of
age-related changes. In humans, HERV-K (HML-2) and
HERV-W provide differential expression patterns in
young and elderly individuals [53]. The expression
of HERV-H and HERV-W has been shown to correlate
significantly with age [54]. In particular, HERV-W ex-
pression markedly increases in individuals over the
age of 40 years, that is a range that coincides with
the development of neurodegenerative diseases, such
as multiple sclerosis. Data about ME transcription
levels may also facilitate a diagnosis of inflammatory
brain disorders [55]. Several studies have shown that
HERV-H, HERV-K, HERV-L, and HERV-W are activated
in Alzheimer’s disease [56]. Moreover, HERV-H expres-
sion is significantly elevated in patients with autism
spectrum disorders (ASD), particularly in individuals
with severe disease progression [57]. Thus, analysis
of HERV-H transcription levels may provide a prom-
ising marker for ASD diagnosis; however, the authors
emphasize that additional studies on larger patient
sampling are required to confirm this hypothesis.
Thus, the overall level of ME transcriptional activ-
ity contributes to biodiagnostic management of many
genetic disorders, especially cancers. Since ME tran-
scription is strictly regulated by the host genome, dis-
ruptions in genome function are estimated to underlie
the changes in ME expression levels. However very
few studies have been carried out to date.
Pseudogene transcription as a marker of ge-
netic disorders. Pseudogenes were historically con-
sidered merely nonfunctional copies of protein-cod-
ing genes that had lost their protein-coding capacity
due to the accumulation of deleterious mutations.
However, while the majority of human pseudogenes
are indeed nonfunctional, approximately 20% exhibit
transcriptional activity, and some are even capable of
producing protein products [58]. With the advent of
high-throughput sequencing technologies, thousands
of pseudogenes have been identified and implicated
in the etiology and pathogenesis of various diseases.
Increasing evidence suggests that pseudogenes are
integral components of the complex regulatory net-
works that control gene expression [59].
Some pseudogenes contribute to the regulation
of gene expression and therefore should be consid-
ered as “functional” genes (Fig. 1). Recent studies have
demonstrated that pseudogene RNAs can enhance
the transcription of their parental genes by compet-
ing for binding with regulatory microRNAs, thereby
alleviating microRNA-mediated repression of target
genes [60].
Most pseudogenes are co-expressed with their
parental genes, and their expression is critical for
the function of the parental genes. For example, loss
of PTENP1 function, a processed pseudogene of the
phosphatase and tensin homolog gene (PTEN), can
lead to a significant decrease in PTEN transcription
levels [61]. It has been shown that both PTEN and
PTENP1 can be deleted in melanoma [62], indicating
that the functions of both the parental gene and its
pseudogene are necessary under normal conditions.
Conversely, some pseudogenes exhibit ex-
pression patterns that differ entirely from those
of their parental genes. A systematic analysis of
pseudogene transcription revealed that pseudogenes
are transcribed differentially depending on their
presence in cancerous or normal tissues [63]. Some
pseudogenes can be classified as cancer-specific [64].
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Depending on certain pathological conditions, these
pseudogenes produce unique expression profiles,
which are considered to be potential biomarkers
for clinical application. For instance, transcription
of SUMO1P, a pseudogene of the ubiquitin-like modi-
fier 1 gene SUMO1, is significantly elevated in gastric
cancer tissues compared to adjacent non-tumorous
tissues, and its expression level correlates with tu-
mor size, differentiation, lymphatic metastasis, and
invasion [65]. Expression of the pseudogene INTS6P1,
derived from the integrator complex subunit 6 gene
INTS6, is significantly reduced in the plasma of
patients with hepatocellular carcinoma compared
to healthy individuals [66]. The transcript levels of
FTH1P3, a pseudogene of the ferritin 1 heavy chain
gene FTH1, are increased in cell lines and tissues of
uveal melanoma [67]. Moreover, it has been shown
that expression of the Foxo3 gene, which encodes a
forkhead family TF, is regulated by its pseudogene
Foxo3P [68]. Ectopic expression of Foxo3P, circular
RNA Foxo3, and Foxo3 mRNA has been demonstrated
to suppress tumor growth, as well as cancer cell pro-
liferation and survival.
In addition to cancer, changes in pseudogene
expression levels have been observed in various
other pathological conditions, e.g., neurodegenera-
tive diseases [69], cardiovascular diseases [70], and
diabetes [71]. Therefore, pseudogene transcription is
suggested to be highly informative diagnostic bio-
marker for these disorders. Despite the recent iden-
tification of numerous pseudogenes, researchers typ-
ically focus only on the expression of their parental
genes, excluding transcription of the corresponding
pseudogenes. However, accounting for pseudogene ex-
pression is essential for accurately measuring paren-
tal gene transcription levels and for determining the
contribution of pseudogenes to overall transcriptional
activity. This consideration is particularly important
when selecting transcriptional biomarkers.
Transcription analysis can be useful for identi-
fying gene networks and discovering genetic mod-
ifiers. Genes always function in interaction with oth-
er genes that influence their activity to some extent,
forming genetic networks. Therefore, even monogenic
diseases can have diverse genetic causes, which ex-
plains their genetic heterogeneity and variable phe-
notypic manifestations.
Genetic modifiers form a group of genes that can
alter the phenotypic effects of disease-causing genes.
They may affect the expression of genes exhibiting
haploinsufficiency or modify the phenotype in haplo-
insufficiency contexts. A significant allelic imbalance
in transcription, observed for 88% of genes in hu-
man tissues, is presumably caused by genetic modifi-
ers [72]. In dominantly inherited diseases caused by
haploinsufficiency, allelic imbalance can either en-
hance the expression of the normal allele, compen-
sating for haploinsufficiency, or reduce its expression,
thereby exacerbating the condition [73, 74].
Genetic compensation of mutant allele expres-
sion can be achieved either through the presence of
additional gene copies in the genome, where loss of
function of one gene is compensated by the activi-
ty of other genes with similar functions, or through
changes in the expression pattern of the single nor-
mal allele, as demonstrated in several model organ-
isms [75]. This process, known as transcriptional ad-
aptation, modulates the expression of compensatory
genes, thereby preventing or reducing the severity of
the mutant phenotype [76]. For example, knockout or
knockdown of the histone deacetylase 1 gene (HDAC-1)
leads to increased expression of its homolog, HDAC-2,
and vice versa, as shown in several cell lines and in
both human and mouse tissues [77, 78].
Numerous examples have been described demon-
strating the influence of genetic modifiers on disease
severity, with a particularly large body of research fo-
cused on identifying genetic modifiers of cystic fibro-
sis progression [79]. A study was conducted to inves-
tigate the association of phenotypic manifestations of
cystic fibrosis in patients homozygous for the F508del
mutation with transcription levels and allelic variants
of the STAT3, IL1B, and IFNGR1 genes [80]. The in-
teraction of the products of these genes determines
the balance between inflammation, antiviral defense,
and tissue repair: STAT3 encodes a transcription fac-
tor of the JAK–STAT signaling pathway; IFNGR1 en-
codes the receptor for interferon-γ, which activates
the JAK–STAT pathway; IL1B encodes the pro-inflam-
matory cytokine IL-1β, which activates the NF-κB
pathway. Expression of all three genes was elevated
in patients with cystic fibrosis, and the data demon-
strated associations between allelic variants of STAT3,
IL1B, and IFNGR1 (determining their transcription
levels) and disease severity [80].
A common intronic mutation in the CFTR gene is
the c.3718-2477C>T variant, which is one of the most
frequent mutations in the Polish cystic fibrosis patient
population [81]. Patients carrying this mutation often
exhibit a mild disease phenotype. It has been found
that disease severity inversely correlates with a spe-
cific type of splicing transcript that facilitates the res-
toration of protein function. Studies have shown that
increased expression of the splicing factors HTRA2-β1
and SC35 in the presence of the c.3718-2477C>T mu-
tation promotes correct splicing of CFTR pre-mRNA,
highlighting the role of splicing regulation as a sig-
nificant modifier of cystic fibrosis clinical progression
in the context of intronic mutations [82].
Thus, transcriptomic data can be used to identi-
fy genetic modifiers associated with specific genetic
disorders.
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GENE TRANSCRIPTION
AS A BIOMARKER OF GENETIC DISEASES:
ACTUAL CHALENGES
Not all LOF and GOF mutations lead to altered
transcription level. As noted above, the NMD path-
way eliminates mRNA transcripts carrying premature
stop codons. However, transcripts containing nonsense
mutations located within the last 50-55 nucleotides of
the penultimate exon or within the final exon may
be able to avoid NMD action [83]. For instance, in the
SOX10 gene, which encodes a transcription factor in-
volved in neural crest development, some nonsense
mutations arise outside the regions typically trigger
NMD. As a result, these mutant transcripts escape deg-
radation and produce truncated proteins with domi-
nant-negative activity, leading to severe neurological
disease [84]. Conversely, nonsense mutations in SOX10
that occur within NMD-targeted regions result in rec-
ognition and degradation of the mutant transcripts,
causing a milder phenotype due to haploinsufficiency
[84, 85]. Thus, while reduced transcript levels may in-
dicate gene inactivation in the case of LOF mutations,
a presence of a LOF mutation by itself does not nec-
essarily lead to decreased transcription.
Moreover, the efficiency of the NMD pathway
may vary between different cell types. This has been
demonstrated in Schmid metaphyseal chondrodyspla-
sia, which is linked to a nonsense mutation in the
COL10A1 collagen gene [86]. In patients with this con-
dition, the mutant mRNA is effectively degraded by
the NMD mechanism in chondrocytes, but is poorly
degraded (or not degraded at all) in lymphoblasts and
osteoblasts. However, in this particular case, the issue
about the cell type-specific differences in NMD effi-
ciency remains to be elucidated [86].
More than 20% of LOF variants have been demon-
strated to be located in exons that are frequently
skipped during splicing and, therefore, do not pro-
vide a mutant phenotype [87]. In monogenic cardio-
myopathies caused by LOF variants in the titin gene
(TTN), transcript-level analysis revealed that nonsense
mutation variants are predominantly found in exons
that are absent in the most highly expressed alterna-
tive transcripts. Consequently, these variants do not
produce the deleterious phenotypic effects typically
associated with nonsense mutations [88].
Blood cell transcription is a convenient but not
obligatory informative diagnostic marker. Numer-
ous studies have demonstrated correlations between
the expression of marker genes in blood cells of pa-
tients and both the presence of tumors and disease
severity. For instance, blood cell transcriptome pro-
filing has been used for the early diagnosis of col-
orectal cancer [89], resulting in the development of a
targeted expression panel based on the transcription
of 29 genes. Such an advance proved to be valuable
for testing asymptomatic cases and predicting dis-
ease severity. Another research, aimed at the blood
transcriptome of patients with metastatic renal cell
carcinoma (when some patients are characterized by
absence of immune response to checkpoint inhibitors),
provide to identify a minimal gene set of 14 tran-
scripts that changed in response to treatment. A gene
expression panel was proposed that can accurately
classify responders to therapy [90]. Similarly, tran-
scriptional biomarkers based on the analysis of blood
cell transcriptomes have also been suggested for lung
cancer diagnosis [91].
In addition to cancer, various inherited diseases
can be diagnosed through the analysis of transcrip-
tomic alterations in blood cells. For example, tran-
scriptional biomarkers for Parkinson’s disease have
been proposed based on gene expression data from
blood, with 29 candidate genes identified for diagnos-
tic purposes at the transcriptional level [92].
However, not all genetic diseases can be diag-
nosed solely based on gene transcription profiles in
blood cells. For instance, a study performing RNA
sequencing of whole blood and skin fibroblasts from
115 patients with various phenotypes but no estab-
lished genetic diagnosis found that only 17% of pa-
tients demonstrated a unique transcriptional profile of
a specific gene set associated with a particular disease
[93]. Comparative analysis of transcriptomes from the
two tissues, blood and fibroblasts, showed that fibro-
blasts produced higher and more consistent expres-
sion of disease-associated genes, while only genes re-
lated to immunodeficiency conditions exhibited higher
expression in blood compared to fibroblasts [93].
Moreover, the blood transcriptome cannot be
used to study tissue-specific diseases. For example,
most genes whose regulation is typically disrupted
in muscle disorders are weakly expressed in blood,
suggesting that RNA-seq from blood cells may be in-
sufficient to detect relevant transcriptional changes in
muscle-specific genes [87].
It should also be noted that whole blood is not a
representative material for studying sex-related dif-
ferences, since blood cells contain only 12.9% of all
sex-associated transcripts [94].
Therefore, while transcriptomic analysis of blood
can aid in the diagnosis of certain conditions, it is not
sufficient to serve as a universal diagnostic approach.
Gene transcription levels depend on the envi-
ronment. As described above, gene transcription is
strongly influenced by environmental factors; here,
we highlight only a few of them.
Thousands of genes show age-related transcrip-
tional changes [95]. However, the extent of these
changes and the existence of transcriptional programs
controlling aging remain unresolved issues [96].
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BIOCHEMISTRY (Moscow) Vol. 90 No. 6 2025
Sex is another important factor determining
the level and tissue specificity of gene transcription.
Significant sex-associated differences in gene expres-
sion have been revealed, with genes showing sex-dif-
ferential expression involved in various biological
processes, such as drug and hormone response, em-
bryonic development, tissue morphogenesis, fertiliza-
tion, sexual reproduction, lipid metabolism, and im-
mune response [94].
Humans, like many other organisms, exhib-
it temporal rhythms in gene expression (circadian
rhythms) that regulate daily physiological cycles. The
genetic regulation of circadian rhythms is generally
conserved across all living organisms [97]. Circadian
rhythms of mRNA transcription are followed by the
combined action of an autonomous circadian oscil-
lator, system signals, and other temporal signaling,
such as feeding and fasting cycles [98]. Although it is
commonly believed that about 10% of genes exhibit
cyclicity at the protein production level, nearly 50%
of genes expressed in the liver are characterized by
cyclic mRNA levels [99], therefore, a considerable part
of the rhythmic proteome is assumed to be regulated
at the translational or post-translational level [100].
A recent study investigated sex- and age-dependent
24-hour rhythms of gene transcription across 46 tis-
sues and identified two waves of expression – morn-
ing and evening [101]. These waves are regulated by
factors related to the biological clock, immunity, car-
bohydrate metabolism, and cell proliferation.
It has been shown that changes in gene tran-
scription can be triggered not only by mutations but
also by external stimuli. For example, the increased
transcription of the receptor tyrosine kinase gene
RET is induced not only by the action of the glial
cell line-derived neurotrophic factor (GDNF) and the
GPI-anchored co-receptor GFRα1, but also depends
on the concentration of interleukin IL-8 in the blood
[102]. Increased transcription of the nerve growth
factor receptor NGFR gene can be caused both by
mutations within the gene itself and by systemic in-
flammatory diseases, such as osteoarthritis, psoriasis,
inflammatory and degenerative disorders of the cen-
tral nervous system [103]. Increased expression of the
MTOR gene, which encodes a serine/threonine protein
kinase involved in the regulation of cellular metab-
olism, growth, and cell survival, can be induced by
various inflammatory cytokines (for example, TNF-α)
[104]. It has also been demonstrated that interferon
γ can stimulate the upregulation of the MAPK1 gene,
encoding mitogen-activated protein kinase 1. Such ef-
fect may be due to inflammation that is in progress or
with the administration of interferon-based immuno-
modulatory therapy during genetic testing [105].
The microbiome is an essential source of genet-
ic modification that has a great impact on the host
transcriptome. For example, mutations in the gene en-
coding mannose-binding lectin (MBL) correlate with
more severe progression of cystic fibrosis in chron-
ic Pseudomonas aeruginosa infection [106]. Screen-
ing of commensal bacterial strains from respiratory
tract microbiomes in cystic fibrosis patients identified
strains capable of reducing the severity of inflamma-
tory responses induced by P. aeruginosa [107]. Tran-
scriptomic analysis of a model system of mono- and
co-infection with P. aeruginosa and Streptococcus re-
vealed downregulation of several signaling pathways
involved in inflammatory responses during co-infec-
tion; protective genes of Streptococcus were identi-
fied [107].
Many of the aforementioned factors influenc-
ing transcription may also affect disease penetrance
[108]. Incomplete penetrance can disrupt the inter-
pretation of gene transcription analyses similarly to
the presence of allelic variants, since control groups
may include individuals exhibiting transcriptional
profiles characteristic of the mutant phenotype but
lacking phenotypic expression for various reasons.
Thus, the environment can influence transcrip-
tion; however, although gene-environment interac-
tions are evident, proving them remains extremely
challenging because comprehensive and systematic
collection of data on interactions between the human
transcriptome and environmental factors is currently
practically impossible.
Issues of sample size and reliability of differ-
ential gene expression assessment. Transcriptomic
studies are limited by patient sample size. For rare
diseases, obtaining a sufficient number of samples is
understandably difficult. Even for common diseases,
achieving appropriate sample sizes remains problem-
atic. Due to high costs, many early next-generation
sequencing studies typically included no more than
three replicates per sample [109-112]. More recent
studies have shown that at least 12 biological repli-
cates are needed to reliably detect most differentially
expressed genes (DEGs) [113]. Comparative transcrip-
tomic analyses require large numbers of replicates
due to genomic and transcriptomic plasticity. More-
over, DEG analysis can be complicated by poor re-
producibility of RNA sequencing experiments, which,
in turn, is provided by not only biological but also
technical factors.
An important challenge in RNA sequencing stud-
ies is the detection of genes with low transcription
levels, which requires substantial sequencing depth.
To identify rare transcripts or analyze differential ex-
pression at the isoform level, both sequencing depth
and the number of replicates must be increased.
Using three replicates allows identification of 20-40%
of significantly differentially expressed genes, where-
as detecting 85% of all differentially expressed genes,
TRANSCRIPTIONAL BIOMARKERS IN DIAGNOSTICS 691
BIOCHEMISTRY (Moscow) Vol. 90 No. 6 2025
including those with less than two-fold changes, re-
quires more than 20 replicates [113].
The false discovery rate (FDR) is another critical
factor in RNA sequencing experiments. It has been
shown that the FDR threshold is approximately 2
−r
,
where r is the number of replicates, varying from 0.25
for two replicates to 0.007 for seven replicates [114].
However, the optimal number of replicates for
each experiment may vary due to dependence on
factors, such as dispersion, library size, and the bio-
logical conditions being compared. Obtaining reliable
estimates of dispersion for each gene, which is essen-
tial for DEG analysis, is also complicated by the small
sample sizes that is typical for most RNA sequencing
experiments [115].
As described above, genes with narrow variability
in transcription levels are more suitable as diagnostic
markers, since their use contributes to better discrimi-
nation between the study and the control groups. Such
genes are often housekeeping genes and other haplo-
insufficient genes. However, many housekeeping genes
are expressed at low levels, meaning that changes in
their transcription may fall below the resolution limit
of RNA sequencing methods. Furthermore, a twofold
decrease in gene transcription, which can result from
the action of the NMD mechanism, may also go unde-
tected in transcriptomic analyses.
Moreover, not all environmental conditions can
be assessed through RNA sequencing. Unaccounted
factors contribute to the variability in transcription
levels, which complicates their interpretation. Iden-
tifying the range of transcriptional variability under
normal conditions is essential for reliably detect-
ing expression changes associated with pathological
states. However, accomplishing this requires generat-
ing large volumes of new data, which remains chal-
lenging at present.
GENE TRANSCRIPTION AS A BIOMARKER
OF GENETIC DISEASES: PROSPECTS
Over the past decade, transcriptomics has become
a powerful tool for studying human diseases at the
molecular level. Transcriptomic profiling facilitates
the identification of DEGs that may serve as disease
biomarkers or therapeutic targets, thereby advanc-
ing the development of personalized treatment ap-
proaches.
However, transcription remains a challenging
stage of gene expression to interpret due to its dynam-
ic nature and sensitivity to external factors. Accurate
assessment of gene expression dynamics under nor-
mal conditions is crucial for analyzing transcriptional
changes associated with pathological states. Current-
ly, such information can be obtained, among other
sources, from publicly available databases containing
RNA sequencing data from various human tissues
across different ages and sexes. The most valuable
publicly accessible resource is the Genotype-Tissue
Expression (GTEx) project [116], which provides tran-
scriptomic data from 54 tissues collected from near-
ly 1000 individuals. Within these tissues, expression
quantitative trait loci (eQTLs) have been identified,
showing significant correlations with gene expression
variation.
Since 2021, the Developmental Genotype-Tissue
Expression (dGTEx) project has been initiated to cre-
ate an analytical resource for studying gene expres-
sion regulatory mechanisms during ontogenesis, the
genetic basis of pediatric diseases, and their pro-
gression with age (https://www.genome.gov/Funded-
Programs-Projects/Developmental-Genotype-Tissue-
Expression/). Samples were collected from 120 rel-
atively healthy pediatric donors across three age
groups. Although the sample size is currently limited,
the value of these data is expected to grow as larger
datasets become available.
In the future, with the accumulation of large-scale
genomic and transcriptomic datasets, it will become
possible to use transcription not only as a disease-as-
sociated marker but also to predict the presence
of specific genomic mutations. Transcriptomic analy-
sis serves as the initial step in such studies, enabling
the identification of DEGs.
The choice of tissue for gene expression analysis
is critical. As discussed above, blood is a convenient
material for such analyses. DEGs identified in blood
can be used as biomarkers for various genetic dis-
eases, including cancer. Blood is considered to be the
most favorable tissue for assessing environmental in-
fluences. A recent study analyzing transcriptomes of
blood cells from over 3000 adults, combined with phe-
notypic data, such as medical history, medication use,
lifestyle factors, and body mass index, demonstrated
the outstanding potential of transcriptomic diagnos-
tics [117]. However, it is crucial to consider that gene
expression patterns in blood cells may not accurately
represent those in other tissues.
For this reason, diagnostic assays implemented in
clinical practice are designed to test gene expression
in tumor tissue. For example, in early-stage (I or II)
breast cancer patients whose tumors are hormone
receptor-positive and HER2-negative by histological
and immunohistochemical assessment, the 21-gene
expression assay Oncotype DX is used on tumor spec-
imens [118]. This gene expression analysis enables
prediction of disease course, assessment of recur-
rence risk, and evaluation of whether chemotherapy
will reduce that risk. A similar assay, MammaPrint,
evaluates the expression of 70 genes associated with
breast cancer [119].
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BIOCHEMISTRY (Moscow) Vol. 90 No. 6 2025
Fig. 2. Algorithm for the development of a targeted gene-expression panel for disease diagnosis and prediction of specific
structural variants in the genome.
Developing a transcription-based diagnostic pan-
el requires only the identification of genes whose ex-
pression is reproducibly altered under pathological
conditions. However, to understand the underlying
genetic mechanisms of the pathology, this is not suf-
ficient. The next step must be a comprehensive in-
vestigation of the structural alterations that provide
the observed expression changes. Figure 2 shows an
algorithm for the development of a targeted gene-ex-
pression panel that can be used not only for diag-
nosis but also to predict specific structural variants
in the genome.
Clearly, to elucidate the molecular-genetic mech-
anisms underlying pathology, transcriptional analysis
must be complemented by structural genomic profil-
ing. Ideally, the integration of GWAS and TWAS data
will reveal correlations between genetic variants and
expression changes, and will facilitate the identifica-
tion of modifier genes that critically influence dis-
ease penetrance and may serve as novel therapeutic
targets. Characterizing and analyzing the transcrip-
tion of such modifier genes will advance our un-
derstanding of disease penetrance and broaden our
insight into the architecture and dynamics of gene
networks.
In summary, transcriptomic analysis is a power-
ful tool that can substantially optimize and enhance
diagnostic workflows. It is clear that the accuracy of
differential gene expression assessment will improve
as sample sizes increase, RNA-sequencing studies
expand, and computational algorithms for sequence
data analysis advance. Further integration of genomic
and transcriptomic datasets will enable the develop-
ment not only of diagnostic but also of predictive and
prognostic targeted panels, thereby facilitating novel,
effective strategies for the treatment of genetic dis-
eases.
Abbreviations. DEGs, differentially expressed
genes; GOF, gain-of-function; HERVs, human endoge-
nous retroviruses; LOF, loss-of-function; ME, mobile
element; NMD, nonsense-mediated mRNA decay; SNP,
single nucleotide polymorphism; TF, transcription
factor.
Contributions. L.N.N. and T.N.K prepared the
manuscript; L.N.N. edited the manuscript.
Funding. This work was supported by the MSU
Development Program (project no.23-SH-04-34).
Ethics approval and consent to participate. This
work does not contain any studies involving human
and animal subjects.
Conflict of interest. The authors of this work
declare that they have no conflicts of interest.
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