Functional roles and clinical implications of pseudogenes signatures as emerging biomarkers in cancer

Abstract

Pseudogenes’ possible functions in gene control and illness have now come to light. However, it has been recognized that numerous pseudogenes play a critical role in the regulation of their respective parent genes, and it is well-established that pseudogenes influence oncogenes and tumor suppressors. Moreover, pseudogenes are being emphasized for their possible use as diagnostic cancer and prognostic biomarkers. Defining, contextualizing, classifying, and discussing the evolutionary relevance of pseudogenes are all aspects of their complex nature that this article explores. An examination of the clustering and co-expression patterns of pseudogenes, as well as the potential regulatory tasks they may perform, is undertaken. This review seeks to investigate the implications of pseudogenes in various cancer types. Pseudogenes’ evolution, biogenesis, and classifications, with in silico analysis, will be reported comprehensively. Additionally, the current review will enumerate the potential of pseudogenes as diagnostic biomarkers and their prognostic significance (good vs. bad) in different cancers. The review elucidated their various functions in cancer and concluded the need for further research to fully understand the roles of pseudogenes in health and disease.

Graphical Abstract

Introduction

Pseudogenes, previously thought to be insignificant genetic remnants, are now acknowledged for their important roles in cancer biology. These sequences, which resemble functional genes but are usually non-coding, play an active role in regulating gene expression and can affect tumor development through multiple mechanisms [1]. Pseudogenes are essentially duplicates of protein-coding genes that have accumulated mutations, rendering them unable to produce functional proteins. Historically dismissed as “junk DNA,” recent studies have revealed that many pseudogenes are transcribed into RNA and can influence gene expression, particularly in the context of cancer [2, 3]. They can act as decoys for microRNAs (miRNAs), effectively modulating the availability of these regulatory molecules and impacting the expression of their parent genes [4–6].

Their involvement in cancer is reshaping our understanding of gene regulation and highlighting the complexity of non-coding RNAs (ncRNAs) in oncogenesis [7–9]. One of the primary mechanisms by which pseudogenes exert their influence is through their function as competing endogenous RNAs (ceRNAs) [10, 11]. In this role, they can sequester miRNAs that would otherwise target and degrade messenger RNAs (mRNAs) from protein-coding genes [6, 12, 13]. For instance, the pseudogene Phosphatase and Tensin Homolog 1 (PTENP1) has been demonstrated to bind specific miRNAs that target the tumor suppressor PTEN, thereby regulating its expression and contributing to tumor growth inhibition [14].

This interaction illustrates how pseudogenes can modulate the activity of their parent genes, effectively altering cellular pathways involved in cancer progression. In addition to acting as ceRNAs, many pseudogenes serve as decoys for miRNAs, reducing the availability of these regulatory molecules for their intended targets [15]. This decoy action can lead to increased expression of oncogenes or decreased expression of tumor suppressors, thereby promoting cancer development [15]. Furthermore, some pseudogenes have been shown to produce endogenous small interfering RNAs (siRNAs) that can target and silence other genes implicated in cancer. For example, pseudogene-derived siRNAs have been linked to the regulation of genes associated with hepatocellular carcinoma (HCC), showcasing another layer of complexity in how these sequences contribute to cancer biology [12].

Interestingly, certain pseudogenes possess the ability to encode functional proteins that may mimic or interfere with the functions of their parental genes. Although POU5F1B (POU domain class 5 transcription factor 1B), a processed pseudogene very similar to OCT4, was recently shown to be transcribed in cancer cells, its biological role and therapeutic significance remain unknown. Now, we demonstrate that POU5F1B, which is found on human chromosome 8q24 next to MYC, is often amplified in gastric cancer (GC) cell lines. It was also discovered that POU5F1B, but not OCT4, was highly expressed in GC cell lines and clinical tissues. This finding challenges the traditional view of pseudogenes as non-functional and underscores their potential roles in oncogenic processes [16]. The dysregulation of pseudogenes has been frequently observed across various cancers, suggesting their potential utility as biomarkers for diagnosis and prognosis. The expression profiles of these sequences can provide useful information about tumor behavior and patient outcomes, making them important tools in the realm of precision medicine [17]. As research continues to uncover the intricate networks involving pseudogenes, there is a growing interest in developing novel therapeutic strategies that target these regulatory RNAs.

Pseudogenes biogenesis

Pseudogene biogenesis is facilitated by the fact that the human genome can duplicate and transcribe, leading to multiple gene products. This phenomenon greatly enhances the inheritance of genetic information. There are two main sources of pseudogenes. To begin with, mutations can cause genes to lose their function, particularly their ability to code for proteins, and even transform into pseudogenes. These mutations can occur during DNA duplication or as a result of multiple insertions, deletions, frameshifts, premature stop codons, or splicing errors in the coding or regulatory regions. It is also possible to transform a functioning gene into a “nonfunctional” pseudogene by amassing mutations in certain regions [18, 19]. Second, retrotransposition: random reintegration of reverse-transcribed cDNA into the genome by the formation of an incorrect locus or mutation might result in the biogenesis of a pseudogene that is not functionally adequate. Given the abundance of mutational opportunities presented by high-synthesis and high-metabolism DNA events, as well as the fact that pseudogenes may be more prevalent in reproductive cells than in somatic cells, it is reasonable to assume that pseudogene biogenesis occurs during these times. Pseudogenes are the result of many gene mutations occurring in the human genome all at once [20, 21].

Theoretically, any DNA sequence might potentially give birth to a pseudogene, as mutations are the essential catalyst for their emergence. Nevertheless, pseudogene biogenesis may be impacted by certain factors. Pseudogene biogenesis is likely less prevalent in regions rich in GC nucleotides because GC nucleotides have a deleterious impact on mutation accumulation. Varied types of pseudogenes are often produced by varied coding gene lengths. Mutations are more likely to occur in genes involved in cell division or metabolism, for example, or in highly expressed genes with a high duplication rate, as stated before. Lastly, a pseudogene might originate from either the parental gene or another pseudogene, according to the available evidence. Both the variety of pseudogene biogenesis and new methods for identifying them are shed light on by these results [22–24].

Classification of pseudogenes

The human genome contains hundreds of pseudogenes, which are classified based on their origin and structural characteristics into duplicated (non-processed), processed (retrotransposed), unitary, and polymorphic pseudogenes [25, 26] as presented in Fig. 1.

Fig. 1.

Classification of pseudogenes, based on their origin and structural characteristics, into unitary (A), which arise from direct mutations or alterations within an ancestral functional gene without gene duplication. Loss-of-function changes such as point mutations, frameshifts, or premature stop codons disrupt normal transcription or translation, leading to gene silencing. These pseudogenes exist as single-copy, nonfunctional remnants and inhibit transcription or translation of the original gene. Duplicated (non-processed) (B) generated by unequal crossing-over during meiosis or by gene duplication followed by mutation. After duplication, one gene copy retains its normal function while the other accumulates disabling mutations (e.g., insertions, deletions, or premature stop codons). These pseudogenes retain introns and regulatory sequences similar to those of their parental genes but are transcriptionally inactive or only partially expressed. Third, processed (retrotransposed) (C) is formed via retrotransposition of mature mRNA transcripts. After transcription of a functional gene, reverse transcription by LINE enzymes produces a complementary DNA (cDNA) copy lacking introns and containing a poly (A) tail. The cDNA integrates back into the genome at a new locus, generating an intronless processed pseudogene. These sequences often lack promoters and regulatory regions, rendering them transcriptionally silent or occasionally expressed as noncoding RNAs. And finally, the polymorphic pseudogenes = pseudogene-related allelic variations (D) depict the impact of pseudogenization on allelic function. Mutations or polymorphisms can result in loss-of-function alleles in one or both homologous chromosomes. [cDNA: complementary DNA; LINEs: Long Interspersed Nuclear Elements; mRNA: messenger RNA.]

Processed pseudogene

A processed pseudogene is formed by retrotransposition of mRNA transcripts, unlike the other two. Processed pseudogenes may have poly(A) tails without introns and regulatory sequences and integrate randomly into the genome, making them more likely to be identified in new locations or on separate chromosomes. Mutations in the processed pseudogene may decrease its function since the retrotransposition is not of high fidelity. Processed pseudogenes highlight how genetic material can be disseminated throughout genomes via retrotransposition and may also play roles in regulating gene expression indirectly through RNA-mediated mechanisms, serving as archives for splice variants due to their origin from spliced mRNAs [27]. Mutations in the processed pseudogene may decrease its function since the retrotransposon is not of high fidelity. Because, unlike its parental gene, a processed pseudogene lacks a promoter, transcription relies on its host gene’s regulatory elements [28, 29].

The transcriptional potential of processed pseudogenes is varied and depends heavily on their genomic insertion site. A common model for their transcription occurs when a processed pseudogene integrates into an intron of an active host gene. In this scenario, the pseudogene is transcribed ‘piggy-back’ style, using the promoter and regulatory elements of that host gene [30]. However, this is not the only mechanism for their expression. Transcription is not always dependent on a host gene. Many processed pseudogenes are inserted into intergenic regions and can become transcribed through several alternative mechanisms: cryptic promoters, co-option of regulatory elements and acquisition of new promoters.

Cryptic promoters: The surrounding genomic region where the pseudogene inserts may contain cryptic or weak promoter sequences that can initiate transcription. These pre-existing, but not typically active, sites can be co-opted by the newly inserted pseudogene sequence [28].

Co-option of regulatory elements: The pseudogene may land near an existing, distant enhancer element. Through the three-dimensional looping of chromatin, such an enhancer can come into physical proximity with the pseudogene and activate its transcription [25].

Acquisition of new promoters: Over evolutionary time, mutations can arise in the sequence upstream of the processed pseudogene, creating a de novo promoter that drives its expression independently. This can happen, for example, through the insertion of other transposable elements that carry their own promoter-like sequences [31].

Therefore, while many processed pseudogenes are initially “dead on arrival” because they lack their own promoter, a subset can become transcriptionally active through various genomic and evolutionary events, allowing them to participate in regulatory networks [32].

Duplicated (non-processed) pseudogenes

Duplicated (non-processed) pseudogenes arise from gene-duplication events, such as unequal crossing-over during meiosis or whole-genome duplication. These events create an extra copy of a functional gene that initially retains all its original features, such as introns and exons. Over time, one of the copies accumulates mutations that disrupt its function without affecting the organism’s fitness since another functional copy remains. They maintain the same exon-intron structure as their parental genes, often retain original regulatory sequences such as promoters, and are frequently found near or clustered with their paralogous functional genes on the same chromosome [33]. Non-processed pseudogenes participate in antisense-mediated regulation, whereby pseudogene transcripts hybridize with sense transcripts of the parental gene, influencing mRNA stability, splicing, or translation. Moreover, duplicated pseudogenes can also act as ceRNAs, competing for shared microRNAs and thereby modulating gene expression networks. In some cases, they contribute to epigenetic regulation by recruiting chromatin-modifying complexes that alter DNA methylation or histone modification patterns at specific loci. These combined mechanisms allow duplicated pseudogenes to exert multilayered regulatory effects [34].

Unprocessed pseudogenes typically contain both promoter sequence and introns necessary for transcription to occur, and thus are more likely to be transcribed/translated than processed pseudogenes, which typically lack these features [35]. Duplicated pseudogenes provide insights into how genomes evolve through redundancy and mutation accumulation, and can indicate evolutionary relatedness among species if shared across different organisms, serving as indicators of evolutionary history by comparing sequence similarities across species [21].

Unitary pseudogenes

Unitary pseudogenes are produced from a single coding gene copy with a few alterations that inhibit transcription or translation. Thus, whereas orthologs can be detected in similar species, the unitary pseudogene has no completely functioning genomic equivalent in the same genome [36].

Unitary pseudogenes form when a single functional gene becomes non-functional due to deleterious mutations without prior duplication. This process does not involve creating an extra copy; instead, it involves disabling an existing gene through mechanisms like indels or nonsense mutations. The original gene itself becomes non-functional, often resulting from point mutations leading to premature stop codons or frameshifts that halt protein synthesis prematurely [37]. Unitary pseudogene formation illustrates how individual genes can lose functionality over time without contributing redundant copies to genomic evolution [38]. Their primary biological effect is the loss of the ancestral gene function, which can lead to species-specific phenotypic traits or altered physiological pathways [26].

Polymorphic pseudogenes

Polymorphic pseudogenes are genes that are present in some individuals within a population but are mutated in others, leading to loss-of-function alleles. These variations can be homozygous for loss-of-function alleles without causing overt pathogenic effects when both copies are inactive. Polymorphic pseudogenes highlight genetic variability within populations and may influence disease susceptibility or phenotypic traits, depending on whether they contribute regulatory functions indirectly, despite being non-functional themselves [12].

The presence of a large number of duplicated pseudogenes supports the concept of gene duplication, a major mechanism for the evolution of new genes and functions. When a gene is duplicated, one copy is free to accumulate mutations and potentially evolve a new function, while the other copy retains the original function. This process gives rise to paralogous genes, which are genes within the same species that have evolved from a common ancestral gene through duplication [39].

However, it is important to distinguish this from the evolution of orthologous genes. Orthologs are genes in different species that have evolved from a common ancestral gene through speciation events. In this case, the gene diverges after the two species have separated, and it retains the same function in both species [40].

Furthermore, evolution is a complex process that is not driven by a single force. In addition to gene duplication and natural selection, stochastic processes such as genetic drift also play a significant role in shaping the genome. Therefore, while gene duplication is a powerful engine for generating genetic novelty, it is one of several key forces driving evolution.

Pseudogenes’ mechanisms of action (regulatory or not)

Arising from the duplication or mutation of functional genes, pseudogenes are sequences that have lost their protein-coding potential. Their mechanisms of action are diverse, including the transcription into non-coding RNAs that interact with microRNAs (miRNAs), epigenetic regulation, and the modulation of parental gene expression. Their mechanisms of action include transcription into non-coding RNAs and interaction with microRNAs (miRNAs), epigenetic regulation and modulation of parental gene expression [1] (Fig. 2). Besides the regulatory role of pseudogenes, they also exert non-regulatory roles in genetic diversity and evolution insight by their contribution to genetic diversity by participating in processes like antibody generation and antigen variation, as well as pseudogenes help clarify genome evolution and the effects of non-selective processes through their accumulation of mutations and degeneration [1].

Fig. 2.

Mechanisms of pseudogene regulation and function. First, the left panel addresses miRNA sponging (ceRNA mechanism), pseudogene transcripts (psRNAs) competitively bind microRNAs, preventing them from repressing parental mRNAs (e.g., PTEN), thereby enhancing protein expression. Second, pseudogenes contribute to epigenetic regulation by interacting with chromatin-modifying complexes, influencing histone modifications and DNA methylation, and modulating transcriptional activity of parental genes

Third, pseudogenes participate in liquid–liquid phase separation, facilitating the formation of membrane-less organelles and condensates that organize macromolecules and regulate intracellular biochemical processes. Finally, the right panel shows that pseudogenes can generate endogenous siRNAs through Dicer-mediated processing of double-stranded RNA, leading to RNA interference (RNAi) and RISC-dependent degradation of target mRNAs, resulting in gene silencing.

RNA-mediated regulation and interaction with miRNA

MicroRNA sponging means that pseudogene transcripts contain miRNA response elements (MREs), which allow them to bind miRNAs that would otherwise target messenger RNAs (mRNAs) for repression. By sequestering miRNAs, Pseudogenes reduce their availability to suppress parental or other target genes, thereby increasing the stability and expression of these genes [41].

Coding and non-coding RNAs with shared MREs communicate and regulate each other’s expression levels. Pseudogenes participate in extensive regulatory networks where multiple RNA transcripts, including mRNAs and lncRNAs, compete for shared miRNAs. This fine-tuning mechanism is crucial in maintaining cellular homeostasis and responding to environmental changes. For example, pseudogene PTENP1 acts as a ceRNA to protect the tumor suppressor gene PTEN from miRNA-mediated repression [42, 43]. Additionally, ceRNA networks modulate gene expression at the post-transcriptional level by altering miRNA availability, impacting cellular processes like differentiation, proliferation, and disease progression [41].

Pseudogenes can be transcribed in antisense orientation relative to their parental genes. These antisense RNAs hybridize with sense transcripts, either stabilizing them or promoting their degradation. Additionally, antisense pseudogene transcripts can epigenetically target parental gene promoters, influencing transcriptional activity [44].

Epigenetic regulation

Pseudogene-derived non-coding RNAs can influence chromatin states and promoter activity of associated genes, contributing to gene silencing or activation depending on the context. Pseudogenes epigenetic regulation is either by histone modification, chromatin modification, liquid phase separation or DNA demethylation [45] as depicted in Fig. 3.

Fig. 3.

Regulatory mechanisms of action of pseudogenes; Epigenetic regulation: Within the nucleus, pseudogene-derived long noncoding RNAs (lncRNAs) can mediate chromatin remodeling and DNA methylation to regulate the accessibility of chromatin. Pseudogene transcription or lncRNA interaction may promote the transition from condensed to open chromatin states, enabling active transcription. Pseudogenes are also involved in histone modifications, such as acetylation (H3K27ac, H3K9ac) and methylation (H3K4me2, H3K4me3), which modulate transcriptional activity by influencing the recruitment of transcriptional machinery. Transcriptional regulation: Pseudogene sequences may possess their own promoters and transcription start sites (TSSs), allowing RNA polymerase to generate pseudogene-derived transcripts. These transcripts can act as competitive endogenous RNAs (ceRNAs) or lncRNAs, interacting with microRNAs (miRNAs) and mRNAs to alter their stability and translation. By sequestering shared miRNAs, pseudogene RNAs modulate the expression of homologous protein-coding genes. Post-transcriptional regulation: Pseudogene-derived mRNAs may enter the RNA interference (RNAi) pathway. After processing by Dicer, pseudogene RNAs can form small interfering RNAs (siRNAs) that participate in mRNA silencing or mRNA degradation of target genes through complementary base pairing. These interactions fine-tune gene expression and can influence diverse biological processes, including cell differentiation, development, and disease. [LncRNA: long non-coding RNA; Me3: 3 methyl group; miRNA: microRNA; mRNA: messenger RNA; RNAi: RNA interference.]

Histone modification

Pseudogene-associated lncRNA genes exhibit enhancement of histone modifications linked with open chromatin, such as H3K4me3 and H3K4me2, which promote transcription initiation, as well as H3K9ac and H3K27ac, which enhance chromatin accessibility and transcriptional activation near transcription start sites (TSSs) [46].

Chromatin modifying complexes

Pseudogene-associated lncRNA genes exhibit higher chromatin accessibility adjacent to TSSs, as evidenced by ATAC-seq and DNase-seq data. This increased accessibility facilitates transcriptional activation [1]. Elevated binding levels of chromatin modifiers, such as components of the H3K4 methyltransferase COMPASS complex and histone acetyltransferase complexes (CBP/p300, GCN5/PCAF), are found near TSSs of pseudogene-associated lncRNA genes. These complexes shape epigenomic signatures characteristic of active transcription [47]. Pseudogenes can produce antisense RNAs that interact with parental gene promoters, recruiting chromatin remodeling complexes. For example, PTENpg1 antisense transcripts alter histone modifications, such as H3K27me3, at the PTEN promoter, affecting its expression [44].

Liquid phase separation

Liquid Phase Separation (LLPS) is a biophysical process where biomolecules (proteins, nucleic acids) condense into membrane-less organelles or condensates, enabling spatiotemporal control of cellular activities [48]. Pseudogenes influence LLPS, regulating membrane-less organelles like P-bodies and Cajal bodies. These structures impact chromatin organization, transcription, and RNA splicing, contributing to epigenetic regulation [49].

DNA demethylation

Pseudogenes display lower DNA methylation levels near their TSSs compared to non-pseudogene-associated lncRNAs. Reduced methylation prevents silencing and supports active transcription. Together, these epigenetic mechanisms, including DNA demethylation, histone modifications, and chromatin remodeling mechanisms, underscore pseudogenes’ ability to modulate gene expression epigenetically, contributing to development and disease processes [50].

Pseudogene-derived siRNAs and RNA interference

Pseudogenes form regulatory pairs with their parental genes, influencing each other’s expression. Knockdown studies reveal that pseudogene activity can destabilize parental mRNAs or alter their transcription levels. Double-stranded RNAs formed between pseudogene transcripts and parental mRNAs can be processed into small interfering RNAs (siRNAs), silencing the parental genes through RNA interference [51].

Pseudogenes generate siRNAs through the following mechanisms:

1. Formation of double-stranded RNA (dsRNA): Pseudogene transcripts can hybridize with complementary RNA sequences from their parental genes or other homologous pseudogenes, forming dsRNA. This is a critical step for siRNA production [52].

2. Processing by dicer: The dsRNA is processed by the Dicer enzyme into siRNAs. These siRNAs are typically 21–23 nucleotides long and play a role in RNA interference (RNAi) [52].

3. Gene regulation via RNAi: Small interfering RNAs bind to their target mRNAs with full complementarity, leading to their cleavage and degradation. Partial complementarity allows siRNAs to block translation without degrading the mRNA [52].

4. Repression of mobile genetic elements: Some pseudogene-derived siRNAs work alongside Piwi-interacting RNAs (piRNAs) to silence transposons and maintain genomic integrity [52].

Pseudogenes in silico analysis

According to the latest statistics from the GENCODE database (release 45) [53], the human genome contains 14,701 pseudogenes. This number is broken down into different categories: Processed pseudogenes (10,638), unprocessed pseudogenes (3,536), unitary pseudogenes (290) and immunoglobulin/T-cell receptor pseudogene segments (237).

While according to the http://pseudogene.org/ [54] and an overview of human pseudogene families [55], they are 1422 with 8,036 total pseudogenes http://pseudofam.pseudogene.org/pages/psfam/overview.jsf. Moreover, the current PseudoPipe results for Ensembl genome release 90 are provided in the Supplementary Table S1 http://pseudogene.org/Human/.

Pseudogenes families

http://pseudofam.pseudogene.org/pages/psfam/browseFams.jsf.

and via the Psuedogene.org for genome analysis http://www.pseudogene.org/motif/index.php for analysis/listing of chromosomes from 1 to 22, X and Y chromosomes pseudogenes and transcription as well as ribosomal protein (RP) pseudogenes, as well as pseudogenes from molecular evolution of the mitochondrial ribosomal protein (MRP) genes.

Mitochondrial ribosomal protein (MRP) pseudogenes in the human genome

120 human MRP pseudogenes, some of them are presented in the supplementary Table S1.

Cytochrome c (cyc) pseudogenes in the human genome

49 cytochrome c (cyc) pseudogenes http://www.pseudogene.org/human-cyc/methods-flowchart.pdf however, 20 previously identified human cyc pseudogenes in GenBank http://www.pseudogene.org/human-cyc/gb.human.cyc.pseudogenes.htm as appear in supplementary Table S2 and Fig. 4.

Fig. 4.

Chromosome-mapping of human cyt pseudogenes, where bars denote identified pseudogenes (HCP1–HCP49), while magenta dots represent the corresponding functional HSC parental genes, and the white circles represent the centromeres. The pseudogene identifiers (HCP1–HCP49) are positioned adjacent to their approximate chromosomal loci. The figure shows that pseudogenes are widely distributed throughout the genome, with a notable clustering on chromosomes 2, 7, 8, 11, 14, and X, suggesting potential hotspots for pseudogene generation or retention. The spatial proximity of pseudogenes to their parental HSC genes in certain regions may reflect conserved sequence homology or shared regulatory environments

HUGO gene nomenclature committee (HGNC)

https://www.genenames.org/tools/search/#!/?query=pseudogene&rows=20&start=0&filter=locus_type:%22RNA,%20small%20nucleolar%22 pseudogenes classified by gene locus type to 313 protein coding genes and 95 ncRNA subclassified into 13 nuclear ncRNA pseudogenes, 62 for nc transfer RNA, 2 small nucleolar ncRNA as appear in Table 1.

Table 1.

Small nucleolar (sn)RNA pseudogenes

Pseudogene		Gene HGNC ID HGNC	Previous gene name
SNORD3F	C/D box 3 F	52,239	snRNA, C/D box 3 pseudogene 2 RNA, U3 small nucleolar pseudogene 2
SNORD73B	C/D box U73B	30,357	RNA, U73B small nucleolar pseudogene snRNA, C/D box U73B (pseudogene)

HGNC: HUGO gene nomenclature committee

LncRNA pseudogenes

LncRNA pseudogenes refer to lncRNAs transcribed from pseudogenes, which are genomic copies of protein-coding genes that have lost their protein-coding capacity due to mutations or truncations as summarized in Table 2.

Table 2.

LncRNA pseudogenes

LncRNA pseudogene symbol	Name	Chromosomal location
FAM157A	family with sequence similarity 157 member A	3q29
LRRC2-AS1	LRRC2 antisense RNA 1	3p21.3
HCG4	HLA complex group 4	6p22.1
HCG4B	HLA complex group 4B
HCG22	HLA complex group 22	6p21.33
HCP5B	HLA complex P5B	6p21.3
LINC02209	long intergenic non-protein coding RNA 2209	8p12
FAM27E4	family with sequence similarity 27 member E4	9q13
FAM74A7	family with sequence similarity 74 member A7	9p11.2-p11.1
SFTA1P	surfactant associated 1, lncRNA	10p14
RN7SL3	RNA component of signal recognition particle 7SL3	14q21.3
FAM30B	family with sequence similarity 30 member B	15q11.1
FAM30C	family with sequence similarity 30 member C	15q11.2
FAM106C	family with sequence similarity 106 member C	17p11.2
FAM197Y4	family with sequence similarity 197 Y-linked member 4	Yp11.2
FAM197Y5	family with sequence similarity 197 Y-linked member 5
FAM197Y7	family with sequence similarity 197 Y-linked member 7
FAM197Y2	family with sequence similarity 197 Y-linked member 2

https://www.genenames.org/tools/search/#!/?query=pseudogene&rows=20&start=0&filter=locus_type:%22RNA,%20long%20non-coding%22

It is noteworthy to mention that there are 203 immunoglobulin pseudogenes vs. 38 T cell receptor pseudogenes. And, the other 101 pseudogenes, 2 complex locus constituents, 14 immunoglobulin genes, 8 readthrough, 7 T cell receptor genes, and 70 unknown pseudogenes. 7 classes of pseudogenes are present, namely, CUT class homeoboxes and pseudogenes, cytoplasmic transfer RNA pseudogenes, NKL subclass homeoboxes and pseudogenes, POU class homeoboxes and pseudogenes or PRD class homeoboxes and pseudogenes, as well as TALE class homeoboxes and pseudogenes, and ZF class homeoboxes and pseudogenes (Fig. 5).

Fig. 5.

Hierarchical organization of homeobox pseudogene classes and their gene counts

At the center are homeobox genes, the circular figure arrangement emphasizes the relationships among six classes, while the gene counts highlight the relative expansion of each family; where Antennapedia class (ANTP) 67 genes, Paired class (PRD) 122 genes, Pit-Oct-Unc class (POU) 23 genes, Three Amino acid Loop Extension class (TALE) 27 genes, Zinc Finger homeobox class (ZF) 15 genes, and Cut homeobox class (CUT) 9 genes.

Pseudogene signatures in cancer are typically identified through systematic analysis of large-scale transcriptomic datasets such as RNA-seq data from TCGA or GEO. Pseudogene loci are first annotated using established databases including GENCODE and Pseudogene.org, and expression levels are extracted and normalized. Differential expression analysis is then performed to identify pseudogenes that are significantly dysregulated between tumors and normal samples. Prognostic pseudogenes are further evaluated using Kaplan–Meier survival curves and Cox proportional hazards regression, and multigene risk models can be constructed using machine learning approaches such as LASSO regression. Additionally, correlation and network-based analyses, including ceRNA network construction, are employed to infer potential regulatory roles of pseudogenes within cancer-associated pathways. Identified signatures are validated using independent datasets and, where possible, experimental verification to ensure robustness and biological relevance.

Computational pipeline

RNA-seq data were obtained from TCGA and processed using a pseudogene annotation set compiled from established resources. To reduce false assignment caused by sequence homology, only pseudogene exons with sufficient align ability were retained, and multi-mapped reads were filtered out.

The analysis workflow included the following steps: (1) download of RNA-seq and clinical data; (2) pseudogene annotation retrieval from curated databases; (3) read filtering to minimize cross-mapping with parent genes; (4) expression quantification and normalization; (5) differential expression analysis between disease groups or subtypes; (6) feature selection for prognostic or classification models; (7) survival analysis using Kaplan–Meier and Cox regression; (8) network-based interpretation through co-expression and ceRNA analysis; and (9) validation in independent cohorts whenever available. This structured pipeline improves transparency and allows reproducible pseudogene biomarker discovery.

Pseudogenes implications for cancer

Various types of cancer in which pseudogenes play a role are presented in Table 3; Fig. 6 below.

Table 3.

The diagnostic potential of some pseudogenes with clinical evidence

Pseudogene	Ensembl	Chromosomal Location	Number of exons	Cancer type	Expression	Refs.
SUMO1P3	ENSG00000235082	1q23.2	1	Gastric	Up	[108]
PTENP1	ENSG00000293199	9p13.3	1	Gastric, Breast	Down	[112, 124]
INTS6P1	ENSG00000250492	5p13.1		HCC	Down	[104]
KLKP1	ENSG00000290915	19q13.33	5	Prostate	Up	[114]
GBP1P1	ENSG00000290525	1p22.2	5	Cervical	Up	[72]
PTTG3P	ENSG00000213005	8q13.1	1
DUXAP8	ENSG00000206195	22q11.1	8	Oral	Up	[115]
TUSC2P	ENSG00000285470	Yp11.2	-	Esophageal squamous cell	Down	[116]
FTH1P3	ENSG00000213453	2p23.3	1	Esophageal squamous cell	Up	[117]
DUXAP10	ENSG00000292986	14q11.2	8	Esophageal squamous cell	Up	[118]
MT1JP	ENSG00000290662	16q13	3	Glioblastoma	Down	[119]
PDIA3P1	ENSG00000180867	1q21.1	1	Glioblastoma	Up	[120]
ANXA2P2	ENSG00000231991	9p13.3	1	Glioblastoma	Up	[121]
PCNAP1	ENSG00000249065			Breast	Up	[123]
PTTG3P	ENSG00000213005	8q13.1	1	Breast	Up	[82]
CRYβB2P1	ENSG00000291087	22q11.23	6	Breast	Up	[125]
UPAT	ENSG00000291070	17q21.31	1	HCC	Down	[126]
WFDC21P	ENSG00000293515	17q23.1	3	HCC	Down	[127]
GOLGA2P10	ENSG00000290948	15q25.2		HCC	Up	[128]
MSTO2P	ENSG00000203761	1q22	13	HCC	Up	[129]
DUXAP8	ENSG00000206195	22q11.1	8	Pancreatic	Up	[85]

SUMO1P3; Small ubiquitin-like modifier 1 pseudogene 3, PTENP1; Phosphatase and tensin homolog pseudogene 1, INTS6P1; integrator complex subunit 6 pseudogene 1, KLKP1; Kallikrein B1, GBP1P1; Guanylate Binding Protein 1 Pseudogene 1, PTTG3P; Pituitary tumor-transforming 3, pseudogene, DUXAP8; Double homeobox A pseudogene 8, TUSC2P; Tumor suppressor candidate-2 pseudogenes, FTH1P3; Ferritin heavy chain 1 pseudogene 3, DUXAP10; Double homeobox A pseudogene 10, MT1JP; Metallothionein 1 J, Pseudogene, PDIA3P1; Protein disulfide isomerase family A member 3 pseudogene 1, ANXA2P2; Annexin A2 Pseudogene 2, PCNAP1; Proliferating Cell Nuclear Antigen Pseudogene 1, PTTG3P; Pituitary tumor-transforming 3, pseudogene, CRYβB2P1; Crystallin beta B2 pseudogene 1, UPAT; Ubiquitin-proteasome pathway-associated pseudogene, WFDC21P; WAP four-disulfide core domain 21, pseudogene, GOLGA2P10; Golgin A2 pseudogene 10, MSTO2P; misato family member 2, pseudogene

Fig. 6.

Diagnostic potential of pseudogenes in various cancer types. Pseudogenes are shown to be differentially expressed in a cancer-type–specific manner and are implicated in tumor initiation and progression. In gastric cancer, pseudogenes such as PTENP1 and SUMO1P3 are highlighted, while DUXAP8 is associated with oral and pancreatic cancers. In glioblastoma, multiple pseudogenes including MT1JP, PDIA3P1, and ANXA2P2 are represented, whereas TUSC2P, FTH1P3, and DUXAP10 are linked to lung squamous cell carcinoma. For hepatocellular carcinoma, pseudogenes such as WFDC21P, GOLGA2P10, INTS6P1, AKR1B10P, UPAT, and MSTO2P are shown. In hormone-related cancers, KLKP1 is associated with prostate cancer, while LDHAP5 and SDHAP1 are linked to ovarian cancer, and GBP1P1 and PTTG3P to cervical cancer. In breast cancer, several pseudogenes including PCNAP1, PTENP1, RP11-480I12.5-004, PTTG3P, and CRYβB2P1 are indicated. Additionally, RPL7AP28, RP4706A16.3, RPL11551L14.1, and RP11326A19.5 are associated with osteosarcoma. [ANXA2P2: annexin A2 pseudogene 2; CRYβB2P1: crystallin beta B2 pseudogene 1; DUXAP10: double homeobox A pseudogene 10; DUXAP8: double homeobox A pseudogene 10; FTH1P3: ferritin heavy chain 1 pseudogene 3; GBP1P1: guanylate binding protein 1 pseudogene 1; GOLGA2P10: golgin A2 pseudogene 10; INTS6P1: integrator complex subunit 6 pseudogene 1; KLKP1: kallikrein B1 pseudogene; LDHAP5: lactate dehydrogenase A pseudogene 5; MSTO2P: misato family member 2 pseudogene; MT1JP: metallothionein 1 J; PCNAP1: proliferating cell nuclear antigen pseudogene 1; PDIA3P1: high protein disulfide isomerase family A member 3 pseudogene 1; PTENP1: phosphatase and tensin homolog 1; PTTG3P: pituitary tumor-transforming 3 pseudogene; PTTG3P: pituitary tumor-transforming 3 pseudogene; SDHAP1: SDHA pseudogene 1; SUMO1P3: small ubiquitin like modifier 1 pseudogene 3; TUSC2P: tumor suppressor candidate-2 pseudogenes; UPAT: ubiquitin-proteasome pathway-associated pseudogene; WFDC21P: WAP four-disulfide core domain 21.]

Hepatocellular carcinoma (HCC)

HCC continues to be a very dangerous malignant tumor with a high rate of metastasis and recurrence [56–58]. The roles of cancer stem cells (CSCs) in treatment resistance, tumor metastasis, and recurrence have garnered a lot of interest lately. Pseudogenes may control stemness, the intrinsic capacity for self-renewal and progressive differentiation [59], to accelerate the development of HCC, according to research [60].

The genome-wide expression of these pseudogenes has been revealed by developments in bioinformatics and microarray technologies [51]. Because of its increased expression in HCC, especially in cases linked to invasion and metastasis, one of these, ANXAP2, a processed pseudogene closely related to annexin A2 pseudogene, has garnered interest [61]. On the other hand, it has been found that pseudogenes, such as the Double homeobox A pseudogene 10 (DUXAP10), are upregulated in HCC, activating the PI3K/AKT pathway and promoting the growth of hepatic cells. Alongside oncogenic pseudogenes like DUXAP10, which promote growth via PI3K/AKT activation, PTENP1, the PTEN pseudogene, is significantly reduced in HCC. PTENP1 acts as a tumor suppressor ceRNA, binding miR-193a-3p to maintain PTEN levels and block PI3K/AKT signaling. Low PTENP1 levels are linked to larger tumors, advanced TNM stage, decreased overall survival, and higher recurrence rates in HCC patients [62]. Conversely, high levels of Protein disulfide isomerase family A member 3 pseudogene 1 (PDIA3P1) in HCC tissues inhibit the p53 pathway, thereby suppressing apoptosis and promoting the growth of liver cancer cells [63, 64]. Mechanistically, several pseudogenes have been identified as microRNA sponges that aid in the pathophysiology of HCC. For example, RACGAP1P, which is overexpressed in HCC tissues, acts as a miR-15a-5p decoy, thereby activating the Rho/ERK pathway to promote cell proliferation and migration [65]. Tumor cell-resident double homeobox A pseudogene 8 (DUXAP8) functions as a miR-490-5p sponge, which raises BUB1 expression in HCC [66]. OCT4-pg4, another contributor, is overexpressed in HCC cells. It controls OCT4 expression by blocking miR-145, which promotes HCC cell proliferation and carcinogenicity [67]. Due to elevated AURKAPS1 levels in tumor cells, it modulates the miRNA cluster miR-142, miR-155, and miR-182, which, in turn, drives ERK pathway activation and promotes cell motility, migration, and invasion [68]. A 99% identical duplicate of peptidylprolyl isomerase A (PPIA), pseudogene PPIAP22 is present in the nucleus, exosomes, and cytoplasm. Through the CCL15-CCR1 or CXCL12-CXCR4/CXCR7 pathways, its upregulation acts as a sponge for miR-197-3p, promoting immune cell infiltration, particularly macrophage infiltration, and cancer cell metastasis [69].

Breast cancer (BC)

The GBP1 pseudogene, guanylate binding protein 1 pseudogene 1 (GBP1P1), has been identified as a functional pseudogene [70]. There have been reports of GBP1P1 upregulation in nasopharyngeal carcinoma, breast cancer, and cervical carcinoma [71–73]. Furthermore, in early BC, it has been shown that the GBP1P1 expression profile could forecast a full response to chemotherapy [74]. ATP8A2, another pseudogene, was found to be cancer-type specific for breast cancer which was expressed in samples of basal breast cancer but not in luminal samples [75]. As an example of a negatively linked pair, it was discovered that following estrogen treatment, the expression of the KHSRP pseudogene and its parent gene was downregulated and increased, respectively, at the transcript level. A pseudogene can hybridize with the RNA of its parent gene and prevent translation if it is transcribed in the opposite direction of its antisense orientation [25, 76, 77].

Ovarian cancer (OC)

By acting as a ceRNA and promoting HMGA1/2 expression, the pseudogene HMGA1P6 increased malignancy in OC, according to functional and mechanistic research [78]. Crucially, in OC, MYC transcriptionally increased both HMGA1 and HMGA1P6 [79]. RPL10P6, AC026688.1, FAR2P4, AL391840.2, AC068647.2, FAM35BP, GBP1P1, ARL4AP5, RPS3AP2, and AMD1P1 were among the 10 pseudogenes that were included in the prediction model. Functional enrichment analysis results suggested that immune-related biological processes and signaling pathways may be the underlying mechanisms by which these pseudogenes affect cancer prognosis. Analysis of correlations revealed a substantial relationship between risk signature, immune cell infiltration, and immunological score [80]. When it came to controlling hsa-miR-363-3p in OC, the most promising pseudogenes were RPS26P15, AC004057.1, RPS26P31, RPS26P6, RPS26P3, and RPS26P. These pseudogenes should act as oncogenes in ovarian cancer under the ceRNA mechanism. Their expressions were higher in advanced stages of OC than in early stages overall, and they were considerably elevated in cancer tissues as compared to normal controls [81].

Pancreatic cancer (PC)

Finding a molecular marker to identify cancer grade/stage early enough to improve survival is challenging in PC [82]. A lncRNA-derived pseudogene in PC, DUXAP10, is oncogenic and influences cell invasion, apoptosis, and proliferation, hence promoting carcinogenesis [83]. Patients with PC who had higher levels of DUXAP8 expression had larger tumors, more advanced stages of the disease, and shorter overall survival. Furthermore, both in vitro and in vivo, suppression of DUXAP8 expression by siRNA or shRNA induced apoptosis and suppressed the growth of PC cells. According to mechanistic investigations, DUXAP8 partially controls PC cell proliferation by suppressing the expression of the tumor suppressor CDKN1A and KLF2 [84].

Brain cancer

Comparing the overexpression of the rac1 pseudogene in human brain tumors to that in normal brain tissues, a high incidence of overexpression was found. The overexpression of the rac1 pseudogene was 6 of 9 in meningiomas, 7 of 9 in astrocytomas, and 7 of 8 in pituitary adenomas. These findings imply that the Rac1 pseudogene may be a key player in brain carcinogenesis [85]. High levels of TDH pseudogene expression were associated with prolonged overall survival in gliomas, suggesting that TDH may be a protective pseudogene [86]. It was found that shorter overall survival time correlated with higher expression of five additional pseudogenes. The five pseudogenes (SP3P, ANXA2P3, PTTG3P, LPAL2, CLCA3P) have the following parent genes: pituitary tumor-transforming 1 (PTTG1), Annexin A2 (ANXA2), SP3 transcription factor (Sp3), Lp(a) (LPA lipoprotein), and chloride channel accessory 3 (CLCA3) [86]. It has been discovered that pseudogene-derived lncRNAs aid in the growth and progression of gliomas and glioblastomas. For instance, the ferritin heavy chain 1 pseudogene 3 (FTH1P3) was found to be increased in high-grade glioma tissues and glioma tissues compared with low-grade glioma tissues and normal brain tissues [87]. By modulating the miR-224-5p/TPD52 pathway, overexpression of FTH1P3 increased glioma cell proliferation and suppressed apoptosis [87]. A separate study showed that LGMNP1 was markedly elevated in glioblastoma cells following radiation and that its overexpression conferred radiation resistance by reducing DNA damage and the apoptotic population [88].

Lung cancer

Through gene conversion, pseudogenes can pass harmful alleles to their parent genes, leading to abnormal expression or gene inactivation. For instance, CYP2A6 * 1B, which has a high in vivo nicotine metabolizing activity and may raise the risk of smoking-induced lung cancer, is produced when the CYP2A7 gene is converted to the CYP2A6 gene [89]. When the PTPN12 pseudogene is inserted into the promoter region of the putative lung cancer suppressor MGA, its expression is rendered inactive, which causes NCI-H2009 cells to develop a malignant phenotype [90]. DUXAP10 promotes methylation of the 2′,5′-ol adenylate synthase (OAS2) promoter region in non-small cell lung cancer, thereby inhibiting OAS2 transcription and accelerating tumor growth [91]. Three pseudogenes (NKAPP1, MSTO2P, and RPLP0P2) were found to be components of the ceRNA triad in lung adenocarcinoma, regulating PRDM1 and EZH2 through interactions with miR-21-5p and miR-29c-3p [92]. In lung adenocarcinoma tissues, SFTA1P expression has a negative correlation with EZH2 and FOXM1 and a positive correlation with HOPX. Important oncogenes that encourage cancer cell migration and metastasis include EZH2 and FOXM1 [93, 94], while HOPX suppresses tumors in human lung cancer by inducing senescence through Ras [95]. These results showed that SFTA1P may influence the expression of those genes, hence contributing to the invasion and metastasis of lung adenocarcinoma cells [96].

Colorectal cancer (CRC)

CRC is the 2nd leading cause of cancer-related deaths worldwide [97, 98]. Larger tumor sizes, lymph node metastases, and advanced clinical stages were all positively connected with the upregulation of DUXAP10 in CRC tissues. Furthermore, in CRC cell lines, DUXAP10 knockdown markedly increased the number of G0/G1 cells, triggering cell death, and decreased cell growth. Furthermore, in vivo, tumor growth was decreased by DUXAP10 silencing [99]. Additional mechanistic research revealed that DUXAP10 suppresses the expression of p21 and the tumor suppressor phosphatase and tensin homolog (PTEN) by binding to the histone demethylase lysine-specific demethylase 1 (LSD1), which promotes the development of CRC cells and decreases cell death [99]. Both FLT1P1-s and FLT1P1-as transcripts are produced by the bidirectional transcription of FLT1P1 in human CRC cells, and these transcripts have counterbalancing functions in the control of VEGFR1 protein production. Additionally, FLT1P1-as controls the expression of miR-520a, which prevents CRC cells from expressing non-cognate VEGF-A [100]. Compared to nearby normal tissues, CR tumors expressed less CSPG4P12. CSPG4P12 overexpression prevented CRC cells from proliferating, invading, and migrating. While overexpressed CSPG4P12 suppressed the expression of vimentin, N-cadherin, and MMP9, it increased E-cadherin expression. According to these results, CSPG4P12 may be a novel target for CRC and suppresses the development of CRC [101].

Compared with earlier studies that primarily describe pseudogenes as passive genomic relics or focus on single-cancer, single-mechanism models, the current blend highlights a more integrative and context-dependent role of pseudogenes across multiple cancer types. The competitive endogenous RNA (ceRNA) activity of pseudogenes, especially their function as miRNA sponges controlling parental gene expression (e.g., PTENP1/miR-193a-3p/PTEN axis) [62], CTNNAP1 [102], and NKAPP1-MSTO2P-RPLP0P2 triad [92] have been extensively highlighted in other studies. However, new research—discussed in this work—shows that pseudogenes perform more extensive regulatory roles that go beyond ceRNA activity, such as influencing the tumor immune microenvironment and cell stemness, interacting with signaling cascades like PI3K/AKT and ERK pathways, and modulating epigenetic states like DUXAP10-mediated chromatin remodeling [62, 91].

This manuscript compares several cancers (hepatocellular, breast, ovarian, pancreatic, lung, and colorectal cancers), revealing both cancer-specific regulatory networks and common oncogenic patterns (e.g., recurrent upregulation of DUXAP family members), in contrast to previous studies that frequently address pseudogene functions alone or within a single tumor context. Crucially, by combining diagnostic, prognostic, and therapeutic implications into a single paradigm, this work also highlights translational distinctions, whereas previous research usually addresses these elements independently [103–105].

When taken as a whole, this comparative and multifaceted approach offers a more thorough understanding of pseudogene biology and emphasizes their dual context-dependent roles as tumor suppressors or oncogenes, providing new insights into their possible clinical utility and setting this work apart from earlier research.

Diagnostic potential of pseudogenes

The sensitivity of biomarkers is important and urgent for establishing optimal treatment strategies for patients. Due to the enormous efforts of scientists, especially the emergence of high-throughput genetic investigation in pseudogene research, dozens of pseudogenes have been recognized as potential players in the genesis and pathological mechanisms of certain diseases. Therefore, these pseudogenes are likely to be regarded as diagnostic markers [45, 106]. This is intended to reduce the morbidity and mortality of diseases, particularly of several diseases that are typically asymptomatic at their earliest stages, and some diseases that progress seriously and rapidly. Notably, pseudogenes naturally exhibit characteristics that aid in the recognition of illnesses, such as widespread distribution across species and within organs, tissues, and blood. This has the benefit of boosting the diversity and reliability of diagnostic indices for certain disorders. Some pseudogenes are exclusively expressed in one disease type, so they can help in distinguishing one disease subtype from another and serve as a particular illness signature to aid in diagnosis. Also, pseudogenes have significant potential in disease diagnosis and differential diagnosis because they may be used to differentiate between normal tissues and lesion tissues through distinct expression [103, 107, 108].

Collectively, ample evidence revealed that pseudogenes possess crucial roles in gene regulation either in physiological or pathological processes, leading to arising as diagnostic tools. Several pseudogenes are detected in body fluids, thus rendering them noninvasive diagnostic test markers. On the other hand, traditional diagnostic tools often require a tissue biopsy. Pseudogenes could be quantitatively assessed via quantitative polymerase chain reactions, RNA sequencing, as well as microarray, which are considered promising diagnostic tools [109, 110].

Advantageously, the regulation of several pseudogenes as a panel could be considered a diagnostic signature. In certain situations, the regulation of pseudogenes may not correspond to that of their counterparts but is distinctive for differentiation, particularly in organisms that carry only pseudogenes. Moreover, pseudogenes are highly conserved, rendering them efficient markers for quantitative and qualitative determination. In conclusion, these five characteristics: Disease-specificity, differential expression, detectability in biological fluid, involvement in different signaling pathways, and compatibility with different molecular detection tools, should enable pseudogenes to function as accurate and effective biomarkers in illness detection in the future, if they are developed and used appropriately and sensibly [104, 105].

There are numerous instances that demonstrate the diagnostic potential of particular pseudogenes. The SUMO1P3 pseudogene is highly expressed in gastric cancer and can be utilized to distinguish between benign and malignant gastric cells [107]. PTENP1 pseudogene blood levels are also downregulated in gastric malignancies compared to those in normal individuals [111]. Similarly, compared to healthy individuals, patients with hepatocellular carcinoma have lower serum levels of the integrator complex subunit 6 pseudogene 1 (INTS6P1) pseudogene, and the pseudogene’s diagnostic power seems to be equivalent to or higher than that of α-fetoprotein, the most widely used diagnostic marker in liver malignancies [103].

Liu et al. demonstrated four pseudogene signatures (RPL11551L14.1, RPL7AP28, RP4706A16.3, RP11326A19.5) as a promising biomarker in osteosarcoma with AUC = 0.87, whereas these pseudogenes were implicated in disease pathogenesis [112]. Chakravarthit et al. found that kallikrein B1 (KLKP1) could be a valuable diagnostic biomarker in prostate cancer, where it is expressed in 30% of severe grades [113] Another two pseudogenes were identified in cervical cancer, GBP1P1 and PTTG3P [71]. DUXAP8, which is located on chromosome 22, could be used in the diagnosis of oral carcinoma, where its suppression mitigates cancer proliferation via suppressing KLF2 [114].

In esophageal squamous cell carcinoma, several pseudogenes have been identified as biomarkers, including tumor suppressor candidate-2 pseudogenes (TUSC2P), FTH1P3, and DUXAP10. The pseudogene TUSC2P was suppressed among patients, whereas elevated levels were linked with good outcomes [115]. The pseudogene FTH1P3 was highly expressed in patients, where the reduction of FTH1P3 lowers development, migration, and invasive potential [116]. DUXAP10 was highly expressed and linked with high mortality, promoting proliferation and metastases [117]. In glioblastoma, the pseudogenes Metallothionein 1 J, pseudogene (MT1JP), PDIA3P1, and Annexin A2 pseudogene 2 (ANXA2P2) were expressed. MT1JP is expressed at low levels in malignant cells and is associated with adverse outcomes, whereas its elevation counteracts the proliferative and invasive properties of cancer cells [118]. The pseudogene PDIA3P1, which was detected in tissue, was highly expressed and associated with different clinicopathological grades and adverse patient outcomes [119]. As well, ANXA2P2 was highly expressed, whereas its suppression attenuates the proliferation of cells as well as suppresses glycolysis [120].

In breast cancer, RP11-480I12.5-004, Proliferating Cell Nuclear Antigen Pseudogene 1 (PCNAP1), PTENP1, PTTG3P, and crystallin beta B2 pseudogene 1 (CRYβB2P1) were detected as pseudogenes with high diagnostic power. RP11-480I12.5-004 was highly expressed in tissues, and its suppression attenuates cancer proliferation and colonization, as well as promotes cell death [121]. PCNAP1 was highly expressed and associated with short overall survival time, whereas its suppressing mitigates the migrative and invasive properties of malignant cells [122]. PTENP1 was expressed at low levels and associated with severe grades [123]. PTTG3P was highly expressed in tissues of breast cancer and inversely associated with estrogen receptors and progesterone receptors [81]. CRYβB2P1 was highly expressed and fosters tumor development and enhances tumorigenesis by promoting cell proliferation and invasion [124].

In hepatocellular carcinoma (HCC), the pseudogenes AOC4P (UPAT), WAP four-disulfide core domain 21, pseudogene (WFDC21P), golgin A2 pseudogene 10 (GOLGA2P10), MSTO2P, and AKR1B10P were suggested as diagnostic biomarkers. AOC4P was downregulated among HBV-linked HCC patients and associated with severe stages. Forced expression of AOC4P mitigates the migrative and invasive potential of malignant tissues [125]. WFDC21P is downregulated in HCC cells, whereas higher levels of it prolong survival time [126]. GOLGA2P10 was highly expressed in HCC and associated with antiapoptotic effects [127]. The pseudogene MSTO2P was highly expressed in HCC and promoted cell proliferation, invasiveness, and metastasis. Its suppression elevates E-cadherin and decreases N-cadherin and vimentin levels [128]. Regarding AKR1B10P, it was highly expressed in malignant cells and associated with severe subtypes [129].

The pseudogene DUXAP8 was markedly elevated in pancreatic tumors and linked with large volumes of tumors, severe stages, and high mortality. The suppression of DUXAP8 attenuates the proliferation and fosters apoptosis too [84]. The pseudogenes LDHAP5 and SDHAP1 were also recognized as diagnostic biomarkers in ovarian cancer. The highly expressed LDHAP5 was associated with short survival time, whereas the elevated levels of SDHAP1 were associated with chemotherapeutic drug resistance [130, 131]. The degree to which pseudogenes contribute to biological processes in organisms is still mostly unknown, even though several research have been conducted to date. The absence of reliable ways that can differentiate between the biological activities of pseudogenes and the roles of the genes from which they originate has further impeded their thorough investigation.

Practical challenges associated with clinical translation

A major and currently limiting challenge in applying pseudogene-based biomarkers lies in the high sequence homology between pseudogenes and their parental genes, which complicates assay specificity and risks misleading results. Without highly discriminative detection strategies, pseudogene signals may be confounded with those of parent genes, undermining their clinical utility. Until this hurdle is addressed by developing pseudogene-specific primers or probe designs, biomarkers based on unique, non-redundant genes may remain more reliable. However, standard array, qRT-PCR, or in situ hybridization methods are imprecise due to the absence of pseudogene-specific primers or probes. Additionally, we are incapable of using immune staining techniques like immunohistochemistry or western blotting to determine their expression since they lack the ability to code for proteins. There are frequently insufficient antibodies that are appropriate for use in immunodetection techniques, even for pseudogenes that actually produce proteins [12].

Over nad beyond, pseudogenes have emerged as promising biomarkers with several advantages over traditional protein-coding markers and other non-coding RNAs. They often exhibit high tissue and disease specificity, which enhances diagnostic accuracy compared to broadly expressed genes. In addition, pseudogenes preserve close regulatory connections with their parental genes, often acting via competitive endogenous RNA mechanisms to control key signaling pathways, thus offering biologically significant insights into disease progression. Their regulations are strongly correlated with clinical parameters such as tumor stage, metastasis, and patient survival, underscoring their prognostic value. Furthermore, pseudogene transcripts can be detected in body fluids such as serum and plasma, enabling non-invasive diagnostic approaches similar to liquid biopsy, while in some cases demonstrating comparable or superior sensitivity and specificity to established biomarkers. Unlike many non-coding RNAs that may have diverse and less specific targets, pseudogenes often reflect more defined regulatory networks, facilitating mechanistic interpretation. Inspite these advantages, their clinical usage still limited by technical challenges such as sequence homology with parental genes, which complicates accurate detection and quantification [6, 109, 132–135].

Prognostic potentials of pseudogenes

Recent studies have highlighted the prognostic potential of pseudogenes in multiple cancers (Table 4), providing insights into treatment strategies in various biological contexts, particularly in cancer. The first prognostic pseudogene is PTENP1, the pseudogene of the phosphatase and tensin homolog (PTEN) tumor suppressor, which is associated with better prognosis in melanoma [136], endometrial [137], and BC [123]. Additionally, the major Histocompatibility Complex, Class II, DP Beta 2 Pseudogene (HLA-DPB2) is positively correlated with immune infiltration and better prognosis of BC following immunotherapy [138].

Table 4.

The prognostic potential (good or poor) of pseudogenes in various cancers

Cancer	Pseudogene	Prognosis	Refs.
Melanoma	PTENP1	Better	[137]
Endometrial cancer	PTENP1		[138]
BC	PTENP1		[124]
	HLA-DPB2		[139]
Acute myeloid leukemia	BMI1P1		[140]
Low-grade glioma	PKMP3, AC027612.4, HILS1, RP5-1132H15.3, HSPB1P1		[141]
Lung adenocarcinoma	SFTA1P		[97]
CRC	CTNNAP1		[103]
HCC	INTS6P1		[142]
	E2F3P1 rs9909601 A > G		[143]
Glioma	UBDP1	Poor	[144]
	ANXA2P1, ANXA2P2, ANXA2P3		[145]
HCC	CTB-63M22.1		[61]
	RP11-564D11.3		[146]
High-grade serous ovarian cancer	SLC6A10P		[147]
	HMGA1P6		[79]
BC	PTTG3P		[82]
GC	POU5F1B		[148]
Lung adenocarcinoma	FAM207BP		[149]
Renal cell carcinoma	DUXAP8 and DUXAP9		[150]

The expression of BMI1 Proto-Oncogene, Polycomb Ring Finger Pseudogene 1 (BMI1P1) is found to be associated with a favorable prognosis and overexpressed after complete remission in acute myeloid leukemia [139].

In low-grade glioma patients, better prognosis and overall survival are associated with pseudogenes such as pyruvate kinase M1/2 pseudogene 3 (PKMP3), specific linker histone H1-like protein (HILS1), RP5-1132H15.3, and heat shock 27 kDa protein 1 pseudogene 1 (HSPB1P1) [140]. Additionally, SFTA1P (Surfactant Associated 1, LncRNA) pseudogene is linked to improved prognosis and reduced proliferation of human lung adenocarcinoma [96]. The upregulated contactin-associated protein 1 (CTNNAP1) pseudogene may function as a competing endogenous RNA (ceRNA) to enhance CTNNA1 gene expression, thereby improving prognosis in CRC [102]. Moreover, integrator complex subunit 6 pseudogene 1 (INTS6P1) may exhibit tumor suppressor activity, predicting a better prognosis for HCC [141]. In addition, the A allele of rs9909601 in E2F Transcription Factor 3 Pseudogene 1 (E2F3P1) is associated with a better prognosis in HCC [142].

Conversely, the overexpression of Ubiquitin D pseudogene 1 (UBDP1) is associated with poor prognosis in glioma due to its competitive binding with miR-6072, promoting glioma progression [143]. Monitoring pseudogenes of annexin A2 (ANXA2P1, ANXA2P2, and ANXA2P3) is correlated with poor prognosis and chemotherapy failure in diffuse glioma [144].

Another overexpressed pseudogene, cholera toxin subunit B (CTB-63M22.1), contributes to the progression of HCC, indicating a poor prognosis [60]. Furthermore, the upregulated pseudogene RP11-564D11.3 may act as an oncogene that worsens the prognosis for HCC [145].

The recurrence of high-grade serous OC increases with the elevated expression of the pseudogene, solute carrier family 6 member 10 (SLC6A10P) [146]. Moreover, high mobility group AT-hook 1 pseudogene 6 (HMGA1P6), defined as one of the overexpressed pseudogenes that is correlated with a poor survival rate in high-grade serous ovarian [78].

Additionally, high expression of pituitary tumor-transforming 3 pseudogene (PTTG3P) indicates a worse prognosis for breast cancer [81]. Overexpression of POU domain class 5 transcription factor 1B (POU5F1B) exacerbates GC, leading to poor prognosis [147]. Pseudogene-derived lncRNA Family With Sequence Similarity 207 Member B, Pseudogene (FAM207BP), is found to be related to poor prognosis in lung adenocarcinoma [148]. At the same time, upregulated double Homeobox A Pseudogene (DUXAP8 and DUXAP9) induce proliferation and an unfavorable diagnosis of renal cell carcinoma [149].

Therapeutic potential of pseudogenes

Collectively, the evidence discussed above not only underscores the biomarker potential of pseudogenes but also highlights their involvement in oncogenic and tumor-suppressive pathways, supporting their emerging role as therapeutic targets, functioning as microRNA sponges, thereby suppressing oncogene activity. This property has prompted proposals to engineer pseudogene-based analogs that enhance these tumor-suppressive effects.

Hendrickson et al. demonstrated that a peptide encoded by the 17-beta-hydroxysteroid dehydrogenase type 12 pseudogene can act as an antigen capable of eliciting a rapid immune response, suggesting possible applications in cancer immunotherapy [150]. Similarly, the (MT1DP) has been shown to exert anti-carcinogenic effects in liver cancer through the downregulation of FoxA1, indicating that targeting this regulatory axis could hold therapeutic value [151].

Furthermore, keratin 19 pseudogene 3 (KRT19P3) has been identified as a potential therapeutic target in gastric cancer, where it inhibits tumor progression by suppressing the NF-κB signaling pathway in a COPS7A-dependent manner [152].

Taken together, these findings indicate that pseudogenes could be a promising new therapeutic approach, especially for cancers resistant to standard diagnostics and treatments.

The limitations of pseudogene biomarker studies

Current pseudogene biomarker studies face significant limitations that undermine their reliability and clinical translation. These include inadequate independent validation, inconsistent findings across datasets, and methodological biases inherent to pseudogene research [153].

Validation shortcomings

Many pseudogene biomarkers lack confirmation in independent cohorts, relying instead on single datasets like TCGA, which introduces collection biases from multiple institutions. Replication efforts, such as the Reproducibility Project: Cancer Biology on PTENP1, showed partial success—some effects like proliferation changes replicated directionally but lost statistical power, while microRNA interactions failed entirely due to cell line variability and unshared reagents. Without standardized protocols for qPCR and raw data sharing, validation remains elusive [105].

Study inconsistencies

Results vary widely across studies; for instance, the BMS1P8 and PTGES3P1 pseudogenes showed poor specificity and low AUC (< 0.7) in HCC, failing to follow stepwise progression patterns. Different cohorts and methods yield divergent biomarkers, as seen in pseudogene networks where clustering reflects methodological artifacts rather than biology. Biological variability, like tissue-specific ceRNA activity, further exacerbates non-reproducibility [154].

Methodological biases

Pseudogenes cause variant calling errors in sequencing pipelines (e.g., GATK vs. DeepVariant), leading to false positives. PCR biases from high pseudogene copy numbers mimic functional genes, as with OCT4 pseudogenes, falsely indicating stemness in cancers. Technical issues, including sample handling and cell line mutations, compound these problems [155].

Summary and conclusion

In conclusion, the evolving perception of pseudogenes from mere genomic remnants to crucial elements in gene regulation underscores their importance in biological research. Their roles in influencing oncogenes and tumor suppressors highlight their potential in cancer diagnostics and prognostics. Despite the insights gained, there remains a significant need for further investigation to unravel the multifaceted functions of pseudogenes, which could lead to advancements in medical research and therapeutic strategies. The review article calls for continued exploration into this promising area to enhance our understanding of their contributions to health and disease.

Future perspectives and recommendations

Prospects for pseudogenes include conducting thorough functional studies to elucidate their roles in gene regulation, especially concerning oncogenes and tumor suppressors. It is vital to investigate potential diagnostic and prognostic molecular/bio-markers in various cancers, such as medulloblastoma [156], bladder [157], leukemia [158], pancreatic [159], alone or in conjunction with other genetic or epigenetic markers, such as pseudogenes, which could facilitate targeted therapies and personalized medicine. Collaborative studies concerning various diseases, such as diabetes, studying adipocytes, as well as the effect of non-coding RNA, and the effect of inflammation [58, 160] or the effect of various treatments [161], can amalgamate findings and enhance insights into pseudogenes in both health and disease. Longitudinal studies examining the role of pseudogenes in disease progression and treatment responses will yield valuable insights into their potential as therapeutic targets, ultimately realizing their full impact in biology and medicine.

Abbreviations

ANXA2P1

Annexin A2 pseudogene 1

ANXA2P2

Annexin A2 pseudogene 2

BC

Breast cancer

BMI1P1BMI1

Proto-Oncogene, Polycomb Ring Finger Pseudogene 1

ceRNA

Competing endogenous RNA

CRC

Colorectal cancer

CRYβB2P1

Crystallin beta B2 pseudogene 1

CTB-63M22.1

Cholera toxin subunit B

CTNNAP1

Contactin-associated protein 1

dsRNA

Double-Stranded RNA

DUXAP10

Double homeobox A pseudogene 10

DUXAP8

Double homeobox A pseudogene 8

E2F3P1E2F

Transcription Factor 3 Pseudogene 1

FAM207BP

Family With Sequence Similarity 207 Member B, Pseudogene

FTH1P3

Ferritin heavy chain 1 pseudogene 3

GBP1P1

Guanylate Binding Protein 1 Pseudogene 1

GC

Gastric cancer

GOLGA2P10

Golgin A2 pseudogene 10

HCC

Hepatocellular carcinoma

HILS1

Histone H1-like protein

HLA-DPB2

Histocompatibility Complex, Class II, DP Beta 2 Pseudogene

HMGA1P6

High mobility group AT-hook 1 pseudogene 6

HSPB1P1

Heat shock 27 kDa protein 1 pseudogene 1

INTS6P1

Integrator complex subunit 6 pseudogene 1

INTS6P1

Integrator complex subunit 6 pseudogene 1

KLKP1

Kallikrein B1

LLPS

Liquid Phase Separation

lncRNA

Long non-coding RNA

miRNAs

MicroRNAs

MREs

MicroRNA response elements

mRNAs

Messenger RNAs

MSTO2P

Misato family member 2, pseudogene

MT1JP

Metallothionein 1 J, Pseudogene

OC

Ovarian cancer

PCNAP1

Proliferating Cell Nuclear Antigen Pseudogene 1

PDIA3P1

Protein disulfide isomerase family A member 3 pseudogene 1

piRNAs

Piwi-interacting RNAs

PKMP3

Pyruvate kinase M1/2 pseudogene 3

POU5F1B

POU domain class 5 transcription factor 1B

psRNAs

Pseudogene RNAs

PTEN

Phosphatase and tensin homolog

PTENP1

Phosphatase and tensin homolog pseudogene 1

PTTG3P

Pituitary tumor-transforming 3 pseudogene

PTTG3P

Pituitary tumor-transforming 3, pseudogene

PTTG3P

Pituitary tumor-transforming 3, pseudogene

RNAi

RNA interference

SFTA1P

Surfactant Associated 1, LncRNA pseudogene

siRNAs

Small interfering RNAs

SLC6A10P

Solute carrier family 6 member 10

SUMO1P3

Small ubiquitin-like modifier 1 pseudogene 3

TSSs

Transcription start sites

TUSC2P

Tumor suppressor candidate-2 pseudogenes

UBDP1

Ubiquitin D pseudogene 1

UPAT

Ubiquitin-proteasome pathway-associated pseudogene

WFDC21P

WAP four-disulfide core domain 21, pseudogene

Author contributions

All authors contributed equally, drafted, rewrote, and revised the review, study design, conceptualization, data analysis, data interpretation, visualization, validation, supervision, resources, and methodology. *In silico* search, analysis, and software by Hamdy N.M.All authors revised the manuscript and gave their final approval for publication and authorship. All authors agreed to the current authorship provided.

Funding

Not applicable.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Human and animal ethics

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.