Skip to main content
NCBI home page
Journal of Taibah University Medical Sciences logoLink to Journal of Taibah University Medical Sciences
. 2026 Aug 14;21(5):917–928. doi: 10.1016/j.jtumed.2026.07.012

Evolutionary study of oncogenes and tumor suppressor genes in breast carcinoma

Amna Nazir a, Sadaf Ambreen a,⁎, Yasir Nawaz b
PMCID: PMC13499521  PMID: 42633313

Abstract

Objectives

Cancer involves abnormal cell proliferation and migration throughout the body. Genetic changes occurring in hereditary breast cancer led to the activation of oncogenes, resulting in gain-of-function effects. This study was designed to conduct a phylogenetic analysis of breast cancer-related oncogenes and tumor suppressor genes families in humans relevant to different species and their potential against breast cancer.

Methods

The protein sequence data were retrieved from Ensembl Genome Browser and NCBI databases. BLAST searches were performed to obtain FASTA sequences of selected vertebrates. Multiple sequence alignment was conducted using ClustalW in MEGA X, followed by the construction of neighbor-joining phylogenetic trees in circular format. Tree robustness was assessed through bootstrap analysis with 500 pseudoreplicates.

Results

Among the eleven oncogenes, the CBLC and JUN gene has two paralogues, BCL3, DDX6, FEV, MYB, KARAS, LCK, NUP214 and MPL had 5, while TFG have no paralogues. These gene families determine functional networks including transcriptional regulation, signaling cascades, receptors activity, and RNA processing, all influence breast cancer pathogenesis. In the 10 tumor suppressor genes, the CDH1, JAK2, PML, PTEN, GPC3, SOCS1, gene had 5 paralogues, EXT1 and WRN gene has 4, NPM1 has 2, VHL has 1 paralogue. These paralogues show corresponding molecular functions vital for tumor suppression and cellular homeostasis. The phylogenetic analysis exposed all studied genes share common evolutionary source in vertebrate lineage.

Conclusions

In conclusion, variability in paralogue numbers among oncogenes and tumor suppressor genes, with some genes having multiple paralogues and others, like TFG and VHL, having few or none was found. Phylogenetic trees highlighted evolutionary relationships among vertebrates.

Keywords: Breast cancer, Evolution, Mammals, Oncogenes, Tumor suppressor genes

Introduction

Breast cancer (BC) is driven by the activation of oncogenes and inactivation of tumor suppressor genes, leading to uncontrolled cell growth. In the United States, BC is a commonly identified cancer in females and is the second most important cause of cancer-related mortality.1,2 The TNM classification system is widely employed for staging BC by assessing the tumor size (T), regional lymph nodes (N), and metastasis (M). Stage 0 represents carcinoma in situ, including both ductal and lobular carcinoma in situ. Stages 1–3 indicate increasing local and regional spread, including lymph node involvement, whereas Stage 4 denotes metastatic disease, which is generally associated with a poor prognosis.3 Incidence rates are notably higher in less developed countries compared with more developed regions.4 According to the World Health Organization, approximately 2.26 million new BC cases were diagnosed in 2020, representing one in every eight cancer diagnoses worldwide, making BC one of the most aggressive and prevalent malignancies globally.5 The standard therapeutic approaches, i.e., chemotherapy, radiotherapy, surgery, and immunotherapy, have limited efficacy and often yield suboptimal long-term outcomes.6 On a global scale, BC is the most prevalent among all cancers in females, with about 2 million fresh cases and 600,000 deaths annually. In the Middle East and North Africa (MENA) region, high frequencies have been reported in Algeria and Iraq, but low frequencies in KSA and Yemen. The mortality rates are high in Iraq, the Syrian Arab Republic, Algeria, and Sudan, but low in KSA. The incidence in MENA remains lower than those in most global regions, but the mortality rates (16.9/100,000) surpass those in all other regions except for Sub-Saharan Africa. Incidence rates are 5.5 times lower among women aged <50 years than those aged ≥50 years, and are below those observed in similarly aged populations in Western countries. Projections indicate that by 2050, the BC burden in the MENA region will reach approximately 219,000 new cases and 88,900 deaths, with increases of 86% and 117%, respectively.7

Molecular-level investigations of cancer cells have revealed a wide spectrum of genetic alterations involving various combinations of oncogenes and tumor suppressor genes.8 Many genes, e.g., TP53, BRCA1, BRCA2, PTEN, ATM, CDKN1B (coding protein p27), SKP2, and RAD51, have recognized tumor suppressor gene functions in DNA repair and other cellular processes. These genes are categorized as gatekeeper and caretaker genes depending on their functions. BRCA1 and BRCA2 genes are caretaker genes involved in the healthy function of cells by coding products that stabilize the whole genome and protect against mutational actions, and they are widely recognized genetic markers for hereditary breast cancer (HBC). Studies of the functions of BRCA1/2 genes in DNA damage response pathways have identified various susceptibility genes, i.e., ATM, BRIP1, CASP8, CHEK2, NBN, PALB2, PTEN, TP53, and STK11.9 In HBC, genetic mutations often activate oncogenes, leading to gain-of-function effects, while concomitantly inactivating tumor suppressor genes through loss-of-function mutations. These alterations disrupt normal cell cycle regulation, inhibit proper cell cycle checkpoints, and impair DNA repair mechanisms. Tumor suppressor genes are referred to as recessive oncogenes due to their characteristic inheritance patterns.10,11 Examples include H-RAS as a prototypical oncogene and retinoblastoma protein (RB) as a key tumor suppressor gene, where both were identified through approaches such as functional assays, linkage analysis, positional cloning, and studies of genetically predisposed individuals.12 Twenty oncogenes have been studied, i.e., ABL, AKT, ATF, BCL, BCR, CARD, CBL, CTNN, MAML, MDM, MYB, MYC, NUP, PDGF, PIK3C, PPAR, REL, ROS, BAF, and TCL, with important roles in cancer expansion, and all have paralogues that contribute to functional variation. Twenty tumor suppressor genes, i.e., TP53, ATM, CARS, CDK, CHECK, FH, MEN, MSH, CDH, PTEN, SOCS, REQL, NF, RUNX, TCDF, and TSC, play vital roles in suppressing tumor formation. Among these genes, 15 have paralogues, whereas five genes (BRCA1, BRCA2, CHEK2, NUP98, and SUFU) lack paralogues, demonstrating their unique and conserved functions.13 Comparative genomic analyses have identified numerous genes that are prone to duplication or deletion in cancer, with major contributions from the Human Genome Project.14 Alterations in BRCA1 and BRCA2 substantially increases the threat of both BC and ovarian cancer, and rare hereditary variants of TP53 and PTEN are related to elevated susceptibility to multiple cancer types, including BC.15,16 Additional germline alterations in ATM, CHK2, NBS1, RAD50, PALB2, and BRIP1 have been linked to heightened BC risk. In particular, ATM and CHK2 encode kinases that are central to the DNA destruction response, RAD50 is essential for DNA repair, PALB2 encodes a protein that interacts with BRCA2, and BRIP1 encodes a BRCA1-interacting DNA helicase. These genes are also implicated in the pathogenesis of other malignancies, including pancreatic and prostate cancers.17 Figure 1 provides these details in graphical form.

Figure 1.

Figure 1

Open in a new tab

Relationships among oncogenes, tumor suppressor genes, and their paralogues in vertebrates and humans, highlighting their importance in breast cancer. A higher number of paralogues contributes to increased longevity in vertebrates.

During evolution, there has been a clear progression from simple to increasingly complex life forms. Phylogenetic trees highlight evolutionary relationships. Life evolved from unicellular organisms, such as bacteria, to multicellular placozoans, and from diploblastic organisms to bilaterians possessing a third germ layer.18 Likewise, chordates gave rise to vertebrates.19 These major evolutionary transitions were often accompanied by the emergence of novel structures and functions, driven in part by genetic innovations such as gene duplications.20 Extensive gene duplication events occurred at the origin of the vertebrate lineage, contributing to the widespread presence of gene families in modern vertebrates.13,21, 22, 23 The expansion of gene numbers has been linked to the increased morphological and anatomical complexity observed in vertebrates compared with non-vertebrate chordates, such as cephalochordates and tunicates. The presence of paralogons in human and other vertebrate genomes has led to the widely discussed two-round (2R) whole-genome duplication hypothesis, which proposes that two successive whole-genome duplications occurred early in vertebrate evolution after divergence from amphioxus-like invertebrate ancestors.24, 25, 26, 27, 28 By contrast, an alternative model suggests that the high number of paralogues in vertebrates could have resulted from continuous, small-scale duplications of individual genes rather than large block duplications.29, 30, 31, 32, 33 If all mammal cells are similarly vulnerable to oncogenic transformation, the risk of cancer would theoretically increase proportionally with body mass and lifespan.

However, Peto's paradox states that the frequency of cancer does not scale as predicted in large, long-lived animals.13 Instead, large, long-lived animals do not exhibit cancer as often as small, short-lived vertebrates because large, long-lived animals have extra cells and more chances of coping with cancer-associated alterations.34 In addition to body size and longevity affecting the likelihood of death caused by cancer, placental mammals may have a high frequency of disease due to various invasive placental genes. Elephants in human settings and in wild areas occasionally exhibit cancer, although the death rate due to cancer is very low in elephants compared with humans.35 In elephants, defense against cancer is considered to be facilitated by the increased apoptotic response of elephant cells to DNA destruction, which is related to the wide amplification of retrogene copies of the tumor suppressor gene TP53. Among different elephant species, TP53 retrogenes have been found in African bush and Asian elephants, but not in forest elephants. Thus, it is unclear whether interspecific changes in the TP53 retrogene copy number contribute to changes in cancer defense in elephant species.36 In humans, a single copy of TP53 (two alleles) is essential for preventing tumor development.

The aims of the present study were to investigate the evolutionary history of oncogenes and tumor suppressor genes within human gene families relative to those in other species, and the implications for BC susceptibility in large-bodied animals. An overview of this relationship is presented in Figure 2.

Figure 2.

Figure 2

Open in a new tab

Peto's paradox among vertebrates, showing lifespan and body mass with respect to cancer resistance according to a previous study.13

Materials and Methods

Species selection

Ten species were selected for this study: Homo sapiens (Human), Colobus angolensis palliatus (Angolan Colobus), Loxodonta africana (African Elephant), Macaca fascicularis (Long-tailed Macaque), Otolemur garnettii (Garnett's Greater Bushbaby), Pan paniscus (Bonobo), Pan troglodytes (Chimpanzee), Propithecus coquereli (Coquerel's Sifaka), Rhinopithecus bieti (Black Snub-nosed Monkey), and Saimiri boliviensis boliviensis (Bolivian Squirrel Monkey).

Oncogenes and tumor suppressor genes under study

Twenty-one gene families were selected for oncogenes and tumor suppressor gene families, with 21 oncogenes, i.e., BCL3, CBLC, DDX6, FEV, JUN, KARAS, LCK, MPL, MYB, NUP214 and TFG, and 10 tumor suppressor genes, i.e., CDH1, EXT1, GPC3, JAK2, NPM1, PML, PTEN, SOCS1, VHL and WRN.

Study limitations

This study was limited to oncogenes and tumor suppressor genes for BC in selected vertebrates and humans to study the effects of gene duplications.

Paralogue sequence collection

The paralogue sequences were identified using BLASTP (protein BLAST) within the Ensembl genome browser (URL: https://www.ensembl.org/index.html?redirect=no).13 The browser was opened to search for the gene of interest, before clicking on the paralogues tab on the left-hand side to obtain all paralogues associated with the gene of interest. In this study, up to five paralogues were selected for each gene to compute the data. BLASTP analysis was conducted in order to augment gene families with sequences from species that were not available in Ensembl. The sequences obtained were further analyzed in different vertebrates by using BLASTP and the sequences were applied to construct a phylogenetic tree. BLASTP analysis was conducted with the default parameters. The search was conducted using the protein database accessed via NCBI (https://www.ncbi.nlm.nih.gov/).

Sequence alignment and phylogenetic analysis

Phylogenetic analysis of each gene family was conducted using MEGA X version 5.37 In addition, we utilized the multiple alignment tool CLUSTALW with the default parameters to align the amino acid sequences, as described previously.13 Amino acid sequence alignments were manually refined when necessary. For pairwise alignments, the gap opening penalty was kept at 10 and the gap extension penalty at 0.1. For multiple alignments, the gap opening penalty was kept at 10 and the gap extension penalty at 0.2. Moreover, the gap separation distance was kept at 4 and all other parameters at the default values for alignment using CLUSTALW.

Neighbor-joining method

Phylogenetic trees were constructed for every gene with the neighbor-joining method. Protein sequences with deviations that disrupted the alignment were excluded. The accuracy of the analysis was confirmed in MEGA X software version 5 using the bootstrap method with 500 pseudo-replicates, which generated the bootstrap probability for every internal branch within the tree.13

Co-duplicated gene families

The topologies of the trees were studied using vertebrate sequences from 21 gene families, i.e., 10 tumor suppressor genes consisting of CDH1, EXT1, GPC3, JAK2, NPM1, PML, PTEN, SOCS1, VHL, and WRN, and 11 oncogenes comprising BCL3, CBLC, DDX6, FEV, JUN, KARAS, LCK, MPL, MYB, NUP214, and TFG.

Results

The identification of paralogues and evolutionary analysis of different oncogenes and tumor suppressor genes were conducted in response to a previous study by Nawaz et al.13 by targeting other oncogenes and tumor suppressor genes to explore the functions and evolution of other BC associated genes.

Oncogenes and tumor suppressor gene families

Table 1 shows all of the oncogenes investigated in this study and Table 2 shows the tumor suppressor genes. The tables show the gene members, locations on chromosomes, protein accession numbers, and molecular functions.

Table 1.

Paralogous genes, chromosomal locations, protein accession numbers, and key molecular functions of oncogenic families.

Genes Paralogues Chromosomal location Protein accession number Molecular functions
BCL3
NFKBIE 6 O00221 Inhibits NF-κB, inhibits DNA binding
POTEJ 2 P0CG39 Protein kinase binding
Structural component of postsynaptic actin cytoskeleton
POTEM 14 A6NI47 Virus receptor activity
DNA binding, RNA binding
POTEG 14 Q6S5H5 –
POTEB2 15 H3BUK9 –
CBLC
CBLB 3 Q13191 Phosphotyrosine residue binding, calcium ion binding, zinc ion binding
CBL 11 P2268 Calcium ion binding, ephrin receptor binding
DDX6
DDX54 12 Q8TDD1 ATP binding
Nuclear estrogen receptor binding, RNA binding
EIF4A2 3 Q14240 ATP hydrolysis activity, RNA binding
DDX53 X Q86TM3 RNA binding
RNA helicase activity
DDX4 5 Q9NQI0 ATP hydrolysis activity
Molecular condensate scaffold activity
DDX49 19 Q9Y6V7 RNA binding
RNA helicase activity
FEV
ERFL 19 A0A1W2PQ73 DNA binding, transcription factor activity, sequence-specific DNA binding
ELK1 X P19419 Chromatin binding
DNA-binding transcription factor activity
SPI1 11 P17947 Chromatin binding, protein sequestering activity
ETV1 7 P50549 DNA binding, transcription factor activity, sequence-specific double-stranded DNA binding
ETV6 12 P41212 DNA binding transcription factor activity, protein domain-specific binding
MYB
SNAPC4 9 Q5SXM2 DNA binding
MYBL2 20 P10244 Sequence-specific DNA binding
MYBL1 8 P10243 RNA polymerase II-specific sequence-specific DNA binding
DMTF1 7 Q9Y222 RNA polymerase II-specific
DNA binding, transcription factor activity
CDC5L 6 Q99459 DNA binding
Identical protein binding, RNA binding
MPL
EPOR 19 P19235 Erythropoietin receptor activity, identical protein binding
IL21R 16 Q9HBE5 Cytokine receptor activity
Transmembrane signaling receptor activity
IL2RB 22 P14784 Coreceptor activity
Interleukin-2 receptor activity
CSF2RB 22 P32927 Coreceptor activity, signaling receptor activity
IL4R 16 P24394 Cytokine receptor activity, interleukin-4 receptor activity
NUP214
NPAP1 15 Q9NZP6 Nuclear localization, sequence binding, structural component of nuclear pores
POM121 7 Q96HA1 –
POM121L12 7 Q8N7R1 –
POM121L2 6 Q96KW2 –
POM121C 7 A8CG34 –
TFG No paralogues
JUN
JUNB 19 P17275 DNA binding
JUND 19 P17535 Enzyme binding, nuclear receptor binding
LCK
JAK1 1 P23458 ATP binding
Growth hormone receptor binding
PTK2 8 Q05397 Actin binding source
Molecular function activator activity
FER 5 P16591 Actin binding, lipid binding
SRMS 20 Q9H3Y6 ATP binding
Signaling receptor binding
TNK1 17 : Q13470 ATP binding
Protein tyrosine kinase activity
KRAS
DIRAS3 1 O95661 GDP binding
GTPase activity
ERAS X Q7Z444 G protein activity, GDP binding
RASL12 15 Q9NYN1 –
HRAS 11 P01112 –
RAP1A 1 P62834 G protein activity
Guanyl-nucleotide exchange factors role
Open in a new tab

Table 2.

Paralogous genes, chromosomal locations, protein accession numbers, and important molecular functions for tumor suppressor gene families.

Genes Paralogues Chromosomal location Protein accession number Molecular functions
CDH1
CDH11 16 P55287 Calcium ion binding
Adherens junction organization
CDH8 16 P55286 Identical protein binding, calcium ion binding
CDH17 8 Q12864 Integrin binding, calcium ion binding
PCDH7 4 O60245 Cyclase activity, antioxidant activity, oxidoreductase activity
CDH20 18 Q9HBT6 Ligase activity, protein tag activity
JAK2
JAK1 1 P23458 ATP binding
Growth hormone receptor binding
LCK 1 P06239 Identical protein binding, protein kinase binding
PTK2 8 Q05397 Integrin binding
Protein phosphatase binding
FER 5 P16591 Lipid binding
Protein tyrosine kinase binding
SRMS 20 Q9H3Y6 ATP binding
Signaling receptor binding
PML
TRIM66 11 O15016 Zinc ion binding, nucleoplasm
TRIM5 11 Q9C035 Protein homodimerization activity, protein kinase binding
TRIM10 6 Q9UDY6 Ubiquitin protein ligase activity, zinc ion binding
TRIM43 2 Q96BQ3 –
TRIM43B 2 A6NCK2 Oxidoreductase activity, transferase activity
PTEN
TRIM66 11 O15016 Zinc ion binding, cyclase activity
TRIM5 11 : Q9C035 Transcription coactivator activity, ubiquitin protein ligase activity
TRIM10 6 Q9UDY6 Zinc ion binding, lyase activity
TRIM43B 2 A6NCK2 Structural and molecular activity, transporter activity
EXT1
EXTL3 8 O43909 Magnesium ion binding
Protein hormone receptor activity
EXT2 11 Q93063 Metal ion binding
Protein heterodimerization activity
EXTL1 1 Q92935 Histone binding, protein folding chaperone
EXTL2 1 Q9UBQ6 Manganese ion binding, lyase activity
NPM1
NPM2 8 Q86SE8 Histone binding, RNA binding
NPM3 10 O75607 Chromatin binding, histone binding
VHL No paralogues
VHLL 1 Q6RSH7 Dominant-negative VHL that serves as a protector of hypoxia-inducible factor 1-alpha (HIF-1α)
WRN
RECQL4 8 O94761 ATP binding, bubble DNA binding
RECQL5 17 O94762 ATP binding
Enzyme activator activity
BLM 15 P54132 DNA/DNA annealing activity, DNA helicase activity
RECQL 12 P46063 DNA helicase activity, ATP hydrolysis activity
GPC3
GPC5 3 P78333 DNA binding, RNA binding
GPC2 7 Q8N158 Ligase activity, protein tag activity
GPC4 X O75487 Catalytic activity, GTPase activity
GPC1 2 P35052 Copper ion binding
Fibroblast growth factor binding
GPC6 13 Q9Y625 Isomerase activity ligase activity
SOCS1
GPC5 13 P78333 Cyclase activity, antioxidant activity
GPC2 7 Q8N158 RNA metabolic process, protein folding
GPC4 X O75487 Hydrolase activity, lyase activity
GPC1 2 P35052 Copper ion binding
Fibroblast growth factor binding
GPC6 13 Q9Y625 Ligase activity, protein tag activity
Open in a new tab

Among all of the oncogene paralogue families, comparative analysis showed that each group had different molecular functions, and they converged on important regulatory mechanisms that are vital for cell survival, signaling, and genes expression. The BCL3 and CBLC families contribute to transcriptional regulation and intracellular signaling via NF-κB inhibition, receptor binding, and kinase-related functions, and the DDX6 family members support RNA metabolism through RNA binding, ATP hydrolysis, and helicase activity. Similarly, the FEV and JUN families act mainly through transcription factor regulation and DNA-binding properties, and MYB paralogues strengthen transcriptional control through RNA polymerase II-dependent activities. The MPL and NUP214 families are involved in regulation by moderating cytokine receptor signaling and nuclear pore transport, respectively, and LCK paralogues participate in kinase-mediated signaling and cytoskeletal connections. TFG have no paralogues but these genes affect protein trafficking, and KRAS family members support GTPase-dependent growth signaling.

Tumor suppressor genes

The tumor suppressor gene paralogues participate in important regulatory functions that support genomic stability, cell adhesion, and controlled signaling. CDH1 family members facilitate calcium-dependent bonding and integrin binding, and JAK2 paralogues control ATP-dependent kinase signaling and receptor connections. PML and PTEN paralogues contribute to zinc binding, ubiquitin ligase activity, transcriptional control, and protein alteration. EXT1 members have roles in metal ion binding, receptor activity, histone binding, and lyase functioning, which are significant for extracellular matrix control. NPM1 paralogues maintain the structure of chromatin through histone and RNA binding, and VHL members affect the stability of hypoxia-inducible factor 1-alpha (HIF-1α). WRN paralogues are involved in the activity of DNA helicase associated with ATP-dependent DNA repair. GPC3 and SOCS1 paralogues have different functions in nucleic acid binding, growth factor interaction, catalysis, oxidative stress response, and protein folding. Table 2 shows the functions and gene families. The Tp53 gene is associated with longevity in elephants and there are many paralogues, and these data been published already.13

Phylogenetic analysis

The evolutionary trees were constructed for 21 gene families, i.e., 10 tumor suppressor genes consisting of CDH1, EXT1, GPC3, JAK2, NPM1, PML, PTEN, SOCS1, VHL and WRN, and 11 oncogenes comprising BCL3, CBLC, DDX6, FEV, JUN, KARAS, LCK, MPL, MYB, NUP214 and TFG.

Oncogenes

Trees were constructed for the paralogues of oncogenes in different species. The gene families are listed in Table 1 for oncogene paralogues that are duplicated among vertebrates and humans.

Comprehensive comparative genomic analysis determined the distributions of multiple oncogenes in the 10 study species. The BCL3 gene (Figure 1 and Supplementary File S1) encoding a protein implicated in BC has five paralogues, and it is present in all 10 species. The CBLC gene (Figure 2 and Supplementary File S2) has two paralogues (CBLB and CBL) with functions in phosphotyrosine, calcium, and zinc ion binding, and it is present in all species. The DDX6 gene (Figure 3 and Supplementary File S3) has five paralogues (DDX5, EIF4A2, DDX53, DDX4, and DDX49) with functions in ATP and RNA binding, and it is present in all species. The FEV gene (Figure 4 and Supplementary File S4) has five paralogues and it plays a role in evolutionary adaptation, and it is absent from Loxodonta africana and Otolemur garnettii. The JUN gene (Figure 5 and Supplementary File S5) with two paralogues (JUND and JUNB) regulates transcription, and it is present in all species. The KARAS gene (Figure 6 and Supplementary File S6) has five paralogues (DIRAS3, ERAS, RASL12, HRAS, and RAP1A), and it is absent from Otolemur garnettii. The LCK gene (Figure 7 and Supplementary File S7) with five paralogues is involved in T-cell signaling, and it is absent from Rhinopithecus bieti. The MPL gene (Figure 8 and Supplementary File S8) with five paralogues participates in cytokine receptor-mediated signaling, and it is absent from Macaca fascicularis and Saimiri boliviensis boliviensis. The MYB oncogene (Figure 9 and Supplementary File S9) with five paralogues regulates cell proliferation and apoptosis, and it is present in all species. The NUP214 gene (Figure 10 and Supplementary File S10) with five paralogues encodes a nuclear pore complex protein, and it is present in all species. Finally, the TFG gene (Figure 11 and Supplementary File S11) has no known paralogues and is absent from Loxodonta africana, Rhinopithecus bieti, and Saimiri boliviensis boliviensis.

Comparative genomic analysis of BC-associated oncogenes in 10 species showed that most genes, i.e., BCL3, CBLC, DDX6, JUN, MYB, and NUP214, are highly conserved, reflecting their important roles in transcription, signaling, and RNA metabolism. The species-specific absences of FEV, KARAS, LCK, MPL, and TFG indicate evolutionary deviation and functional specialism. The distribution of paralogues highlights their expanded regulatory roles, supporting the phylogenetic associations and evolutionary conservation of oncogenes related to BC.

Tumor suppressor genes

Phylogenetic trees were constructed for paralogues of tumor suppressor genes in 10 different species. The gene families are listed in Table 2 for tumor suppressor paralogues that are duplicated in vertebrates and humans.

The CDH1 gene (Figure 12 and Supplementary File S12) with five paralogues is involved in calcium ion binding and adherens junction organization, and it is present in all species. The EXT1 gene (Figure 13 and Supplementary File S13) has four paralogues (EXTL3, EXT2, EXTL1, and EXTL2) and is conserved in all species. The GPC3 gene (Figure 14 and Supplementary File S14) has five paralogues and is also present in all species. The JAK2 gene (Figure 15 and Supplementary File S15) with five paralogues and key roles in cellular signaling is evolutionarily conserved in all species. The NPM1 gene (Figure 16 and Supplementary File S16) has two paralogues (NPM2 and NPM3) and is present in all species. The PML gene (Figure 17 and Supplementary File S17) has five paralogues and is present in all species. The PTEN gene (Figure 18 and Supplementary File S18) has five paralogues and is conserved in all species. The SOCS1 gene (Figure 19 and Supplementary File S19) has five paralogues (GPC5, GPC2, GPC4, GPC1, and GPC6) and is present in all species. The VHL gene (Figure 20 and Supplementary File S20) has one paralogue and is present in all species. Finally, the WRN gene (Figure 21 and Supplementary File S21) has four paralogues and is present in all species. None of these genes are absent from any of the 10 species analyzed, demonstrating their evolutionary conservation and possible functional indispensability.

Our analysis of tumor suppressor genes in 10 species indicates that these genes i.e., CDH1, EXT1, GPC3, JAK2, NPM1, PML, PTEN, SOCS1, VHL, and WRN, are highly conserved with multiple paralogues in many cases. This ubiquitous presence highlights their evolutionary conservation and suggests that these genes have important, indispensable roles in cell signaling, DNA repairing, transcriptional regulations, and cell adhesion, as well as key roles in maintaining genomic stability and suppressing tumor growth.

Discussion

This study was conducted in response to that by Nawaz et al.13 in order to explore the evolution of gene duplication patterns in different species to the target species that they described.13 Tumor suppressor genes and oncogenes were studied in vertebrates to examine how these genes have evolved. Similar to other mammals, cats and dogs exhibit comparable cancer incidence rates, suggesting that these genes have followed a similar evolutionary pattern across mammals. However, elephants possess more paralogous copies of these genes, which may contribute to their increased resistance to cancer.38

Gene duplication is a common process in evolution, and the primary mode for the construction of new genes.39 Paralogues are genes that originate through gene duplication mechanisms.40 Homozygous paralogue damage occurs more commonly than singletons in cancers, indicating that cancerous cells that can withstand gene damage are selected, although this make the cancer susceptible when the paralogue is engaged.41 Synthetic lethality (SL) is a useful method for precision cancer treatment42 by using medicines to target genes with complementary roles in cancers that have developed some variations. Thus, genes with selected roles and faults in cancer are targeted to reduce cell viability, whereas healthy cells with normal genes remain intact. Groups of paralogous genes with straightforward associations and the same roles can be targeted by SL, but it is important to note that drugs based on paralogue-dependent SL are still developed infrequently according to clinical trials (ClinicalTrials.gov) of cancer treatments.43 The lack of development of paralogue-associated suppressors may be due to the difficulty of balancing efficiency and cytotoxicity in the complex surroundings of cancers, where particular suppressors may be required to replace the non-selected paralogue with the homologous gene or protein sequence.44

Around 35 years ago, Peto first found that very large and longer-lived mammals develop cancer less frequently than predicted, but the evolutionary and functional mechanisms have only been investigated in rodents. Empirical studies have shown that very large animals with great longevity have low cancer rates, particularly elephants. The death rate due to cancer in elephants is less than 5% compared with around 11–25% in humans. Moreover, TP53 amplification was identified in elephants, and the effect of TP53 amplification on the apoptotic response to DNA damage was demonstrated. These findings support the concept of evolutionary-based methods for cancer suppression. The threshold for DNA damage before triggering p53-related apoptosis may be lower in elephants than humans, which is probably an evolutionary approach to evading cancer by competently removing altered cells. Evidence also indicates that increasing the TP53 gene dose enhances the transcriptional control of p53 targeted genes, where the apoptotic rate in lymphocytes increased the in order of humans with Li-Fraumeni syndrome LFS (one TP53 functioning allele), human controls (two TP53 alleles), and elephants (40 TP53 alleles). Thus, the high cancer cell mortality in elephants may be facilitated through p53 and improved by TP53 retrogenes.13,38

An analysis of cancer gene duplications across mammals found 548 recognized human genes and 63 mammalian genes. The findings showed that 87% of human genes are duplicated in about one mammalian genome, with many lineage-specific expansions of cancer-related genes.13,45 Little is known about cancer among wild animals, but many species are recognized as highly cancer resistant, including naked mole rats, blind mole rats, elephants, and bowhead whales. The age of cancer initiation also varies depending on the longevity of species, where the average time required for developing a tumor is about 2 years in mice but many years in long-lived species.46

In addition to naked mole rats, blind mole rats, microbats, and elephants, horses and cows are known to be highly resistant to BC. The mechanisms responsible for cancer resistance in large mammals such as hippopotamuses, walruses, and whales still need to be uncovered. Squirrels are also interesting species because their telomerase activity is very high in a similar manner to that in humans, and squirrels are also long-lived with cancer resistance.46 The naked mole rat has the highest number of cancer gene replications and studies suggest it has highly effective cancer defense mechanisms and unique anti-aging traits,47,48 and no cases of cancer have been observed in this species. By contrast, cancer has been observed in murine rodents, such as brown rats and mice, which have more copies of cancer-related genes compared with most other mammals, except for two-toed sloths. Sloths have the highest number of caretakers, somatic tumor suppressor genes, and both tumor suppressor genes and oncogenes. Previous findings also suggest that rodent lineages exhibit more gene family expansions than other mammals. Two-toed sloths and nine-banded armadillos are xenarthran mammals that diverged from other eutherians early in the Cenozoic and they possess large numbers of cancer gene duplications. They are distantly related to rodents and primates, but these species possess significant numbers of gene replications. Sloths have not been reported to develop cancer but their fur contains an anticancer fungus.49, 50, 51 Interestingly, these species do not have exceptionally large body sizes or long lifespans.

Naked mole rat cells are more sensitive to suppression than the normal cells of other species, where cell division arrest occurs at an early stage to form a dense monolayer of fibroblastic cells. Early contact inhibition is initiated by activating p16INK4A and then p27, which is typical of the contact inhibition process in other species such as humans or mice. If Cdkn2a INK4A (encodes p16INK4A) is altered in naked mole rat fibroblasts, then normal contact suppression is initiated by activating p27. Entire abolition of contact inhibition in naked mole rats requires loss of both the Cdkn2a INK4A and Cdkn1b (encodes p27) genes. Contact inhibition is absent in many solid tumors, and the loss of contact inhibition increases cancer resistance. The Cdkn2a and Cdkn2b locus has different structures and is subject to positive selection in naked mole rats. Cdkn2a and Cdkn2b is a rapidly evolving locus with important tumor suppressor genes. In humans and mice, it encodes cyclin-dependent kinase (CDK) inhibitors p15INK4B, p16INK4A, and p53-activator protein ARF, which shares a coding sequence with p16INK4A. However, in splicing studies of naked mole rats, a novel transcript that fused the first exon of p15 with the second and third exons of p16INK4A resulted in the novel pALT acting as a strong CDK inhibitor, which is another level of cell cycle regulation in naked mole rat cells.46

In the present study of BC-related genes in different species, we showed that the BCL3 gene is present in various species, including Colobus angolensis, Homo sapiens, Loxodonta africana, Macaca fascicularis, Otolemur garnettii, Pan paniscus, Pan troglodytes, Propithecus coquereli, Rhinopithecus bieti, and Saimiri boliviensis, with five paralogues. This gene is linked to BC in these species. The CBLC gene found in the same species has two paralogues (CBLB and CBL) and functions in binding phosphotyrosine residues, calcium ions, and zinc ions. Bats have a high metabolic rate and long life.52, 53, 54 The small brown bat is related to other laurasiatherians with many tumor suppressor genes55 and it was shown that the DDX6 gene with five paralogues (DDX5, EIF4A2, DDX53, DDX4, and DDX49) is involved in ATP and RNA binding, and is present in the same organisms. The FEV gene also has five paralogues and provides insights into the evolutionary history and genetic diversity of these species. The JUN gene with two paralogues (JUND and JUNB) is similarly widespread. The KARAS gene with five paralogues (DIRAS3, ERAS, RASL12, HRAS, and RAP1A) is found in the same species. The LCK gene with five paralogues and the MPL gene with five paralogues play roles in cellular signaling, and are also present in these species. The MYB and NUP214 genes, each with five paralogues, are found in the same organisms, where they contribute to biological processes (i.e., cell cycle regulation). Finally, the TFG gene lacks paralogues and has a unique evolutionary history in these species.

Among the tumor suppressor genes, the CDH1 gene found in species such as Colobus angolensis, Homo sapiens, Loxodonta africana, Macaca fascicularis, Otolemur garnettii, Pan paniscus, Pan troglodytes, Propithecus coquereli, Rhinopithecus bieti, and Saimiri boliviensis plays roles in calcium ion binding and adherens junction organization, and it has five paralogues. The EXT1 gene is also present in these organisms and has four paralogues (EXTL3, EXT2, EXTL1, and EXTL2). The GPC3 gene has five paralogues and is found in the same species. The JAK2 gene with five paralogues is important in signaling pathways, and it is found in these species. The NPM1 gene with two paralogues (NPM2 and NPM3) is involved in histone and RNA binding. The PML gene and PTEN gene have five paralogues in these species, and the SOCS1 gene also has five paralogues (GPC5, GPC2, GPC4, GPC1, and GPC6). The VHL gene with one paralogue has unique evolutionary adaptations in these species. Finally, the WRN gene with four paralogues reflects evolutionary changes that have contributed to species survival.

Study strengths and limitations

Our systematic phylogenetic comparison of BC-associated oncogenes and tumor suppressor genes in different vertebrates clearly indicated that the numbers of paralogues vary among different gene families. The use of different databases (i.e., Ensembl and NCBI), recognized alignment and phylogenetic tools (i.e., CLUSTALW and MEGA X), and bootstrap validation increased the consistency and reproducibility of the results. Including both oncogenes and tumor suppressor genes provided a stable evolutionary perspective.

However, this study was limited to sequence-based phylogenetics and did not consider variations in the expression or regulation of different paralogues. The use of a single tree-building approach and restricted set of cancer-associated genes and species may also have limited the phylogenetic resolution. Future studies based on functional data, other phylogenetic methods, and wider taxonomic coverage may support the biological relevance of our findings.

Conclusion

In this study, we conducted phylogenetic and comparative genomic analyses of 21 BC-related gene families, i.e., 11 oncogenes and 10 tumor suppressor genes, in different vertebrate species. The high variability in the numbers of paralogues in gene families reflects differences in gene duplications and evolutionary pressures. Many oncogenes, such as BCL3, DDX6, MYB, NUP214, MPL, LCK, and KRAS, and most tumor suppressor genes, have multiple paralogues with strong evolutionary conservation and functional divergence. By contrast, genes such as TFG and VHL have few or no paralogues, indicating a controlled evolutionary background and possible functional specialization. Paralogues help to suppress disease effects through functional redundancy because if one paralogue lose its function, another can compensate by playing its role and maintaining normal cellular processes. Phylogenetic analysis demonstrated conservation of paralogues in the species studied, with potential roles in cellular signaling, transcriptional regulation, genomic stability, and cell to cell adhesion. The irregular absence of oncogenes in some species suggests lineage-specific evolutionary events rather than loss of fundamental cancer-related functions. Future studies could involve functional, expression, and regulatory analyses with extended taxonomic sampling to clarify how paralogue diversification might affect gene functions and the susceptibility to cancer. Integrative methods will increase our knowledge of the evolutionary basis of BC-related genes and may help to classify conserved targets with translational relevance. Moreover, studies could focus on differences in paralogues to facilitate precision oncology by developing targeted therapies that exploit evolutionarily conserved patterns. Paralogues with high conservation and essential functions may serve as stable therapeutic targets in patient populations, whereas rapidly evolving or lineage-specific paralogues may provide opportunities for personalized intervention strategies.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Authors contributions

All authors contributed to the manuscript. AN collected the data, performed data analysis, and interpreted data, SA played roles in the conception and design of the work and supervised the study, and YN drafted the manuscript and wrote, reviewed and edited the manuscript. All authors approved the final version of manuscript.

Availability of data and materials

Not applicable.

Human and animal rights

No animals/humans were used in studies that formed the basis of this research.

Declaration of generative AI

The AI tool ChatGPT was used for language editing and improving the English language quality for publication.

Source of funding

None.

Conflict of interest

The authors have no conflicts of interest to declare, financial or otherwise.

Acknowledgments

We thank Dr. Muhammad Hassan from the Department of Bioinformatics, University of Okara, Okara, Pakistan for helping with the research.

Footnotes

Peer review under responsibility of Taibah University.

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jtumed.2026.07.012.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (2.6MB, docx)

References

  • 1.Mokoena D R., George B P., Abrahamse H. Enhancing breast cancer treatment using a combination of cannabidiol and gold nanoparticles for photodynamic therapy. Int J Mol Sci. 2019;20(19):4771. doi: 10.3390/ijms20194771. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Afifi A.M., Saad A.M., Al-Husseini M.J., Elmehrath A.O., Northfelt D.W., Sonbol M.B. Causes of death after breast cancer diagnosis: a US population-based analysis. Cancer. 2020;126(7):1559–1567. doi: 10.1002/cncr.32648. [DOI] [PubMed] [Google Scholar]
  • 3.Ji H., Hu C., Yang X., Liu Y., Ji G., Ge S., et al. Lymph node metastasis in cancer progression: molecular mechanisms, clinical significance and therapeutic interventions. Signal Transduct Targeted Ther. 2023;8(1):367. doi: 10.1038/s41392-023-01576-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Reyes-Monasterio A., Lozada-Martinez I.D., Cabrera-Vargas L.F., Narvaez-Rojas A.R. Breast cancer care in Latin America: the ghost burden of a pandemic outbreak. Int J Surg. 2022;104 doi: 10.1016/j.ijsu.2022.106784. [DOI] [PubMed] [Google Scholar]
  • 5.Lv L., Zhao B., Kang J., Li S., Wu H. Trend of disease burden and risk factors of breast cancer in developing countries and territories, from 1990 to 2019: results from the global burden of disease study 2019. Front Public Health. 2023;10 doi: 10.3389/fpubh.2022.1078191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Wang J., Wu S.-G. Breast cancer: an overview of current therapeutic strategies, challenge, and perspectives. Breast Cancer. 2023:721–730. doi: 10.2147/BCTT.S432526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Zahwe M., Bendahhou K., Eser S., Mukherji D., Fouad H., Fadhil I., et al. Current and future burden of female breast cancer in the Middle East and North Africa region using estimates from GLOBOCAN 2022. Int J Cancer. 2025;156(12):2320–2329. doi: 10.1002/ijc.35325. [DOI] [PubMed] [Google Scholar]
  • 8.Dakal T.C., Dhabhai B., Pant A., Moar K., Chaudhary K., Yadav V., et al. Oncogenes and tumor suppressor genes: functions and roles in cancers. MedComm. 2024;5(6) doi: 10.1002/mco2.582. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Czegle I., Huang C., Soria P.G., Purkiss D.W., Shields A., Wappler-Guzzetta E.A. The role of genetic mutations in mitochondrial-driven cancer growth in selected tumors: breast and gynecological malignancies. Life. 2023;13(4):996. doi: 10.3390/life13040996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Hansen M. Developmental Biology and Cancer: CRC Press; 2024. Genetic predisposition and cancer; pp. 277–312. [Google Scholar]
  • 11.Munir S., Nawaz Y., Tanvir F., Umar A., Luqman M., Ambreen S. Tumour suppressor genes involved in human breast cancer and its applications. An approach to best predicted markers. Rawal Med J. 2024;49(1) 190-190. [Google Scholar]
  • 12.Peta C., Tsirimonaki E., Fedonidis C., Koliou X., Sakellaridis N., Mangoura D. Clinical and Basic Aspects of Neurofibromatosis Type 1: IntechOpen; 2021. Two tails for neurofibromin: a tale of two microtubule-associated proteins. [Google Scholar]
  • 13.Nawaz Y., Zaib Khan A., Tanvir F., Ambreen S., Zafar J., Umar A., et al. Evolutionary history of oncogenes and tumor suppressor genes families: the ancestral origin in mammals and duplication patterns. Lett Drug Des Discovery. 2024;21(7):1226–1239. [Google Scholar]
  • 14.Zhu B., Joo L., Zhang T., Koka H., Lee D., Shi J., et al. Comparison of somatic mutation landscapes in Chinese versus European breast cancer patients. Human Genet Genom Adv. 2022;3(1) doi: 10.1016/j.xhgg.2021.100076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Jamwal A., Chatterjee M., Hooda K., Jan N., Mir M.A. p53 in Breast Cancer: CRC Press; 2025. Insights from genetic studies: the role of p53 in hereditary breast cancer syndrome; pp. 291–300. [Google Scholar]
  • 16.Murawski M., Jagodziński A., Bielawska-Pohl A., Klimczak A. Complexity of the genetic background of oncogenesis in ovarian cancer—Genetic instability and clinical implications. Cells. 2024;13(4):345. doi: 10.3390/cells13040345. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Fernandez A., Artola M., Leon S., Otegui N., Jimeno A., Serrano D., et al. Cancer vulnerabilities through targeting the ATR/Chk1 and ATM/Chk2 axes in the context of DNA damage. Cells. 2025;14(10):748. doi: 10.3390/cells14100748. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Lamża Ł. Diversity of ‘simple’multicellular eukaryotes: 45 independent cases and six types of multicellularity. Biol Rev. 2023;98(6):2188–2209. doi: 10.1111/brv.13001. [DOI] [PubMed] [Google Scholar]
  • 19.Malakhov V., Gantsevich M. The origin and main trends in the evolution of bilaterally symmetrical animals. Paleontol J. 2022;56(8):887–937. [Google Scholar]
  • 20.Shlyakhtina Y., Moran K.L., Portal M.M. Genetic and non-genetic mechanisms underlying cancer evolution. Cancers. 2021;13(6):1380. doi: 10.3390/cancers13061380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Dornburg A., Mallik R., Wang Z., Bernal M.A., Thompson B., Bruford E.A., et al. Placing human gene families into their evolutionary context. Hum Genom. 2022;16(1):56. doi: 10.1186/s40246-022-00429-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Baines C., Meitern R., Kreitsberg R., Sepp T. Comparative study of the evolution of cancer gene duplications across fish. Evol Appl. 2022;15(11):1834–1845. doi: 10.1111/eva.13481. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Comaills V., Castellano-Pozo M. Chromosomal instability in genome evolution: from cancer to macroevolution. Biology. 2023;12(5):671. doi: 10.3390/biology12050671. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Lamb T.D. Analysis of paralogons, origin of the vertebrate karyotype, and ancient chromosomes retained in extant species. Genome Biol Evol. 2021;13(4):evab044. doi: 10.1093/gbe/evab044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Ray L., Medeiros D. Linking vertebrate gene duplications to the new head hypothesis. Biology. 2023;12(9):1213. doi: 10.3390/biology12091213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Peterson K.J., Beavan A., Chabot P.J., McPeek M.A., Pisani D., Fromm B., et al. MicroRNAs as indicators into the causes and consequences of whole-genome duplication events. Mol Biol Evol. 2022;39(1) doi: 10.1093/molbev/msab344. msab344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Gout J.-F., Hao Y., Johri P., Arnaiz O., Doak T.G., Bhullar S., et al. Dynamics of gene loss following ancient whole-genome duplication in the cryptic paramecium complex. Mol Biol Evol. 2023;40(5) doi: 10.1093/molbev/msad107. msad107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Morini M., Bergqvist C.A., Asturiano J.F., Larhammar D., Dufour S. Dynamic evolution of transient receptor potential vanilloid (TRPV) ion channel family with numerous gene duplications and losses. Front Endocrinol. 2022;13 doi: 10.3389/fendo.2022.1013868. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Tasnim M., Wahlquist P., Hill J.T. Zebrafish: unraveling genetic complexity through duplicated genes. Dev Gene Evol. 2024;234(2):99–116. doi: 10.1007/s00427-024-00720-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Brasó-Vives M., Marlétaz F., Echchiki A., Mantica F., Acemel R.D., Gómez-Skarmeta J.L., et al. Parallel evolution of amphioxus and vertebrate small-scale gene duplications. Genome Biol. 2022;23(1):243. doi: 10.1186/s13059-022-02808-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Mottes F., Villa C., Osella M., Caselle M. The impact of whole genome duplications on the human gene regulatory networks. PLoS Comput Biol. 2021;17(12) doi: 10.1371/journal.pcbi.1009638. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Assis R., Conant G., Holland B., Liberles D.A., O'Reilly M.M., Wilson A.E. Models for the retention of duplicate genes and their biological underpinnings. F1000Research. 2024;12:1400. doi: 10.12688/f1000research.141786.2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Begum T., Duchen P., Bucao C.F., Robinson-Rechavi M. Phylogenetic modeling provides evidence for sudden shifts in expression after small-scale duplication in vertebrates and strong support for the ortholog conjecture. bioRxiv. 2023 doi: 10.1101/2023.12.29.571877. [DOI] [Google Scholar]
  • 34.Tollis M., Boddy A.M., Maley C.C. Peto's paradox: how has evolution solved the problem of cancer prevention? BMC Biol. 2017;15(1):60. doi: 10.1186/s12915-017-0401-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Boddy A.M., Abegglen L.M., Pessier A.P., Aktipis A., Schiffman J.D., Maley C.C., et al. Lifetime cancer prevalence and life history traits in mammals. Evol Med Publ Health. 2020;2020(1):187–195. doi: 10.1093/emph/eoaa015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Tollis M., Ferris E., Campbell M.S., Harris V.K., Rupp S.M., Harrison T.M., et al. Elephant genomes reveal accelerated evolution in mechanisms underlying disease defenses. Mol Biol Evol. 2021;38(9):3606–3620. doi: 10.1093/molbev/msab127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Kumar S., Nei M., Dudley J., Tamura K. MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences. Briefings Bioinf. 2008;9(4):299–306. doi: 10.1093/bib/bbn017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Abegglen L.M., Caulin A.F., Chan A., Lee K., Robinson R., Campbell M.S., et al. Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans. JAMA. 2015;314(17):1850–1860. doi: 10.1001/jama.2015.13134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Kuzmin E., Taylor J.S., Boone C. Retention of duplicated genes in evolution. Trends Genet. 2022;38(1):59–72. doi: 10.1016/j.tig.2021.06.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Xin Y., Zhang Y. Paralog-based synthetic lethality: rationales and applications. Front Oncol. 2023;13 doi: 10.3389/fonc.2023.1168143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.De Kegel B., Ryan C.J. Paralog dispensability shapes homozygous deletion patterns in tumor genomes. Mol Syst Biol. 2023;19(12) doi: 10.15252/msb.202311987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Topatana W., Juengpanich S., Li S., Cao J., Hu J., Lee J., et al. Advances in synthetic lethality for cancer therapy: cellular mechanism and clinical translation. J Hematol Oncol. 2020;13(1):118. doi: 10.1186/s13045-020-00956-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Parrish P.C.R., Thomas J.D., Gabel A.M., Kamlapurkar S., Bradley R.K., Berger A.H. Discovery of synthetic lethal and tumor suppressor paralog pairs in the human genome. Cell Rep. 2021;36(9) doi: 10.1016/j.celrep.2021.109597. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Llombart V., Mansour M.R. Therapeutic targeting of "undruggable" MYC. EBioMedicine. 2022;75 doi: 10.1016/j.ebiom.2021.103756. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Paul D. Springer; 2026. The main cancer theories in focus. Cancer: an integrative approach: principles and strategies-from precision targeting to process oncology; pp. 51–74. [Google Scholar]
  • 46.Seluanov A., Gladyshev V.N., Vijg J., Gorbunova V. Mechanisms of cancer resistance in long-lived mammals. Nat Rev Cancer. 2018;18(7):433–441. doi: 10.1038/s41568-018-0004-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Oka K., Yamakawa M., Kawamura Y., Kutsukake N., Miura K. The naked mole-rat as a model for healthy aging. Ann Rev Anim Biosci. 2023;11(1):207–226. doi: 10.1146/annurev-animal-050322-074744. [DOI] [PubMed] [Google Scholar]
  • 48.Sönmez G., Yağcı Gurbanov T. Structural features of DNA and their potential contribution to blind mole rat (Nannospalax xanthodon) longevity. Biogerontology. 2025;26(2):1–21. doi: 10.1007/s10522-025-10221-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Higginbotham S., Wong W.R., Linington R.G., Spadafora C., Iturrado L., Arnold A.E. Sloth hair as a novel source of fungi with potent anti-parasitic, anti-cancer and anti-bacterial bioactivity. PLoS One. 2014;9(1) doi: 10.1371/journal.pone.0084549. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Lee B-r, Oh S., Lee S-h, Kim Y., Youn S., Kim Y., et al. Squamous cell carcinoma in a nine-banded armadillo (Dasypus novemcinctus) J Zoo Wildl Med. 2015;46(2):333–334. doi: 10.1638/2013-0258R1.1. [DOI] [PubMed] [Google Scholar]
  • 51.Thomas F., Asselin K., MacDonald N., Brazier L., Meliani J., Ujvari B., et al. Oncogenic processes: a neglected parameter in the evolutionary ecology of animals. C R Biol. 2024;347(G1):137–157. doi: 10.5802/crbiol.159. [DOI] [PubMed] [Google Scholar]
  • 52.Trivedi D.D., Dalai S.K., Bakshi S.R. The mystery of cancer resistance: a revelation within nature. J Mol Evol. 2023;91(2):133–155. doi: 10.1007/s00239-023-10092-6. [DOI] [PubMed] [Google Scholar]
  • 53.Ragni M., Ruocco C., Nisoli E. Mitochondrial uncoupling, energy substrate utilization, and brown adipose tissue as therapeutic targets in cancer. Npj Metabolic Health Disease. 2025;3(1):37. doi: 10.1038/s44324-025-00080-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Chaves M.R. 2024. Molecular evolution of tumor suppressor gene promoters in bats: unraveling peto's paradox with phylogenetic and TFBS enrichment studies. [Google Scholar]
  • 55.Tollis M., Schneider-Utaka A.K., Maley C.C. The evolution of human cancer gene duplications across mammals. Mol Biol Evol. 2020;37(10):2875–2886. doi: 10.1093/molbev/msaa125. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.docx (2.6MB, docx)

Data Availability Statement

Not applicable.

Articles from Journal of Taibah University Medical Sciences are provided here courtesy of Taibah University

RESOURCES