Clinical and genetic insights into novel TP53 mutations in De Novo AML patients

Abstract

This study intended to uncover any potential variations in TP53 gene particularly the exons 3–7 as this region is reported to have abundant oncogenic variations with adverse impacts on its structural stability and functional activities. The selected region of TP53 was amplified in ninety de novo AML patients and amplicons were Sanger sequenced to analyze possible nucleotide variations. Ten (10) mutations were found in the coding region of the TP53 exons 3–7 which are unique to the regional mutation data in AML patients. Further evaluation of identified variants by employing multiple bioinformatics techniques revealed likely hazardous impacts of majority of them due to their existence in the core DNA binding domain. Clinical features with the exception of platelet count did not differ significantly among the AML patients within TP53 wild type and TP53 mutant groups. Concurrently, no statistically considerable difference was observed in event free survival (EFS) (p = 0.67, HR = 1.222) or overall survival (OS) (p = 0.74, HR = 0.837) comparing two groups.

Introduction

Acute Myeloid Leukemia is a genetically heterogeneous clonal disorder that influences the normal mechanism of self-renewal, proliferation, and differentiation in hematopoietic progenitor cells [1]. Although AML can affect individuals of any age, it primarily affects those over 60 [2]. As per the 2025 statistics summarized from SEER and American Cancer Society, it was projected that 22,010 new cases of AML are expected to be recorded in the United States, with an estimated 11,090 deaths [3].

Multiple genetic variations are thought to impact the development of AML. In addition to many fusion oncogenes, TP53 also serves as the most important gene in cell division, regulating every aspect including the start of cell cycle and cell death. Encoding the p53 protein on chromosome 17 with 11 exons, it functions as a transcription factor. In patients with de novo AML, TP53 variations contribute around 10%, 20–37% in t-AML/s-AML, and around 70% variations found in patients with complex karyotype [4]. It has five signature domains: N-terminal transactivation, proline-rich, DNA-binding, oligomerization, and C-terminus regulation. In cancerous cells, most variations are found in the DNA-binding domain of the TP53 gene [5]. TP53 is one of the most investigated genes in myeloid malignancies. In AML cases, the TP53 mutational burden has also emerged as a critical prognostic marker and therapy decision factor [6]. It acts as the “guardian of the human genome” and encodes for the transcription factor p53 [7]. It is a crucial transcription factor that plays an important role in tumor suppression via apoptosis, cell cycle arrest, senescence, DNA repair, differentiation, metabolism, and autophagy [8]. Approximately 80% of TP53 mutations occur in exons 5–8, specifically the DNA-binding domain (codons 102–292) [9].

The utilization of data for developing drugs and medical treatment depends mainly on in silico protein analysis. Proteins are intricate macromolecules that play a crucial to biological function. Therefore, understanding protein function, significance, and post-translational modifications in the human body requires extensive in silico analysis and expertise [10, 11]. In present investigation, newly diagnosed AML patients were screened to identify any possible aberrations in this important gene and patients clinical, hematological and survival outcomes will be assessed in this perspective. The computational approach will be utilized to determine any possible structural or functional changes caused by genetic changes.

Materials and methods

Blood sample collection

Peripheral blood samples from de novo cytogenetically normal AML patients (N = 90) were originally collected from two different hospitals in Lahore including Mayo Hospital and Jinnah Hospital, with the permission of hospital authorities. Patient’s attributes since diagnosis and treatment plan, hematological and clinical findings were obtained from patient files. The informed consent was obtained from all the subjects and/or their legal guardians. The samples were immediately stored at −20 °C. These samples (n = 90) were screened for possible changes in the TP53 gene. A follow-up of 24 months was conducted to observe the selected cohort’s survival status.

Clinical parameters of AML patients

Among the 90 AML patients, there were 51 males (56.7%) and 39 females (43.3%) with a mean age of 42 years (14–70 years). Among our selected cohort, fever represented 80% of the symptoms, with fatigue (46.7%), hepatomegaly (36.6%), splenomegaly (43.3%), cough, stomach issues, body pain, and weight loss, bleeding and spots on the body, and eye infection followed closely behind. The mutant AML patients presented a variety of French-American-British subtypes; M4 (26.8%) was the most frequent subtype identified in patients, followed by M2 (23.2%), M5 (16.7%), M1 (13.3%), M3 (6.6%), and M6 (6.6%). Furthermore, 3 (3.3%) patients had fewer common subtypes, including M0 and M7.

DNA isolation, amplification, and sequencing

Isolation of DNA was performed through Gene JET Genomic DNA Extraction Kit (K0722) from Thermo Fisher Scientific. The targeted area of the gene NCBI reference sequence (NM_000546.6), was amplified using previously published primers except exon 3 which was designed by prime 3 plus tool to look for possible variations. The primers for Exon 3: FP (CAGTCAGATCCTAGC GTCGAG) RP (GGATGATTGATGCTGT CCC), Exon4: FP (CTGGTCCTCTGACTGCTCTT) RP (AGGCATTGAAGTCTCATGGA) [20], Exon 5: FP (ATCTGTTCACTTGTGCCCTG) RP (ATCAGTGAGGAATCAGAGGC) [21], Exon 6: FP (CTGGAGAGACGACAGGGCTGGTTG) RP (CCAGAGACCCCAGTTGCAAAC) [22], Exon7: FP (CCTGCTTGCCACAGGTCT) RP (GATGAGAGGTGGATGGGTAGT) [23] with the product length of 127 bp, 358 bp, 309 bp, 206 bp, and 293 bp, respectively. The said exons 3–7 were amplified through polymerase chain reaction for which the conditions are as follows: initial denaturation 5 min at 94 °C, subsequently followed by 35 cycles of denaturation (94 °C, 45 s), annealing (60 °C/58°C, 45 s) annealing, and extension (72 °C, 30 s). A single cycle of final extension was carried out at 72 °C for 10 min. Later on, Sanger sequencing was performed on the target regions of the gene. The NCBI reference sequence (NM_000546.6) matches to ensemble transcript ID (ENST00000269305.9) and was used against the sequencing results to check for any possible variations through Geneious Prime (version 2023.1.1) software. All sequencing reads were manually inspected for base-call quality, and ambiguous calls were rechecked to ensure accuracy of variant identification. The use of standardized reference sequences, primer sets, PCR conditions, and commercially available reagents enables independent replication of the experimental workflow.

All sequencing chromatograms were visually inspected to assess base-call quality and signal intensity. Variants were considered reliable only when clear, non-overlapping peaks were observed in both forward and reverse sequencing reads. Low-quality regions or ambiguous calls were re-sequenced for confirmation. Only variants consistently detected across replicate reads were included in the final analysis.

Statistical analysis

Statistical data was evaluated using software named GraphPad Prism 9.5.1. “Fisher’s exact test” was implemented to make the comparison between different parameters among mutated and non-mutated groups. P values less than 0.05 were taken as statistically significant. Survival analysis was performed using the Kaplan-Meier method, and differences between groups were evaluated using the log-rank test. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using Cox proportional hazards regression analysis to assess the association between TP53 mutation status and patient’s survival (event-free and overall).

In Silico analysis

The in silico analysis was conducted in a stepwise manner, beginning with pathogenicity prediction, followed by protein stability and evolutionary conservation assessment, and subsequently structural modeling and interaction analysis. For clarity and reproducibility, all tools were grouped according to their specific analytical purpose, as summarized in Table 1.

Table 1.

Various bioinformatics tools for the assessment of p53 protein structure and function

In Silico Tool	Function	URL
SIFT	For Functional Analysis	https://sift.bii.a-star.edu.sg/
Align GVGD		http://agvgd.hci.utah.edu/agvgd_input.php
SNAP2		https://service.rostlab.org/services/snap2web/
PolyPhen-2		http://genetics.bwh.harvard.edu/pph2/
PANTHER		http://pantherdb.org
SNPs&GO.		https://snps-and-go.biocomp.unibo.it/snps-and-go/
Mutation Taster		https://www.mutationtaster.org/
mCSM	For Protein Stability Analysis	https://biosig.lab.uq.edu.au/tools
I-Mutant		http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi
Consurf	For Evolutionary Conservation Analysis	https://consurf.tau.ac.il/consurf_index.php
HOPE	For Structural Analysis	https://www3.cmbi.umcn.nl/hope/
Swiss Model	For Protein Modeling	https://swissmodel.expasy.org/interactive
Chimera	For 3D Visualization	http://www.cgl.ucsf.edu/chimera
PyMOL		https://www.pymol.org/
Vienna Package	For Secondary Structure Analysis of RNA	http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi
mCSM-PPI2	For Protein-Protein Interaction	https://biosig.lab.uq.edu.au/mcsm_ppi2/
MusiteDeep	For Post- translational modifications of Proteins	https://www.musite.net/
Netphos 3.1		https://services.healthtech.dtu.dk/services/NetPhos-3.1/
GPS 6.0		https://gps.biocuckoo.cn/
cBioPortal		https://www.cbioportal.org/
GSEA(version 4.2.3)		https://www.gsea-msigdb.org/gsea/login.jsp
String		https://string-db.org/
Cytoscape(version 3.9.1)		https://cytoscape.org/download.html
Metascape		https://metascape.org/gp/index.html#/main/step1

Results

Gene variations found in hotspot regions of TP53

The TP53 gene exon 3–7 was amplified and sequenced successfully among all AML patients. The sequencing results of the gene’s targeted regions were checked against the NCBI reference sequence (NM_000546.6) to look for any possible variations. The results were analyzed using software named Geneious Prime (version 2023.1.1).

In the current investigation, the age range of the cytogenetically normal AML patients was 14–70 years. Ten mutations (P72R, E171D, H179Q, Q165L, S240N, T150I, I251N, M237K, I254T, and Q192del) were detected in the translated region of the gene. Nine out of ten mutations were missense amino acid substitutions, while one Q192del was an in-frame deletion (Fig. 1). While variants, such as P72R, H179Q, and M237K, are widely recognized TP53 mutations. All identified variations in this cohort are reported here for the first time in the regional AML population.

Fig. 1.

Electropherograms aligned with the reference sequence (A) Proline changed into Arginine, heterozygous (B) Proline changed into Arginine, homozygous (C) Glutamine changed into Leucine (D) Glutamic acid changed into Aspartic acid (E) Histidine changed into Glutamine (F) Threonine changed into Isoleucine (G) Q192del (H) Serine changed into Asparagine (I) Isoleucine changed into Asparagine (J) Methionine changed into Lysine (K) Isoleucine changed into Threonine

The recognized variants were submitted to NCBI (ClinVAR database) and the accession numbers were obtained. All mutational data present in the current study are stored in the ClinVar repository and easily accessible through accession numbers which are shown in (Table 2).

Table 2.

Accession numbers of TP53 variations along with the genomic location, nucleotide change, and protein change in AML patients are given

Variant Name	Accession number	Genomic location	HGVS	HGVS	Molecular consequence
			Nucleotide	Protein
P72R	VCV000012351.65	7676154	c.215C>G	p. Pro72Arg	SNP
	RCV002510562.2
T150I	VCV001350373.5	7675163	c.449C>T	p. Thr150Ile	Missense
	RCV003229062.1
Q165L	VCV002505496.1	7675118	c.494A>T	p. Gln165Leu	Missense
	RCV003234763.1
E171D	VCV002446885.1	7675099	c.513G>T	p. Glu171Asp	Missense
	RCV003159585.1
H179Q	VCV000406578.11	7675075	c.537T>A	p. His179Gln	Missense
	RCV003159559.1
Q192del	VCV002446886.1	7674955	c.575-577delCAG	p. Gln192del	In-frame
	RCV003159586.1				Deletion
M237K	VCV000376636.4	7674253	c.710T>A	p. Met237Lys	Missense
	RCV002509381.2
S240N	VCV002502882.1	7674244	c.719G>A	p. Ser240Asn	Missense
	RCV003229539.1
I251N	VCV000528254.6	7577529	c.752T>A	p. Ile251Asn	Missense
	RCV003228966.1
I254T	VCV001759844.2	7674202	c.761T>C	p. Ile254Thr	Missense
	RCV002509835.1

Clinical and hematological characteristics correlation

Clinical and hematological parameters of patients i.e., age, gender, platelets, TLC, hemoglobin, and blast with TP53 mutant and wild type, were compared (Table 3). No statistically significant difference was observed in different parameters except platelet count, which can lead to excessive bleeding. The male ratio in TP53^mut patients was higher than in females. In the current investigation, the age range of the cytogenetically normal de novo AML patients was 14–70 years, while twenty patients (22.2%) with TP53 variants were found with an average age of 45 years.

Table 3.

Comparison of clinical characteristics of TP53 wild and mutant AML patients (n = 90)

Characteristics	TP53wt(n = 70)	TP53mut(n = 20)	P-value
Age, years
Mean (range)	34 (14–70)	45 (45–65)	0.7643
> 60	15 (21.4%)	05 (25%)
< 60	55 (78.6%)	15 (75%)
Gender			0.5921
Male	47 (67%)	15 (75%)
Female	23 (33%)	05 (25%)
Platelets(×103/µl), mean (range)	49.1 (1–889)	42.7 (10–966)	0.0398*
Hemoglobin (g/dl), mean (range)	8.7 (6.1–13.2)	8.1 (4.7–12.5)	0.1291
TLC (×103/µl), mean (range)	31.7 (0.3 to 125.7)	26.6 (2.4 to 102.3)	0.1792
Blast cells (%age)	69.25%	58.15%	0.6112
OS proportion	35.07%	25.64%	0.74
EFS proportion	51.76%	36.48%	0.67

g/dl = gram/deciliter, µl = microliter, (P < 0.05 = *)

Survival analysis

Kaplan–Meier analysis demonstrated no statistically significant differences in event-free survival (EFS) (p = 0.67, HR = 1.222, CI = 0.09969 to 4.451) or overall survival (OS) (p = 0.74, HR = 0.837, CI = 0.1365 to 3.013) between TP53mut and TP53wt groups. Given the limited number of TP53-mutant cases and the wide confidence intervals, these findings should be interpreted descriptively Fig 2.

Fig. 2.

Kaplan-Meier curves showing survival trends in AML cohort with or without TP53 alterations (A) Overall survival (B) Event-free survival

In Silico analysis

In silico analyses predicted that most of the identified TP53 variants may have deleterious effects on protein stability, structure, and interaction potential.

Protein sequence retrieval

The p53 protein’s sequence is accessed through the UniProt database (https://rest.uniprot.org/uniprotkb/P04637.fasta), a freely available repository for protein-related information. The necessary protein sequence may also be found using their accession number (NP_000537.3) in FASTA format. This sequence is further utilized by various in silico tools.

Identification of variant’s effects on protein integrity

The pathogenicity of identified variants (P72R, T150I, Q165L, H179Q, E171D, M237K, S240N, I251N, and I254T) in the coding region that may affect the protein structure or function was assessed using seven different in silico tools (SIFT, Align GVGD, SNAP2, PolyPhen-2, PANTHER, SNPs&GO, and Mutation Taster). All variants were shown to be harmful with the exception of P72R (Table 4).

Table 4.

Possible influence of observed variations on TP53 protein’s integrity using various computational methods

Variants	SIFT	Align-	SNAP2	Poly-	PANTHER	Mutation	SNPs&
		GVGD		Phen2		Taster	GO
P72R	Tolerated	C65	Neutral	Benign	Probably	Polymorphism	Neutral
	Score =0.57	GV (0.00)	Score =	Score =0.083	Benign	Score =103	Score =0.130
		GD (102.71)	−20		Score =0.18
T150I	Not	C65	Neutral	Benign	Probably	Polymorphism	Neutral
	Tolerated	GV (0.00)	Score=	Score =0.026	Benign	Score =89	Score =0.120
	Score =0.02	GD (89.28)	−52		Score =0.19
Q165L	Not	C65	Effect	Benign	Probably	Disease	Neutral
	Tolerated	GV (0.00)	Score =41	Score =0.011	Benign	Causing	Score =0.169
	Score =0.00	GD (112.44)			Score =0.19	__
H179Q	Not	C15	Effect	Probably	Probably	Disease Causing	Disease
	Tolerated	GV (0.00)	Score =59	Damaging	Damaging	Score =24	Score =0.815
	Score =0.00	GD (24.08)		Score =0.999	Score =0.85
E171D	Tolerated	C35	Neutral	Possibly	Probably	Disease	Neutral
	Score =0.27	GV (0.00)	Score=−42	Damaging	Benign	Causing	Score =0.215
		GD (44.60)		Score =0.628	Score =0.27	Score =45
S240N	Not Tolerated	C45	Effect	Probably Damaging	Probably Damaging	Disease Causing	Disease
	Score =0.00	GV (0.00)	Score =55	Score =1.0	Score =0.27	Score =46	Score =10
		GD (46.24)
I251N	Not Tolerated	C65	Effect	Probably Damaging	Probably Damaging	Disease Causing	Disease
	Score =0.00	GV (0.00)	Score =70	Score =1.0	Score =0.74	Score =194	Score =10
		GD (148.91)
M237K	Not	C65	Effect	Probably	Probably	Disease	Disease
	Tolerated	GV (0.00)	Score =82	Damaging	Damaging	Causing	Score =10
	Score =0.00	GD (94.49)		Score =1.0	Score =0.85	Score =95
I254T	Not	C65	Effect	Probably	Probably	Disease	Disease
	Tolerated	GV (0.00)	Score =52	Damaging	Damaging	Causing	Score =10
	Score =0.00	GD (89.28)		Score =1.0	Score =0.74	Score =89

Protein stability and structural analysis

The I-mutant and mCSM evaluated the impact of detected alterations on protein stability. Decreased stability was observed in almost all variants. The protein conformation was affected by a negative sign (-ve) of the free energy change value, which reduced the stability of the protein structure (Table 5). According to the HOPE server, alterations in amino acids impact the physiochemical properties, hydrophobicity, structure, and functioning of the protein. HOPE (Fig. 4B) indicated that four mutant residue’s chemical structures (E171D, Q165L, H179Q, and I254T) were smaller than the wild type while the rest of the five (P72R, T150I, M237K, I251N, and S240N) were bigger than that of wild type residues. Less hydrophobic interactions were seen in altered residues which might cause impairment in the protein shape and performance.

Table 5.

Predicted effects of identified variations on protein stability via different in Silico tools

Variants	I-Mutant		mCSM		Stability
	Stability	DDG value (kcal/mol)	Stability	DDG value (kcal/mol)
P72R	Increase	−0.14	Increase	−0.13	Destabilizing
T150I	Decrease	−0.10	Increase	−0.04	Destabilizing
Q165L	Decrease	−0.20	Increase	0.14	Stabilizing
E171D	Decrease	−0.42	Decrease	−0.56	Destabilizing
M237K	Decrease	− 0.1.37	Decrease	−1.45	Destabilizing
H179Q	Decrease	−0.53	Decrease	0.37	Stabilizing
I251N	Decrease	−1.99	Decrease	−2.20	Highly Destabilizing
S240N	Decrease	−0.67	Decrease	−0.59	Destabilizing
I254T	Decrease	−2.53	Decrease	−2.49	Highly Destabilizing

Fig. 4.

Representation of 3D structures (A) Left Chimera represents red highlighted boxes superimposed models of mutant and wild type (B) Right Illustration of 3D structures of the mutant and wild type residues along with the projection of HOPE’s 2D structures using PyMOL. Mutants appeared in red and wild types were highlighted in blue color

Conservation status of detected variations

ConSurf servers were applied to locate the conserved regions in the protein sequence. All detected mutations with the exception of P72R indicated that variants (T150I, Q165L, E171D, H179Q, M237K, I251N, S240N, and I254T) in the protein sequence were possessed as highly conserved residues as shown by the conservation score represented in (Fig. 3). The variants (Q165L, H179Q, S240N) were shown to be exposed and responsible for functional modifications, whereas the other variants (M237K, I251N, I254T) were demonstrated to have a role in structural framework and were buried.

Fig. 3.

The ConSurf results illustrate the colorful bar sheet which represents amino acids in a protein sequence. Functional status and conservation scale descriptions are located beneath the bar sheet

Swiss model

The three-dimensional structural models of the TP53 protein, both wild type and mutants were created by the swiss model. Some parameters such as sequence identity, GMQE, and QMEAN Z-score were considered to estimate the quality of generated models when choosing a suitable template. The template 8f2h.1.B with a 393 amino acid range was selected. The created models were saved as a PDB file format and subjected to different web tools, including ProSA web, molprobity, and Vadar, to validate the structures further. The models were reliable as predicted by all tools. The physiochemical characteristics were also checked by the protparam tool.

3D representation of the TP53 mutants

Multiple wild type and mutant structures were combined using UCSF Chimera; the red emphasized area on the overlapping structure indicates the position of the substituted amino acid as shown in (Fig. 4A). Another software named PyMOL was used for the same purpose. The 2D structures from the HOPE Project were combined with PyMOL to show better 3 d structures, as shown in (Fig. 4B).

Protein-protein interaction

The protein structure was suggested to become less durable and brittle as the number of protein-protein interactions decreased by utilizing the mCSM-PPI2 server. This prediction applied to all variations (P72R, Q165L, E171D, M237K, S240N, H179Q, I251N, and I254T) with the exception of T150I.

Secondary structure analysis of RNA

RNAfold is the main tool used for predicting the secondary structure and figuring out the RNA secondary structure’s minimal free energy. The stability of RNA’s secondary structure increases with decreasing MFE value. All mutations lead to aberrant RNA folding which influences the location of mRNA and protein translation (Fig. 5).

Fig. 5.

Representation of the RNA secondary structures by RNA fold tool (A) Wild Type (B) P72R (C) T150I (D) Q165L (E) E171D (F) H179Q (G) Q192del (H) M237K (I) S240N (J) I251N (K) I254T

Discussion

The current study focused on the Central Punjab Pakistan that revealed genetic variants in the targeted region of the TP53 gene, as well as exploring their possible outcomes based on an in silico approach. P53 is a tumor suppression protein that is essential for maintaining genomic integrity in the context of DNA damage. The ability of this TP53 gene to inhibit cellular growth and differentiation caused by aberrant and unregulated oncogene expression is its most well-known feature [6]. The AML patients in present cohort were mostly younger (77.8%) and exhibited a range of symptoms. Twenty patients (22.2%) with TP53 variants were found with an average age of 45 years. Patients with variations in TP53 gene have an incidence rate of de novo AML ranging from 5% to 10%, as reported by many recent studies [8, 12, 13].

Clinical characteristics were compared among TP53 mutant and wild type AML individuals. There was no significant difference seen between these groups in terms of total leukocyte count, hemoglobin levels, or blast cells in patients. Only platelet count was observed to be significantly low, causing thrombocytopenia. Genetic modifications that suppress TP53 promote oncogenic activity, which in turn promotes the unchecked growth of cancerous cells [14]. The Kaplan-Meier survival curves plotted between TP53^wt and TP53^mut groups projected no statistically significant difference in EFS or OS. The hazard ratios hint at a possible trend i.e., increased risk for EFS, decreased for OS, but the confidence intervals are too wide to draw firm conclusions. This is possibly due to small sample size, low event rates or normal cytogenetics of our patient cohort. The small TP53 ^mut subgroup precludes definitive conclusions regarding prognostic impact. Therefore, the survival findings are presented descriptively and should be interpreted with caution.

In line the present findings, the mutant TP53 has been associated to large chromosomal aberrations with complex karyotypes, which is linked to reduced overall survival in myeloid melignancies [8, 15].

The harmful effects of identified variants were indicated by various online tools such as SNAP2, FATHMM, SIFT, PolyPhen-2, PANTHER, Align GVGD, SNPs&GO, and Mutation Taster. The MUpro, I-Mutant, mCSM, and Project HOPE web tools were predicted to destabilize the protein stability as well as the modifications in the conserved amino acid sequence affect protein’s 3D structure and interacting molecules. Therefore, this study used the ConSurf and NetSurfP-3.0 servers for evolutionary conservation analysis. With a score of 9, eight out of nine variants were anticipated to be deleterious due to their highly conserved status. The swiss model generated the 3D structures of p53 mutants and wild type which were subjected to ProSA-Web, molprobity, protparam, and Vadar tools for further validation. The selected models of the p53 protein were predicted as reliable without any major defects in them. The current study utilized PyMOL and UCSF Chimera software to observe the overlapping of 3D structures [16].

The functioning of cells depends on a complex web of functional connections between biomolecules [17]. Among these interactions, protein-protein interactions are especially significant owing to their adaptability, variety, and specificity. Thus, mCSM-PPI2 was used in this study to verify protein-protein affinity. According to mCSM-PPI2, the majority of variants were suggested the reduce the protein-protein affinity, which lowered the p53 protein’s ability to interact with other associated proteins. Studying RNA secondary structure is essential to understanding the function and regulation of RNA transcripts [18]. Using the RNAfold software (Vienna Package), all changes observed in this study resulted in improper folding of the RNA secondary structure which is likely possible to trigger abnormal protein production and compromised its function. The computational predictions suggest that the identified TP53 variants may influence protein stability, structural integrity, and interaction networks; however, these findings remain predictive and require experimental validation.

Limitations

While the present study provides valuable insights into TP53 variations in de novo AML patients, certain limitations should be acknowledged. The cohort size, particularly the number of TP53-mutant cases, may have limited the statistical power of survival analyses, contributing to wider confidence intervals. Consequently, survival outcomes should be interpreted with caution. The in silico analyses employed in this study offer predictive assessments of variant pathogenicity and structural impact; however, these findings represent computational predictions rather than direct functional validation. Future studies involving larger, multi-center cohorts and functional assays would help to further validate and expand upon these findings.

Author contributions

Project design, supervision, manuscript writing and revision by AMA, Experiment work and resource SH, experiment and manuscript revision HY, experiment and manuscript writing NS, Experiment and manuscript writing AT, experiment and data analysis SA, experiment and results write up FY, Clinical diagnosis of AML patients and assessment of clinical impact of study findings AZ, data analysis and manuscript revision MD, funding and resources HMH, Supervision, manuscript writing, revision MIUK.

Funding

This research work was funded by King Khalid University, Saudi Arabia through grant No. RGP2/373/46.

Data availability

Data can be providing upon request from the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Study was done in accordance with the principles of Declaration of Helsinki and was approved by ethics committee of Jinnah and Mayo Hospitals, Lahore. The written informed consent was obtained from all the participants and/or their legal guardians.

Consent for publication

not applicable.

Clinical trial number

Not applicable.