Implication of <i>Mt-CYB</i> Gene Mutations in the Genetic Evolution of Breast Cancer in Chad

Mahamout Ahmat Issa; Fatimata Mbaye; Mbacke Sembene

doi:doi:10.11648/j.ijgg.20261401.11

Research Article |

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Implication of Mt-CYB Gene Mutations in the Genetic Evolution of Breast Cancer in Chad

Mahamout Ahmat Issa^*

, Fatimata Mbaye

, Mbacke Sembene

Published in International Journal of Genetics and Genomics (Volume 14, Issue 1)

Received: 17 December 2025 Accepted: 26 December 2025 Published: 20 January 2026

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Abstract

Background WHO reports in 2024 revealed that breast cancer affects women of all ages from puberty onward, with incidence increasing with age. Approximately 2.3 million new cases were recorded. In 2022, this disease caused 670,000 deaths worldwide. Low-penetrance genes, although not systematically associated with a high risk of breast cancer, appear to play an important role. These genes, frequently mutated in the general population, contribute significantly to breast cancer susceptibility, particularly when they interact with environmental factors or other genetic mutations. This study aims to evaluate the involvement of MT-CYB gene mutations in the progression of breast cancer among Chadian women. Methods We analyzed the variability of the MT-CYB gene in 43 patients using the PCR-sequencing technique. First, raw sequencing data were processed through the Mutation Surveyor software, which compares submitted chromatograms with the reference sequence. Next, we identified present mutations and assessed their potential impact on pathogenicity. Results Our findings highlight the potential role of the MT-CYB gene in the development of breast cancer in Chadian women. We identified 53 mutations, including 21 (39.62%) homozygous and 32 (60.37%) heterozygous mutations. Among them, 14 were already listed in the dbSNP database, while 39 were novel, with the majority found in cancerous tissues. Among these mutations, 69.81% (37/53) were non-synonymous substitutions, resulting in an amino acid change in 86.04% (37/43) of cases. Pathogenicity analysis revealed that 48.64% (18/37) were potentially deleterious, while 51.35% (19/37) were classified as neutral polymorphisms according to prediction software that considers protein structure. A detailed evaluation of the non-synonymous mutations showed that, of the 37 analyzed, 67.56% (25/37) were considered pathogenic, and 32.43% (12/37) were deemed benign. Conclusion These results highlight the crucial importance of prevention, early detection and genetic research to better understand and treat breast cancer.

Published in	International Journal of Genetics and Genomics (Volume 14, Issue 1)
DOI	10.11648/j.ijgg.20261401.11
Page(s)	1-13
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Cancer, Breast, Mutations, MT-CYB, Chad

1. Introduction

Cancer is a genetic disease of the cell, a principle that the last 30 years of research have continually confirmed. We now understand that cancer is a DNA disease resulting from the accumulation of successive mutational events: acquired or germline mutations alter the normal function of certain genes

[1]

. These genetic alterations have two distinct consequences. They may increase the activity of certain genes that broadly promote tumor growth, or conversely, they may inactivate other genes whose physiological activity counteracts tumor transformation

[2]

. In fact, genetic tumor models that provided conceptual tools to explain tumor genesis and its evolution dominated the second half of the 20th century. Thus, breast cancer in women, holding the highest rank in terms of incidence and mortality worldwide, has been at the heart of fundamental research and constitutes a major public health concern

[3]

. According to the WHO, in 2022, 2.3 million female cases and 670,000 deaths due to breast cancer were recorded globally. Present in all countries, breast cancer affects women of all ages from puberty onward, with incidence increasing as age advances

[4]

The aim of this study is, in addition to histoprognostic diagnosis, to identify new biomarker molecules that can guide clinical decision-making, clarify prognosis, and better target treatment. In the near future, it will also seek to propose a risk factor assessment for young or moderate-risk groups, suggesting routine interval testing as a precaution. This general objective includes a short-term specific goal: to study the genetic diversity of the mitochondrial cytochrome b (MT-CYB) gene between healthy and cancerous tissues to evaluate the involvement of this mitochondrial gene in the onset of breast cancer in Chadian women and potentially observe differences between breast cancers in younger versus older women.

MT-CYB is a region of the mitochondrial genome, located at positions 14747 to 15887

[5]

. The MT-CYB gene encodes a subunit of the respiratory complex III. MT-CYB, a component of the respiratory chain, is also known as the bc1 complex or ubiquinol-cytochrome c reductase complex III. It is involved in substrate binding to quinone and is responsible for the electron transfer by which transmembrane redox energy is converted into a proton motive force. Thus, complex III plays a crucial role in cellular function

[6]

2. Materials and Methods

2.1. Patients and Samples

The biological material used in this study was obtained through surgery or breast biopsy from patients with breast cancer. The samples were collected by doctors from various healthcare facilities in N’djamena, specifically at the Mother and Child University Hospital Center, the National Reference University Hospital, the Chad-China Friendship Hospital, and Guinebor Hospital. A total of forty-three (43) samples were collected, consisting of twenty-eight (28) cancerous tissues and fifteen (15) healthy tissues (controls), and were then transported to the Genomics Laboratory in the Department of Animal Biology at FST-UCAD, where the different stages of analysis were performed. Samples were preserved in 96% alcohol to ensure the integrity of the DNA molecule.

2.2. DNA Extraction, Amplification, and Sequencing

DNA extraction from breast tissues was carried out using the Standard Zymo method (Zymo Research kit), while the Qiagen kit (Mini QIAamp kit) was used for blood DNA extraction as controls. The quality of the extracted DNA was verified by electrophoretic migration on a 2% agarose gel. DNA from breast tissues was then stored at -20°C, while DNA from blood tissues was stored at -30°C.

PCR amplification of the MT-CYB gene was performed in a reaction volume of 25 μl containing 465.5 μl of MilliQ water, 612.5 μl of Master Mix, 24.5 μl of each primer: Forward (5’-CGG-ACT-ACA-ACC-ACG-ACC-AA-3’) and Reverse (5’-TCC-GGT-TTA-CAA-GAC-TGG-TGT-3’), and 2 μl of DNA extract. The PCR was conducted in an Eppendorf thermocycler under the following conditions: an initial denaturation at 95°C (3 minutes), followed by 35 cycles of initial denaturation at 95°C (40 seconds), annealing at 57°C (30 seconds), and elongation of complementary DNA strands at 72°C (1 minute), ending with a final elongation at 72°C (5 minutes). The PCR products were sequenced, and the sequencing reactions were carried out in Seoul, South Korea, by an American company named Macrogen using 30 μl of the PCR product.

2.3. Genetic Analyses of MT-CYB

2.3.1. Identification of Mutations of Interest

Each mutation and its position were identified by submitting the raw sequencing data to the Mutation Surveyor software (version 5.2.0), which compares the submitted chromatograms with the reference sequence NC_012920_14174. The Uniprot accession number is P00156. In our study, we considered only mutations with a Phred score ≥ 20.

2.3.2. Pathogenicity Prediction of Mutations

To determine if non-synonymous substitutions could induce an amino acid change in the MT-CYB gene with greater clarity and reliability, more than ten prediction software tools with different functionalities were used to assess the functional impact of the mutations (Table 1).

Table 1. Different prediction software. Different prediction software. Different prediction software.

Prediction Tools

Link

Reference

Fonction

Polyphen2

http://genetics.bwh.harvard.edu/pph2/

[7]

Protein function and structure

MutPred2

http://mutpred2.mutdb.org/

[8]

MutationTaster

http://www.mutationtaster.org/

[9]

PhD-SNP

https://snps.biofold.org/phd-snp/phd-snp.html

[10]

Probability that mutations are disease-causing

Deogen2

http://deogen2.mutaframe.com/

[11]

PredictSNP, MAPP et SNAP

https://loschmidt.chemi.muni.cz/predictsnp/

[12]

SNPs&GO

http://snps.biofold.org/snps-and-go//snpsand-go.html

[13]

PANTHER

http://www.pantherdb.org/

[14]

Evolutionary conservation

FATHMM

http://fathmm.biocompute.org.uk/

[15]

SIFT

https://sift.bii.a-star.edu.sg/

[16]

3. Results

We analyzed the mutation profiles of the MT-CYB gene, with the results presented in the table below. Chromatogram analysis using Mutation Surveyor software revealed 53 mutations (Z-score ≥ 20), including 21 (39.62%) homozygous and 32 (60.37%) heterozygous mutations. The majority of these mutations were found in cancerous tissues. Among these mutations, 69.81% (37/53) cause an amino acid change (non-synonymous mutations). Of these, 72.97% (27/37) are specific to cancerous tissues, while 27.02% (10/37) (c.593C>T, c.733C>CG, c.824C>CG, c.827A>AG, c.1153A>G, c.1258G>A, c.1594C>CA, c.1627C>CA, c.1651A>AC, c.1655C>CA) are present in both tissue types and may be considered neutral polymorphisms. Among the identified mutations, 14 were previously listed in the dbSNP database, while 39 are novel (Table 2).

Table 2. Mutation of Interest in the MT-CYB Gene. Mutation of Interest in the MT-CYB Gene. Mutation of Interest in the MT-CYB Gene.

Mutations	Score	DbSNP	Affected amino acid	Statut	Nature des mutations
c.593C>T	76.20	rs3135031	p.T7I	Homozygote	Non synonyme
c.596A>G	110.22	rs28357679	p.N8S	Homozygote	Non synonyme
c.696G>A	149,82	Nouvelle	p.L41L	Homozygote	Synonyme
c.732G>A	149,73	rs28357682	p.M53M	Homozygote	Synonyme
c.733C>CG	20,24	Nouvelle	p.H54HD	Heterozygous	Non synonyme
c.753A>G	128,34	Nouvelle	p.S60S	Homozygote	Synonyme
c.824C>CG	25,38	Nouvelle	p.A84AG	Heterozygous	Non synonyme
c.827A>AG	34,69	Nouvelle	p.N85NS	Heterozygous	Non synonyme
c.870G>A	149,65	rs28357684	p.G99G	Homozygote	Synonyme
c.888A>G	149,70	Nouvelle	p.G105G	Homozygote	Synonyme
c.903A>G	150,79	Nouvelle	p.S110S	Homozygote	Synonyme
c.937G>A	150,42	rs28357685	p.A122T	Homozygote	Non synonyme
c.963C>T	118,15	rs2854124	p.G130G	Homozygote	Synonyme
c.968T>TC	118,33	Nouvelle	p.V132VA	Heterozygous	Non synonyme
c.1031T>C	149,97	rs28357687	p.I153T	Homozygote	Non synonyme
c.1044G>A	149,73	rs28357368	p.G157G	Homozygote	Synonyme
c.1128G>A	150.31	rs28573847	p.L185L	Homozygote	Synonyme
c.1153A>G	118.13	rs2853508	p.T194A	Homozygote	Non synonyme
c.1258G>A	144,29	rs2853509	p.A229T	Homozygote	Non synonyme
c.1341T>C	150,07	Nouvelle	p.Y256Y	Homozygote	Synonyme
c.1497T>C	147,71	rs28357374	p.H308H	Homozygote	Synonyme
c.1561G>A	69,10	Nouvelle	p.A330T	Homozygote	Non synonyme
c.1564G>GA	30,75	Nouvelle	p.D331DN	Heterozygous	Non synonyme
c.1576C>CT	34,91	Nouvelle	p.L335LL	Heterozygous	Synonyme
c.1581C>CT	54,93	Nouvelle	p.T336TT	Heterozygous	Synonyme
c.1582T>TC	91,84	Nouvelle	p.W337WR	Heterozygous	Non synonyme
c.1582T>TG	84,46	Nouvelle	p.W337WG	Heterozygous	Non synonyme
c.1586T>TA	20,87	Nouvelle	p.I338IN	Heterozygous	Non synonyme
c.1594C>CA	86,00	Nouvelle	p.Q341QK	Heterozygous	Non synonyme
c.1611T>C	53,09	rs28357375	p.P346H	Homozygote	Non synonyme
c.1611T>C	78.72	Nouvelle	p.P346P	Homozygote	Synonyme
c.1627C>CA	29,52	Nouvelle	p.Q352QK	Heterozygous	Non synonyme
c.1630G>A	60,95	Nouvelle	p.V353K	Homozygote	Non synonyme
c.1631T>TA	45,78	Nouvelle	p.V353K	Heterozygous	Non synonyme
c.1640T>TG	31,08	Nouvelle	p.V356VG	Heterozygous	Non synonyme
c.1643T>TA	121,75	Nouvelle	p.L357LQ	Heterozygous	Non synonyme
c.1643T>TC	121,75	Nouvelle	p.L357P	Heterozygous	Non synonyme
c.1643T>TC	69,64	Nouvelle	p.L357T	Heterozygous	Non synonyme
c.1644A>AT	27,45	Nouvelle	p.L357PP	Heterozygous	Non synonyme
c.1645T>TA	64,62	Nouvelle	p.Y358YN	Heterozygous	Non synonyme
c.1647C>CT	25,07	Nouvelle	p.Y358YN	Heterozygous	Non synonyme
c.1650C>CA	58,81	Nouvelle	p.F359FL	Heterozygous	Non synonyme
c.1651A>AC	44,72	Nouvelle	p.T360TP	Heterozygous	Non synonyme
c.1651A>AC	61,36	Nouvelle	p.T360H	Heterozygous	Non synonyme
c.1652C>CA	25,77	Nouvelle	p.T360Q	Heterozygous	Non synonyme
c.1655C>CA	37,57	Nouvelle	p.T361K	Heterozygous	Non synonyme
c.1660C>CT	47,54	rs41504845	p.L363LL	Heterozygous	Synonyme
c.1670T>TA	86,94	Nouvelle	p.M366K	Heterozygous	Non synonyme
c.1680C>CT	34,66	Nouvelle	p.I369Y	Heterozygous	Non synonyme
c.1681T>TC	29,46	Nouvelle	p.S370T	Heterozygous	Non synonyme
c.1681T>TC	22.82	Nouvelle	p.S370S	Heterozygous	Synonyme
c.1695C>CA	76.57	Nouvelle	p.N374K	Heterozygous	Non synonyme
c.1700T>TA	61.08	Nouvelle	p.M376K	Heterozygous	Non synonyme

3.1. Predicting the Impact of Non-Synonymous Mutations

The assessment of the pathogenicity of non-synonymous mutations, carried out using different software for predicting the impact of mutations on the structure and function of proteins, the probability that they are the cause of the disease and protein stability reveals the following results (Table 3).

Table 3. Pathogenicity of mutations. Pathogenicity of mutations. Pathogenicity of mutations.

Protein function and structure						Likelihood that mutations cause disease				Evolutionary conservation
Mutations	Polyphen2	MutPred2	SNAP	MAPP	MutationTaster	PhD-SNP	Deogen2	PredictSNP	SNPs&GO	PANTHER	FATHMM	SIFT
c.593C>T p.T7I	Benign (0.00)	Benign (0.064)	Neutral (61%)	Deleterious (59%)	Benign (0\|100)	Disease (0.521)	Benign (0.08)	Neutral (65%)	Neutral (0.321)	Neutral (56%)	Tolerated (-1.54)	Neutral (90%)
c.596A>G p.N8S	Probable damage (0.990)	Benign (0.102)	Neutral (55%)	Deleterious (56%)	Benign (0\|100)	Neutral (0.252)	Benign (0.02)	Deleterious (51%)	Neutral (0.376)	Neutral (48%)	Tolerated (0.07)	Deleterious (53%)
c.733C>CG p.H54HD	Probable damage (0.998)	Benign (0.297)	Deleterious (89%)	Deleterious (88%)	Benign (0\|100)	Disease (0.846)	Benign (0.18)	Deleterious (87%)	Disease (0.875)	Deleterious (72%)	Tolerated (-1.84)	Deleterious (79%)
c.824C>CG p.A84AG	Possible damage (0.591)	Pathogenic (0.506)	Deleterious (62%)	Deleterious (77%)	Benign (0\|100)	Disease (0.864)	Benign (0.06)	Deleterious (51%)	Disease (0.878)	Deleterious (73%)	Tolerated (-0.81)	Deleterious (79%)
c.827A>AG p.N85NS	Probable damage (0.998)	Benign (0.291)	Deleterious (81%)	Deleterious (75%)	Benign (0\|100)	Disease (0.880)	Benign (0.05)	Deleterious (76%)	Disease (0.914)	Deleterious (66%)	Tolerated (-1.30)	Deleterious (79%)
c.937G>A p.A122T	Benign (0.000)	Benign (0.176)	Neutral (71%)	Deleterious (56%)	Benign (0\|100)	Neutral (0.363)	Benign (0.01)	Neutral (65%)	Neutral (0.318)	Neutral (64%)	Tolerated (-0.21)	Deleterious (53%)
c.968T>TC p.V132VA	Probable damage (1)	Benign (0.416)	Deleterious (72%)	Deleterious (86%)	Benign (0\|100)	Disease (0.635)	Benign (0.05)	Deleterious (72%)	Disease (0.732)	Deleterious (66%)	Tolerated (-2.30)	Deleterious (79%)
c.1031T>C p.I153T	Possible damage (0.903)	Benign (0.238)	Deleterious (56%)	Deleterious (81%)	Benign (0\|100)	Disease (0.746)	Benign (0.04)	Deleterious (64%)	Disease (0.806)	Deleterious (71%)	Tolerated (-2.19)	Deleterious (53%)
c.1153A>G p.T194A	Benign (0.000)	Benign (0.097)	Neutral (77%)	Neutral (70%)	Unclassified	Neutral (0.208)	Benign (0.00)	Neutral (83%)	Neutral (0.197)	Neutral (64%)	Tolerated (0.41)	Neutral (90%)
c.1258G>A p.A229T	Benign (0.020)	Benign (0.059)	Neutral (83%)	Deleterious (57%)	Benign (0\|100)	Neutral (0.299)	Benign (0.01)	Neutral (65%)	Neutral (0.441)	Neutral (56%)	Tolerated (-0.39)	Deleterious (53%)
c.1561G>A p.A330T	Benign (0.001)	Benign (0.094)	Neutral (71%)	Deleterious (72%)	Benign (0\|100)	Disease (0.558)	Benign (0.03)	Neutral (63%)	Neutral (0.413)	Neutral (57%)	Tolerated (-1.32)	Deleterious (53%)
c.1564G>GA p.D331DN	Benign (0.001)	Benign (0.045)	Neutral (77%)	Neutral (71%)	Benign (0\|100)	Neutral (0.257)	Benign (0.01)	Neutral (75%)	Neutral (0.340)	Neutral (57%)	Tolerated (0.45)	Deleterious (79%)
c.1582T>TC p.W337WR	Probable damage (1)	Pathogenic (0.766)	Deleterious (89%)	Deleterious (88%)	Benign (0\|100)	Disease (0.923)	Benign (0.11)	Deleterious (87%)	Disease (0.905)	Deleterious (69%)	Tolerated (2.75)	Deleterious (79%)
c.1582T>TG p.W337WG	Probable damage (1)	Pathogenic (0.792)	Deleterious (87%)	Deleterious (82%)	Benign (0\|100)	Disease (0.849)	Benign (0.09)	Deleterious (87%)	Disease (0.793)	Deleterious (68%)	Dommageable (-3.46)	Deleterious (79%)
c.1586T>TA p.I338IN	Probable damage (0.999)	Benign (0.357)	Deleterious (72%)	Deleterious (86%)	Benign (0\|100)	Disease (0.891)	Benign (0.18)	Deleterious (87%)	Disease (0.884)	Deleterious (72%)	Dommageable (-3.43)	Deleterious (79%)
c.1594C>CA p.Q341QK	Possible damage (0.855)	Benign (0.278)	Neutral (61%)	Deleterious (86%)	Benign (0\|100)	Disease (0.883)	Benign (0.02)	Deleterious (61%)	Disease (0.871)	Neutral (55%)	Tolerated (-0.13)	Deleterious (79%)
c.1611T>C p.P346H	Probable damage (1)	Benign (0.314)	Deleterious (89%)	Deleterious (84%)	Benign (0\|100)	Disease (0.875)	Benign (0.18)	Deleterious (87%)	Disease (0.788)	Neutral (48%)	Dommageable (-3.86)	Deleterious (79%)
c.1627C>CA p.Q352QK	Probable damage (0.967)	Benign (0.231)	Deleterious (81%)	Deleterious (86%)	Unclassified	Disease (0.880)	Benign (0.02)	Deleterious (87%)	Disease (0.832)	Neutral (55%)	Tolerated (-2.38)	Deleterious (79%)
c.1630G>A p.V353K	Benign (0.324)	Benign (0.433)	Deleterious (81%)	Deleterious (91%)	Unclassified	Disease (0.691)	Benign (0.17)	Deleterious (61%)	Disease (0.683)	Deleterious (69%)	Tolerated (-2.81)	Deleterious (79%)
c.1631T>TA p.V353K	Benign (0.324)	Benign (0.433)	Deleterious (81%)	Deleterious (91%)	Benign (0\|100)	Disease (0.691)	Benign (0.17)	Deleterious (61%)	Disease (0.683)	Deleterious (69%)	Tolerated (-2.81)	Deleterious (79%)
c.1640T>TG p.V356VG	Possible damage (0.921)	Benign (0.437)	Deleterious (62%)	Deleterious (76%)	Benign (0\|100)	Disease (0.707)	Benign (0.05)	Deleterious (61%)	Disease (0.618)	Deleterious (72%)	Dommageable (-3.04)	Deleterious (79%)
c.1643T>TA p.L357LQ	Probable damage (1)	Pathogenic (0.509)	Deleterious (56%)	Neutral (56%)	Benign (0\|100)	Disease (0.777)	Benign (0.10)	Deleterious (61%)	Disease (0.655)	Deleterious (72%)	Dommageable (-3.46)	Deleterious (79%)
c.1643T>TC p.L357P	Probable damage (1)	Pathogenic (0.754)	Deleterious (72%)	Deleterious (63%)	Benign (0\|100)	Disease (0.889)	Benign (0.14)	Deleterious (87%)	Disease (0.865)	Deleterious (72%)	Dommageable (-3.92)	Deleterious (79%)
c.1643T>TC p.L357T	Probable damage (1)	Benign (0.497)	Neutral (61%)	Deleterious (43%)	Benign (0\|100)	Neutral (0.494)	Benign (0.02)	Deleterious (55%)	Neutral (0.420)	Deleterious (61%)	Tolerated (-1.74)	Deleterious (79%)
c.1644A>AT p.L357PP	Dommage probable (1)	Pathogenic (0.754)	Deleterious (72%)	Deleterious (63%)	Benign (0\|100)	Disease (0.889)	Benign (0.14)	Deleterious (87%)	Disease (0.865)	Deleterious (72%)	Dommageable (-3.92)	Deleterious (79%)
c.1645T>TA p.Y358YN	Probable damage (1)	Pathogenic (0.675)	Deleterious (72%)	Deleterious (86%)	Benign (0\|100)	Disease (0.872)	Benign (0.17)	Deleterious (61%)	Disease (0.920)	Deleterious (73%)	Dommageable (-5.26)	Deleterious (79%)
c.1647C>CT p.Y358YN	Probable damage (1)	Pathogenic (0.675)	Deleterious (72%)	Deleterious (86%)	Unclassified	Disease (0.872)	Benign (0.17)	Deleterious (61%)	Disease (0.920)	Deleterious (73%)	Dommageable (-5.26)	Deleterious (79%)
c.1650C>CA p.F359FL	Probable damage (1)	Benign (0.360)	Deleterious (81%)	Deleterious (75%)	Benign (0\|100)	Disease (0.823)	Benign (0.05)	Deleterious (72%)	Disease (0.816)	Neutral (48%)	Dommageable (-3.89)	Deleterious (79%)
c.1651A>AC p.T360TP	Possible damage (0.954)	Benign (0.275)	Neutral (50%)	Deleterious (62%)	Benign (0\|100)	Disease (0.839)	Benign (0.03)	Deleterious (61%)	Disease (0.701)	Neutral (65%)	Tolerated (-2.05)	Deleterious (79%)
c.1651A>AC p.T360H	Possible damage (0.948)	Benign (0.280)	Neutral (50%)	Deleterious (77%)	Benign (0\|100)	Disease (0.644)	Benign (0.09)	Deleterious (76%)	Disease (0.577)	Neutral (47%)	Tolerated (-1.95)	Deleterious (79%)
c.1652C>CA p.T360Q	Benign (0.426)	Benign (0.198)	Neutral (58%)	Deleterious (57%)	Benign (0\|100)	Disease (0.557)	Benign (0.08)	Deleterious (51%)	Neutral (0.487)	Neutral (63%)	Tolerated (-1.27)	Deleterious (79%)
c.1655C>CA p.T361K	Possible damage (0.686)	Benign (0.293)	Neutral (55%)	Deleterious (91%)	Benign (0\|100)	Disease (0.716)	Benign (0.04)	Deleterious (61%)	Disease (0.776)	Deleterious (57%)	Tolerated (-0.78)	Deleterious (79%)
c.1670T>TA p.M366K	Benign (0.065)	Pathogenic (0.679)	Deleterious (56%)	Deleterious (92%)	Unclassified	Disease (0.872)	Benign (0.04)	Deleterious (76%)	Disease (0.861)	Deleterious (61%)	Tolerated (-2.15)	Deleterious (79%)
c.1680C>CT p.I369Y	Benign (0.002)	Benign (0.195)	Neutral (61%)	Deleterious (59%)	Benign (0\|100)	Neutre (0.494)	Benign (0.02)	Deleterious (61%)	Neutral (0.459)	Neutral (56%)	Tolerated (-2.00)	Deleterious (79%)
c.1681T>TC p.S370T	Possible damage (0.724)	Benign (0.144)	Neutral (61%)	Deleterious (57%)	Benign (0\|100)	Neutre (0.440)	Benign (0.03)	Deleterious (51%)	Neutral (0.333)	Neutral (56%)	Tolerated (-0.04)	Deleterious (79%)
c.1695C>CA p.N374K	Probable damage (0.995)	Benign (0.173)	Neutral (67%)	Deleterious (81%)	Benign (0\|100)	Disease (0.731)	Benign (0.07)	Deleterious (72%)	Disease (0.863)	Neutral (63%)	Tolerated (-0.46)	Deleterious (79%)
c.1700T>TA p.M376K	Possible damage (0.685)	Benign (0.449)	Neutral (55%)	Deleterious (92%)	Benign (0\|100)	Disease (0.518)	Benign (0.03)	Deleterious (61%)	Neutral (0.471)	Neutral (56%)	Tolerated (-0.13)	Deleterious (79%)

3.2. Structure of Mutated Proteins and Their Molecular Mechanisms

We analyzed the impact of substitutions of each amino acid considering molecular mechanisms using MutPred2 software, which uses genetic and molecular data to probabilistically assess the pathogenicity of substitutions. Results reveal that p.A84G, p.W337R, p.W337G, p.L357Q, p.L357P, p.Y358N, and p.M366K substitutions affect molecular mechanisms, with significant p-values (less than 0.05), as shown in Table 4 below.

Table 4. Mutation prediction based on molecular mechanisms. Mutation prediction based on molecular mechanisms. Mutation prediction based on molecular mechanisms.

Substitution	Molecular mechanisms with P-values <= 0.05	Probability	P-value
A84G	Altered Transmembrane protein	0.28	4.0e-04
	Altered Disordered interface	0.28	0.04
	Altered Ordered interface	0.25	0.02
	Gain of GPI-anchor amidation at N85	0.02	0.01
W337R	ltered Transmembrane protein	0.38	9.7e-06
	Altered Ordered interface	0.32	3.5e-03
	Loss of Pyrrolidone carboxylic acid at Q341	0.12	7.2e-03
W337G	Altered Ordered interface	0.54	9.7e-05
	Altered Transmembrane protein	0.33	4.9e-05
	Loss of Strand	0.27	0.02
	Loss of Pyrrolidone carboxylic acid at Q341	0.12	7.2e-03
	Altered Stability	0.12	0.03
L357Q	Altered Transmembrane protein	0.48	0.0e+00
	Gain of Strand	0.35	1.8e-04
	Loss of Helix	0.34	7.5e-04
	Altered Ordered interface	0.31	2.2e-03
L357P	Altered Transmembrane protein	0.40	4.9e-06
	Loss of Helix	0.37	1.6e-04
	Gain of Strand	0.35	1.2e-04
	Altered Ordered interface	0.32	3.3e-03
Y358N	Altered Ordered interface	0.39	1.3e-03
	Altered Transmembrane protein	0.33	4.9e-05
	Gain of Helix	0.28	0.02
	Altered Disordered interface	0.27	0.04
	Loss of Strand	0.27	0.02
	Gain of N-linked glycosylation at Y358	0.13	7.6e-03
M366K	Altered Transmembrane protein	0.44	0.0e+00
	Altered Ordered interface	0.24	0.03
	Gain of Relative solvent accessibility	0.24	0.04
	Altered Signal peptide	0.03	4.0e-03

Table 5. Structuring of amino acids using the SNPMuSiC software. Structuring of amino acids using the SNPMuSiC software. Structuring of amino acids using the SNPMuSiC software.

Mutation	Schematic structures of the original amino acid (left) and mutant (right)	3D Structure
A84AG
W337WR
W337WG
L357LQ
L357P
Y358N
M366K

4. Discussion

In this study, the mitochondrial gene MT-CYB, involved in substrate binding to quinone and responsible for electron transfer converting transmembrane redox energy into proton force, was examined in 43 Chadian patients, including 28 with cancer. breast and 15 serving as controls. The MT-CYB gene exhibits polymorphism in tumor cells, and genetic analysis revealed mutations confirming its involvement in the development of breast cancer in Chadian women. These results are consistent with the studies of Mbaye et al. (2015)

[6]

, Doupa et al. (2015)

[3]

and Mze et al. (2017)

[17]

, carried out on Senegalese patients. Due to the heterogeneity of pathologies associated with mutations in the cyt b gene, it is difficult to group the different classes of mutations and precisely characterize their effects in humans

[18]

We analyzed mutation profiles of the MT-CYB gene using Mutation Surveyor software, identifying 53 mutations with a Z-score ≥ 20, including 21 (39.62%) homozygous and 32 (60.37%) heterozygous. The majority of these mutations are located in cancerous tissues, reflecting great variability and genetic instability of the MT-CYB gene. Our results confirm the work of F. Mbaye (2015), who reported that "the variations observed in cancerous tissues (90.29%) are much more numerous than those in healthy tissues (41.77%), indicating the implication of nucleotide variations of the MT-CYB gene in breast carcinogenesis in Senegalese women”

[6]

Song Z et al., (2016), demonstrated that while a small number of MT-CYB variations have no functional effect, others have the capacity to modify the properties of complex III, suggesting that they could play a more important role in human health and disease than previously thought

[19]

Among the mutations observed, 69.81% (37/53) are non-synonymous substitutions, inducing an amino acid change in 86.04% (37/43) of cases. These results are consistent with those reported by several researchers. For example, Mbaye (2014) reports that “73.83% of variations are substitutions, resulting in an amino acid change in 58.28% of cases”

[20]

. Parella et al. (2001) note that “58% of variations in coding regions are substitutions, inducing a change in 25% of cases”

[21]

, while Polyak et al. (1998) note that the majority of mutations (11/12) observed are also substitutions

[22]

. Among the substitutions noted here, 72.97% (27/37) are specific to cancerous tissues, while 27.02% (10/37) (c.593C>T, c.733C>CG, c.824C>CG, c.827A>AG, c.1153A>G, c.1258G>A, c.1594C>CA, c.1627C>CA, c.1651A>AC, c.1655C>CA) are present in both tissue types, suggesting that they could be neutral polymorphisms. Among the mutations identified, 14 were already referenced in the dbSNP database, while 39 are new. Thus, the link between mutations in the MT-CYB gene and breast cancer is well documented by several studies.

Non-synonymous single nucleotide polymorphisms (nsSNPs) are coding variants that introduce amino acid changes in their corresponding proteins. Because nsSNPs can affect protein function, they are thought to have the greatest impact on human health compared to SNPs in other regions of the genome. Therefore, it is important to distinguish nsSNPs that affect protein function from those that are functionally neutral

[23]

. Non-synonymous polymorphisms in the MT-CYB gene were evaluated using more than ten prediction software programs, each using specific algorithms to determine the functional impact of mutations. This pathogenicity analysis, based on protein structure and function, indicates that the mutations p.H54HD, p.A84AG, p.N85NS, p.V132VA, p.I153T, p.W337WR, p.W337WG, p. I338IN, p.P346H, p.Q352QK, p.V356VG, p.L357LQ, p.L357P, p.Y358N, p.F359L and p.M366K could be harmful. This is in agreement with the work of Issa J (2024), who reported that “The p.L357L/V mutations were predicted to be deleterious thus inducing a modification of the function and structure of the protein”

[24]

. Among the 37 mutations analyzed, 25 were considered pathogenic and 11 considered benign according to specific pathogenicity prediction tools. As for protein stability, 21 mutations were classified as deleterious and 16 as neutral.

The identification of cancer-promoting amino acid substitutions (AAS) (driver mutations) promises to lead to a better understanding of the molecular mechanisms underlying disease, as well as provide potential diagnostic and therapeutic markers

[25]

. However, this remains a major challenge, because the majority of SAAs detected in cancer genomes do not contribute to carcinogenesis; in fact, these “passenger mutations” are a consequence of tumorigenesis rather than a cause

[26]

. Therefore, accurate automated computational prediction algorithms capable of distinguishing between driver and passenger mutations are of paramount importance

[27]

. This analysis clearly showed that no method completely summarizes all other methods, that is, each method was successful in correctly and uniquely identifying certain disease-associated SAAs where other methods did not. Did not succeed. These results reaffirm previous suggestions that combining predictions from multiple prediction methods has the potential to perform better than any individual method

[28, 29]

We analyzed the impact of substitutions of each amino acid considering molecular mechanisms using MutPred2 software, which uses genetic and molecular data to probabilistically assess the pathogenicity of substitutions. Automated methods capable of accurately and reliably distinguishing pathogenic and functionally neutral nsSNPs are therefore becoming increasingly important

[15]

. Although the available methods produce interesting results in the detection of disease-related mutations, they do not provide any information on the associated pathology. Only MutPred

[30]

is the first attempt at an algorithm capable of providing information about the disease mechanism

[31]

Results reveal that p.A84G, p.W337R, p.W337G, p.L357LQ, p.L357P, p.Y358N, and p.M366K substitutions affect molecular mechanisms, with significant p-values (less than 0.05), as indicated in Table 4. Among the mechanisms affected the alteration of transmembrane protein. Complex III of the mitochondrial respiratory chain, anchored in the inner mitochondrial membrane, catalyzes the transfer of electrons from ubiquinol to cytochrome c, coupling this electron transfer to the vectorial translocation of protons across the membrane. The MT-CYB gene encodes one of the three main subunits of this complex, a hydrophobic protein formed by eight transmembrane helices. This complex plays a key role in energy conservation and the production of reactive oxygen species (ROS). ROS participate in signaling pathways that coordinate homeostatic responses between the nucleus and mitochondria, but they can also damage cellular components, leading to cell death. Mutations p.L357LQ and p.L357P result in helix loss, thereby disrupting complex III function. This alteration could compromise the structural integrity of cells and impact the health of individuals carrying these variants

[21]

. The M366K mutation results in an alteration of the signal peptide, thereby disrupting the structure of the enzyme, which normally exists as a functional dimer composed of 10 or 11 polypeptides per monomer. Given these numerous essential functions, we therefore understand that any defect in respiratory enzymes caused by a genetic mutation or pharmacological interference can lead to disastrous consequences with a multiplicity of presentations depending on the tissues and the function affected

[18]

The 3D structure of each amino acid affected by the amino acid substitutions was determined using the SNPMuSiC software, integrated into MutaFarme, which predicts deleterious variants with high accuracy due to their impact on stability (Table 5). In the human population, the MT-CYB gene exhibits a high level of variance compared to the consensus reference sequence. Most of these variations could be silent. However, some variations, particularly those located in regions involved in catalytic activity, could induce subtle changes in complex III function that go unnoticed under normal circumstances. Mutations in the MT-CYB gene have also been associated with mitochondrial diseases or reported in patients with specific cancers. Although several advances have been made in the treatment of cancer

[32, 33]

, the mechanism of the disease is still largely obscure. Unlike Mendelian disease where the pathology is mainly linked to one gene, cancer is a complex disease that often involves several genes. Although it is difficult to dissect the contribution of each gene, individual variants could be indicators of disease risk

[34]

It is worth noting certain limitations of our study, notably the limited sample size. However, this research is the first to explore the relationship between genetics and breast cancer in Chad.

5. Conclusion

In conclusion, our results highlight the potential role of the MT-CYB gene in the development of breast cancer in Chadian women. We identified 53 mutations, including 21 (39.62%) homozygous and 32 (60.37%) heterozygous. Among them, 14 were already listed in the dbSNP database, while 39 are new, and the majority were found in cancerous tissues. Of these mutations, 69.81% (37/53) are non-synonymous substitutions, resulting in an amino acid change in 86.04% (37/43) of cases. Analysis of their pathogenicity reveals that 48.64% (18/37) are potentially deleterious, while 51.35% (19/37) are classified as neutral polymorphisms according to prediction software taking into account protein structure. The in-depth evaluation of non-synonymous mutations showed that, of the 37 mutations analyzed, 67.56% (25/37) were considered pathogenic and 32.43% (12/37) were considered benign. These results highlight the crucial importance of prevention, early detection and genetic research to better understand and treat breast cancer.

Abbreviations

AAS	Amino Acid Substitutions
MT-CYB	Mitochondrial Cytochrome b
nsSNP	Non-synonymous Single Nucleotide Polymorphisms
PCR	Polymerase Chaine Reaction
ROS	Reactive Oxygen Species
SNP	Single Nucleotide Polymorphism

Conflicts of Interest

The authors declare no conflicts of interest.

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Issa, M. A., Mbaye, F., Sembene, M. (2026). Implication of Mt-CYB Gene Mutations in the Genetic Evolution of Breast Cancer in Chad. International Journal of Genetics and Genomics, 14(1), 1-13. https://doi.org/10.11648/j.ijgg.20261401.11

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Issa, M. A.; Mbaye, F.; Sembene, M. Implication of Mt-CYB Gene Mutations in the Genetic Evolution of Breast Cancer in Chad. Int. J. Genet. Genomics 2026, 14(1), 1-13. doi: 10.11648/j.ijgg.20261401.11

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Issa MA, Mbaye F, Sembene M. Implication of Mt-CYB Gene Mutations in the Genetic Evolution of Breast Cancer in Chad. Int J Genet Genomics. 2026;14(1):1-13. doi: 10.11648/j.ijgg.20261401.11

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@article{10.11648/j.ijgg.20261401.11,
  author = {Mahamout Ahmat Issa and Fatimata Mbaye and Mbacke Sembene},
  title = {Implication of Mt-CYB Gene Mutations in the Genetic Evolution of Breast Cancer in Chad},
  journal = {International Journal of Genetics and Genomics},
  volume = {14},
  number = {1},
  pages = {1-13},
  doi = {10.11648/j.ijgg.20261401.11},
  url = {https://doi.org/10.11648/j.ijgg.20261401.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijgg.20261401.11},
  abstract = {Background WHO reports in 2024 revealed that breast cancer affects women of all ages from puberty onward, with incidence increasing with age. Approximately 2.3 million new cases were recorded. In 2022, this disease caused 670,000 deaths worldwide. Low-penetrance genes, although not systematically associated with a high risk of breast cancer, appear to play an important role. These genes, frequently mutated in the general population, contribute significantly to breast cancer susceptibility, particularly when they interact with environmental factors or other genetic mutations. This study aims to evaluate the involvement of MT-CYB gene mutations in the progression of breast cancer among Chadian women. Methods We analyzed the variability of the MT-CYB gene in 43 patients using the PCR-sequencing technique. First, raw sequencing data were processed through the Mutation Surveyor software, which compares submitted chromatograms with the reference sequence. Next, we identified present mutations and assessed their potential impact on pathogenicity. Results Our findings highlight the potential role of the MT-CYB gene in the development of breast cancer in Chadian women. We identified 53 mutations, including 21 (39.62%) homozygous and 32 (60.37%) heterozygous mutations. Among them, 14 were already listed in the dbSNP database, while 39 were novel, with the majority found in cancerous tissues. Among these mutations, 69.81% (37/53) were non-synonymous substitutions, resulting in an amino acid change in 86.04% (37/43) of cases. Pathogenicity analysis revealed that 48.64% (18/37) were potentially deleterious, while 51.35% (19/37) were classified as neutral polymorphisms according to prediction software that considers protein structure. A detailed evaluation of the non-synonymous mutations showed that, of the 37 analyzed, 67.56% (25/37) were considered pathogenic, and 32.43% (12/37) were deemed benign. Conclusion These results highlight the crucial importance of prevention, early detection and genetic research to better understand and treat breast cancer.},
 year = {2026}
}

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TY - JOUR
T1 - Implication of Mt-CYB Gene Mutations in the Genetic Evolution of Breast Cancer in Chad
AU - Mahamout Ahmat Issa
AU - Fatimata Mbaye
AU - Mbacke Sembene
Y1 - 2026/01/20
PY - 2026
N1 - https://doi.org/10.11648/j.ijgg.20261401.11
DO - 10.11648/j.ijgg.20261401.11
T2 - International Journal of Genetics and Genomics
JF - International Journal of Genetics and Genomics
JO - International Journal of Genetics and Genomics
SP - 1
EP - 13
PB - Science Publishing Group
SN - 2376-7359
UR - https://doi.org/10.11648/j.ijgg.20261401.11
AB - Background WHO reports in 2024 revealed that breast cancer affects women of all ages from puberty onward, with incidence increasing with age. Approximately 2.3 million new cases were recorded. In 2022, this disease caused 670,000 deaths worldwide. Low-penetrance genes, although not systematically associated with a high risk of breast cancer, appear to play an important role. These genes, frequently mutated in the general population, contribute significantly to breast cancer susceptibility, particularly when they interact with environmental factors or other genetic mutations. This study aims to evaluate the involvement of MT-CYB gene mutations in the progression of breast cancer among Chadian women. Methods We analyzed the variability of the MT-CYB gene in 43 patients using the PCR-sequencing technique. First, raw sequencing data were processed through the Mutation Surveyor software, which compares submitted chromatograms with the reference sequence. Next, we identified present mutations and assessed their potential impact on pathogenicity. Results Our findings highlight the potential role of the MT-CYB gene in the development of breast cancer in Chadian women. We identified 53 mutations, including 21 (39.62%) homozygous and 32 (60.37%) heterozygous mutations. Among them, 14 were already listed in the dbSNP database, while 39 were novel, with the majority found in cancerous tissues. Among these mutations, 69.81% (37/53) were non-synonymous substitutions, resulting in an amino acid change in 86.04% (37/43) of cases. Pathogenicity analysis revealed that 48.64% (18/37) were potentially deleterious, while 51.35% (19/37) were classified as neutral polymorphisms according to prediction software that considers protein structure. A detailed evaluation of the non-synonymous mutations showed that, of the 37 analyzed, 67.56% (25/37) were considered pathogenic, and 32.43% (12/37) were deemed benign. Conclusion These results highlight the crucial importance of prevention, early detection and genetic research to better understand and treat breast cancer.
VL - 14
IS - 1
ER -

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Mahamout Ahmat Issa

Faculty of Sciences and Technics, Cheikh Anta Diop University, Dakar, Senegal

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http://orcid.org/0009-0006-2120-731X
Fatimata Mbaye

Faculty of Sciences and Technics, Cheikh Anta Diop University, Dakar, Senegal

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http://orcid.org/0000-0002-1098-9858
Mbacke Sembene

Faculty of Sciences and Technics, Cheikh Anta Diop University, Dakar, Senegal

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http://orcid.org/0000-0002-3219-5194

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Table 1

Table 1. Different prediction software. Different prediction software.
Table 2

Table 2. Mutation of Interest in the MT-CYB Gene. Mutation of Interest in the MT-CYB Gene.
Table 3

Table 3. Pathogenicity of mutations. Pathogenicity of mutations.
Table 4

Table 4. Mutation prediction based on molecular mechanisms. Mutation prediction based on molecular mechanisms.
Table 5

Table 5. Structuring of amino acids using the SNPMuSiC software. Structuring of amino acids using the SNPMuSiC software.

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APA Style

Issa, M. A., Mbaye, F., Sembene, M. (2026). Implication of Mt-CYB Gene Mutations in the Genetic Evolution of Breast Cancer in Chad. International Journal of Genetics and Genomics, 14(1), 1-13. https://doi.org/10.11648/j.ijgg.20261401.11

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Issa, M. A.; Mbaye, F.; Sembene, M. Implication of Mt-CYB Gene Mutations in the Genetic Evolution of Breast Cancer in Chad. Int. J. Genet. Genomics 2026, 14(1), 1-13. doi: 10.11648/j.ijgg.20261401.11

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AMA Style

Issa MA, Mbaye F, Sembene M. Implication of Mt-CYB Gene Mutations in the Genetic Evolution of Breast Cancer in Chad. Int J Genet Genomics. 2026;14(1):1-13. doi: 10.11648/j.ijgg.20261401.11

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@article{10.11648/j.ijgg.20261401.11,
  author = {Mahamout Ahmat Issa and Fatimata Mbaye and Mbacke Sembene},
  title = {Implication of Mt-CYB Gene Mutations in the Genetic Evolution of Breast Cancer in Chad},
  journal = {International Journal of Genetics and Genomics},
  volume = {14},
  number = {1},
  pages = {1-13},
  doi = {10.11648/j.ijgg.20261401.11},
  url = {https://doi.org/10.11648/j.ijgg.20261401.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijgg.20261401.11},
  abstract = {Background WHO reports in 2024 revealed that breast cancer affects women of all ages from puberty onward, with incidence increasing with age. Approximately 2.3 million new cases were recorded. In 2022, this disease caused 670,000 deaths worldwide. Low-penetrance genes, although not systematically associated with a high risk of breast cancer, appear to play an important role. These genes, frequently mutated in the general population, contribute significantly to breast cancer susceptibility, particularly when they interact with environmental factors or other genetic mutations. This study aims to evaluate the involvement of MT-CYB gene mutations in the progression of breast cancer among Chadian women. Methods We analyzed the variability of the MT-CYB gene in 43 patients using the PCR-sequencing technique. First, raw sequencing data were processed through the Mutation Surveyor software, which compares submitted chromatograms with the reference sequence. Next, we identified present mutations and assessed their potential impact on pathogenicity. Results Our findings highlight the potential role of the MT-CYB gene in the development of breast cancer in Chadian women. We identified 53 mutations, including 21 (39.62%) homozygous and 32 (60.37%) heterozygous mutations. Among them, 14 were already listed in the dbSNP database, while 39 were novel, with the majority found in cancerous tissues. Among these mutations, 69.81% (37/53) were non-synonymous substitutions, resulting in an amino acid change in 86.04% (37/43) of cases. Pathogenicity analysis revealed that 48.64% (18/37) were potentially deleterious, while 51.35% (19/37) were classified as neutral polymorphisms according to prediction software that considers protein structure. A detailed evaluation of the non-synonymous mutations showed that, of the 37 analyzed, 67.56% (25/37) were considered pathogenic, and 32.43% (12/37) were deemed benign. Conclusion These results highlight the crucial importance of prevention, early detection and genetic research to better understand and treat breast cancer.},
 year = {2026}
}

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