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pmid:36836779

In Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkers.

Rachid Kaddoura · Fatma Alqutami · Mohamed Asbaita · Mahmood Hachim

Life (Basel, Switzerland) · 2023 doi:10.3390/life13020422

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In Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkers.
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10.3390/life13020422
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36836779
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Rachid Kaddoura
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Fatma Alqutami
[2]
Mohamed Asbaita
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Mahmood Hachim
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Life (Basel, Switzerland)
year
2023
abstract
BACKGROUND: Breast cancer is the most common type of cancer among women and is classified into multiple subtypes. Triple-negative breast cancer (TNBC) is the most aggressive subtype, with high mortality rates and limited treatment options such as chemotherapy and radiation. Due to the heterogeneity and complexity of TNBC, there is a lack of reliable biomarkers that can be used to aid in the early diagnosis and prognosis of TNBC in a non-invasive screening method. AIM: This study aims to use in silico methods to identify potential biomarkers for TNBC screening and diagnosis, as well as potential therapeutic markers. METHODS: Publicly available transcriptomic data of breast cancer patients published in the NCBI's GEO database were used in this analysis. Data were analyzed with the online tool GEO2R to identify differentially expressed genes (DEGs). Genes that were differentially expressed in more than 50% of the datasets were selected for further analysis. Metascape, Kaplan-Meier plotter, cBioPortal, and the online tool TIMER were used for functional pathway analysis to identify the biological role and functional pathways associated with these genes. Breast Cancer Gene-Expression Miner v4.7 was used to validify the obtained results in a larger cohort of datasets. RESULTS: A total of 34 genes were identified as differentially expressed in more than half of the datasets. The DEG GATA3 had the highest degree of regulation, and it plays a role in regulating other genes. The estrogen-dependent pathway was the most enriched pathway, involving four crucial genes, including GATA3. The gene FOXA1 was consistently down-regulated in TNBC in all datasets. CONCLUSIONS: The shortlisted 34 DEGs will aid clinicians in diagnosing TNBC more accurately as well as developing targeted therapies to improve patient prognosis. In vitro and in vivo studies are further recommended to validate the results of the current study.
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Each dataset was processed individually to identify DEGs using the GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/, accessed on 1 October 2021). Samples were assigned to groups based on their subtype and analyzed using the standardized parameters of the tool. These standardized parameters include automated log2 transformation of non-transformed data, empirical Bayes method of calculation through the limma statistical package, and adjustment of p value using the default Benjamini and Hochberg (false discovery rate) method. p-value < 0.05 was used to indicate statistical significance.
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sections[4] "2.2. Identification of Differentially Expressed Genes" paragraph 2
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Each dataset was processed individually to identify DEGs using the GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/, accessed on 1 October 2021). Samples were assigned to groups based on their subtype and analyzed using the standardized parameters of the tool. These standardized parameters include automated log2 transformation of non-transformed data, empirical Bayes method of calculation through the limma statistical package, and adjustment of p value using the default Benjamini and Hochberg (false discovery rate) method. p-value < 0.05 was used to indicate statistical significance.
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sections[29] "Data Availability Statement" paragraph 2
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The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
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The re-analysis of these datasets revealed a total of 1217 DEGs in all datasets (Supplementary Files S1–S9), 34 of which are consistent across five of the nine datasets (50%) (Figure 2, Table 2, Supplementary File S10), with these genes being either up- or down-regulated.
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The re-analysis of these datasets revealed a total of 1217 DEGs in all datasets (Supplementary Files S1–S9), 34 of which are consistent across five of the nine datasets (50%) (Figure 2, Table 2, Supplementary File S10), with these genes being either up- or down-regulated.
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sections[7] "3. Results" paragraph 2
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The re-analysis of these datasets revealed a total of 1217 DEGs in all datasets (Supplementary Files S1–S9), 34 of which are consistent across five of the nine datasets (50%) (Figure 2, Table 2, Supplementary File S10), with these genes being either up- or down-regulated.
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The re-analysis of these datasets revealed a total of 1217 DEGs in all datasets (Supplementary Files S1–S9), 34 of which are consistent across five of the nine datasets (50%) (Figure 2, Table 2, Supplementary File S10), with these genes being either up- or down-regulated.
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sha256
null
bytes
null
access
open
state
resolved
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reader_status
reader_required
identifier
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version
null
note
The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
access_statement_kind
data_availability_statement
sample_accessions array · 343 items
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GSM760925
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GSM760926
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GSM760927
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GSM760932
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sample_count
343
group_sizes
null
group_sizes_basis
null
platform
6884
taxon
Homo sapiens
title
Search for a gene-expression signature of breast cancer local recurrence in young women
resolution_checked_at
2026-11-01T18:46:34Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
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version
null
note
The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
access_statement_kind
data_availability_statement
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sample_count
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group_sizes
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6244
taxon
Homo sapiens
title
Transcriptomic analysis of breast cancer
resolution_checked_at
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resolution_check
identifier_resolution_only_no_download_no_licence_read
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version
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note
The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
access_statement_kind
data_availability_statement
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sample_count
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group_sizes
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group_sizes_basis
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platform
6244
taxon
Homo sapiens
title
Molecular Profiles of Human Breast Cancer and Their Association with Tumor Subtypes and Disease Prognosis (Affymetrix)
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
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note
The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
access_statement_kind
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275
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null
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null
platform
15932;15931
taxon
Homo sapiens
title
Classifications within Molecular Subtypes Enables Identification of BRCA1/BRCA2 Mutation Carriers by RNA Tumor Profiling
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2026-11-01T18:46:34Z
resolution_check
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[8] object · 15 fields
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note
The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
access_statement_kind
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139
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96
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Homo sapiens
title
Expression Profiles of Breast Tumors from Singapore and Europe
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2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
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[9] object · 15 fields
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The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
structure object · 15 fields
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The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
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6947
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Genome-wide multi-omics profiling reveals extensive genetic complexity in 8p11-p12 amplified breast carcinomas [expression]
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Each dataset was processed individually to identify DEGs using the GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/, accessed on 1 October 2021). Samples were assigned to groups based on their subtype and analyzed using the standardized parameters of the tool. These standardized parameters include automated log2 transformation of non-transformed data, empirical Bayes method of calculation through the limma statistical package, and adjustment of p value using the default Benjamini and Hochberg (false discovery rate) method. p-value < 0.05 was used to indicate statistical significance.
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The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none.
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tier
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The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.
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[0]
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s to improve patient prognosis. In vitro and in vivo studies are further recommended to validate
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Each dataset was processed individually to identify DEGs using the GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/, accessed on 1 October 2021). Samples were assigned to groups based on their subtype and analyzed using the standardized parameters of the tool. These standardized parameters include automated log2 transformation of non-transformed data, empirical Bayes method of calculation through the limma statistical package, and adjustment of p value using the default Benjamini and Hochberg (false discovery rate) method. p-value < 0.05 was used to indicate statistical significance.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36836779", "kind": "science-record", "revision": 4, "content_hash": "sha256:71cddf92b9fed4f69fdd945a7c553539a90aa6f3bfe98c44502653adee8b820a", "created_at": "2026-11-01T19:01:27Z", "updated_at": "2026-11-01T22:51:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "In Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkers.", "doi": "10.3390/life13020422", "pmid": "36836779", "authors": ["Rachid Kaddoura", "Fatma Alqutami", "Mohamed Asbaita", "Mahmood Hachim"], "journal": "Life (Basel, Switzerland)", "year": 2023, "abstract": "BACKGROUND: Breast cancer is the most common type of cancer among women and is classified into multiple subtypes. Triple-negative breast cancer (TNBC) is the most aggressive subtype, with high mortality rates and limited treatment options such as chemotherapy and radiation. Due to the heterogeneity and complexity of TNBC, there is a lack of reliable biomarkers that can be used to aid in the early diagnosis and prognosis of TNBC in a non-invasive screening method.\nAIM: This study aims to use in silico methods to identify potential biomarkers for TNBC screening and diagnosis, as well as potential therapeutic markers.\nMETHODS: Publicly available transcriptomic data of breast cancer patients published in the NCBI's GEO database were used in this analysis. Data were analyzed with the online tool GEO2R to identify differentially expressed genes (DEGs). Genes that were differentially expressed in more than 50% of the datasets were selected for further analysis. Metascape, Kaplan-Meier plotter, cBioPortal, and the online tool TIMER were used for functional pathway analysis to identify the biological role and functional pathways associated with these genes. Breast Cancer Gene-Expression Miner v4.7 was used to validify the obtained results in a larger cohort of datasets.\nRESULTS: A total of 34 genes were identified as differentially expressed in more than half of the datasets. The DEG GATA3 had the highest degree of regulation, and it plays a role in regulating other genes. The estrogen-dependent pathway was the most enriched pathway, involving four crucial genes, including GATA3. The gene FOXA1 was consistently down-regulated in TNBC in all datasets.\nCONCLUSIONS: The shortlisted 34 DEGs will aid clinicians in diagnosing TNBC more accurately as well as developing targeted therapies to improve patient prognosis. In vitro and in vivo studies are further recommended to validate the results of the current study.", "claims": [{"id": "claim-method", "statement": "Each dataset was processed individually to identify DEGs using the GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/, accessed on 1 October 2021). Samples were assigned to groups based on their subtype and analyzed using the standardized parameters of the tool. These standardized parameters include automated log2 transformation of non-transformed data, empirical Bayes method of calculation through the limma statistical package, and adjustment of p value using the default Benjamini and Hochberg (false discovery rate) method. p-value < 0.05 was used to indicate statistical significance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36836779", "locator": "sections[4] \"2.2. 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These standardized parameters include automated log2 transformation of non-transformed data, empirical Bayes method of calculation through the limma statistical package, and adjustment of p value using the default Benjamini and Hochberg (false discovery rate) method. p-value < 0.05 was used to indicate statistical significance."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36836779", "locator": "sections[29] \"Data Availability Statement\" paragraph 2", "quote": "The datasets supporting the conclusion of this article are available in the NCBI repository, (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 October 2021), with their accession codes in Table 1."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The re-analysis of these datasets revealed a total of 1217 DEGs in all datasets (Supplementary Files S1–S9), 34 of which are consistent across five of the nine datasets (50%) (Figure 2, Table 2, Supplementary File S10), with these genes being either up- or down-regulated.", "kind": "quantitative", "target": {"value": 50.0, "unit": "per cent", "tolerance": null, "source": "The re-analysis of these datasets revealed a total of 1217 DEGs in all datasets (Supplementary Files S1–S9), 34 of which are consistent across five of the nine datasets (50%) (Figure 2, Table 2, Supplementary File S10), with these genes being either up- or down-regulated."}, "evidence": [{"asset": "source:pmid:36836779", "locator": "sections[7] \"3. 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