Identifying Survival Subtypes of Esophageal Squamous Cell Carcinoma Patients: An Application of Deep Learning in Gene Expression Data Analysis Article Swipe
Zahra Kousehlou
,
Ebrahim Hajizadeh
,
Leili Tapak
,
Ahmad Shalbaf
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5812/ijcm-145929
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5812/ijcm-145929
Background: Esophageal squamous cell carcinoma (ESCC) is one of the most lethal types of cancer. Late diagnosis significantly decreases patient survival rates. Objectives: The study aimed to identify survival groups for patients with ESCC and find predictive biomarkers of time-to-death from ESCC using state-of-the-art deep learning (DL) and machine learning algorithms. Methods: Expression profiles of 60 ESCC patients, along with their demographic and clinical variables, were downloaded from the GEO dataset. A DL autoencoder model was employed to extract lncRNA features. The univariate Cox proportional hazard (Cox-PH) model was used to select significant extracted features related to patient survival. Hierarchical clustering (HC) identified risk groups, followed by a decision trees algorithm which was used to identify lncRNA profiles. We used Python.3.7 and R.4.0.1 software. Results: Inputs of the autoencoder were 8,900 long noncoding RNAs (lncRNAs), of which 1000 features were extracted. Out of the features, 42 lncRNAs were significantly related to time-to-death using the Cox-PH model and used as input for clustering of patients into high and low-risk groups (P-value of log-rank test = 0.022). These groups were then labeled for supervised HC. The C5.0 algorithm achieved an overall accuracy of 0.929 on the test set and identified four hub lncRNAs associated with time-to-death. Conclusions: Novel discovered lncRNAs lnc-FAM84A-1, LINC01866, lnc-KCNE4-2 and lnc-NUDT12-4 implicated in the pathogenesis of death from ESCC. Our findings represent a significant advancement in understanding the role of lncRNAs on ESCC prognosis. Further research is necessary to confirm the potential and clinical application of these lncRNAs.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5812/ijcm-145929
- https://brieflands.com/articles/ijcm-145929.pdf
- OA Status
- diamond
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402100455
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402100455Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5812/ijcm-145929Digital Object Identifier
- Title
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Identifying Survival Subtypes of Esophageal Squamous Cell Carcinoma Patients: An Application of Deep Learning in Gene Expression Data AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-01Full publication date if available
- Authors
-
Zahra Kousehlou, Ebrahim Hajizadeh, Leili Tapak, Ahmad ShalbafList of authors in order
- Landing page
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https://doi.org/10.5812/ijcm-145929Publisher landing page
- PDF URL
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https://brieflands.com/articles/ijcm-145929.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://brieflands.com/articles/ijcm-145929.pdfDirect OA link when available
- Concepts
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Esophageal squamous cell carcinoma, Basal cell, Gene expression, Gene, Medicine, Cancer research, Pathology, Carcinoma, Oncology, Biology, Internal medicine, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.long | 131 |
| abstract_inverted_index.most | 10 |
| abstract_inverted_index.risk | 103 |
| abstract_inverted_index.role | 230 |
| abstract_inverted_index.test | 172, 194 |
| abstract_inverted_index.then | 178 |
| abstract_inverted_index.used | 89, 113, 119, 157 |
| abstract_inverted_index.were | 65, 129, 139, 147, 177 |
| abstract_inverted_index.with | 32, 59, 202 |
| abstract_inverted_index.0.929 | 191 |
| abstract_inverted_index.8,900 | 130 |
| abstract_inverted_index.ESCC. | 220 |
| abstract_inverted_index.Novel | 205 |
| abstract_inverted_index.These | 175 |
| abstract_inverted_index.aimed | 25 |
| abstract_inverted_index.along | 58 |
| abstract_inverted_index.death | 218 |
| abstract_inverted_index.input | 159 |
| abstract_inverted_index.model | 74, 87, 155 |
| abstract_inverted_index.study | 24 |
| abstract_inverted_index.their | 60 |
| abstract_inverted_index.these | 248 |
| abstract_inverted_index.trees | 109 |
| abstract_inverted_index.types | 12 |
| abstract_inverted_index.using | 42, 152 |
| abstract_inverted_index.which | 111, 136 |
| abstract_inverted_index.(ESCC) | 5 |
| abstract_inverted_index.Cox-PH | 154 |
| abstract_inverted_index.Inputs | 125 |
| abstract_inverted_index.groups | 29, 168, 176 |
| abstract_inverted_index.hazard | 85 |
| abstract_inverted_index.lethal | 11 |
| abstract_inverted_index.lncRNA | 79, 116 |
| abstract_inverted_index.rates. | 21 |
| abstract_inverted_index.select | 91 |
| abstract_inverted_index.0.022). | 174 |
| abstract_inverted_index.Further | 236 |
| abstract_inverted_index.R.4.0.1 | 122 |
| abstract_inverted_index.cancer. | 14 |
| abstract_inverted_index.confirm | 241 |
| abstract_inverted_index.extract | 78 |
| abstract_inverted_index.groups, | 104 |
| abstract_inverted_index.labeled | 179 |
| abstract_inverted_index.lncRNAs | 146, 200, 207, 232 |
| abstract_inverted_index.machine | 48 |
| abstract_inverted_index.overall | 188 |
| abstract_inverted_index.patient | 19, 97 |
| abstract_inverted_index.related | 95, 149 |
| abstract_inverted_index.(Cox-PH) | 86 |
| abstract_inverted_index.(P-value | 169 |
| abstract_inverted_index.Methods: | 51 |
| abstract_inverted_index.Results: | 124 |
| abstract_inverted_index.accuracy | 189 |
| abstract_inverted_index.achieved | 186 |
| abstract_inverted_index.clinical | 63, 245 |
| abstract_inverted_index.dataset. | 70 |
| abstract_inverted_index.decision | 108 |
| abstract_inverted_index.employed | 76 |
| abstract_inverted_index.features | 94, 138 |
| abstract_inverted_index.findings | 222 |
| abstract_inverted_index.followed | 105 |
| abstract_inverted_index.identify | 27, 115 |
| abstract_inverted_index.learning | 45, 49 |
| abstract_inverted_index.lncRNAs. | 249 |
| abstract_inverted_index.log-rank | 171 |
| abstract_inverted_index.low-risk | 167 |
| abstract_inverted_index.patients | 31, 163 |
| abstract_inverted_index.profiles | 53 |
| abstract_inverted_index.research | 237 |
| abstract_inverted_index.squamous | 2 |
| abstract_inverted_index.survival | 20, 28 |
| abstract_inverted_index.algorithm | 110, 185 |
| abstract_inverted_index.carcinoma | 4 |
| abstract_inverted_index.decreases | 18 |
| abstract_inverted_index.diagnosis | 16 |
| abstract_inverted_index.extracted | 93 |
| abstract_inverted_index.features, | 144 |
| abstract_inverted_index.features. | 80 |
| abstract_inverted_index.necessary | 239 |
| abstract_inverted_index.noncoding | 132 |
| abstract_inverted_index.patients, | 57 |
| abstract_inverted_index.potential | 243 |
| abstract_inverted_index.profiles. | 117 |
| abstract_inverted_index.represent | 223 |
| abstract_inverted_index.software. | 123 |
| abstract_inverted_index.survival. | 98 |
| abstract_inverted_index.(lncRNAs), | 134 |
| abstract_inverted_index.Esophageal | 1 |
| abstract_inverted_index.Expression | 52 |
| abstract_inverted_index.LINC01866, | 209 |
| abstract_inverted_index.Python.3.7 | 120 |
| abstract_inverted_index.associated | 201 |
| abstract_inverted_index.biomarkers | 37 |
| abstract_inverted_index.clustering | 100, 161 |
| abstract_inverted_index.discovered | 206 |
| abstract_inverted_index.downloaded | 66 |
| abstract_inverted_index.extracted. | 140 |
| abstract_inverted_index.identified | 102, 197 |
| abstract_inverted_index.implicated | 213 |
| abstract_inverted_index.predictive | 36 |
| abstract_inverted_index.prognosis. | 235 |
| abstract_inverted_index.supervised | 181 |
| abstract_inverted_index.univariate | 82 |
| abstract_inverted_index.variables, | 64 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Objectives: | 22 |
| abstract_inverted_index.advancement | 226 |
| abstract_inverted_index.algorithms. | 50 |
| abstract_inverted_index.application | 246 |
| abstract_inverted_index.autoencoder | 73, 128 |
| abstract_inverted_index.demographic | 61 |
| abstract_inverted_index.lnc-KCNE4-2 | 210 |
| abstract_inverted_index.significant | 92, 225 |
| abstract_inverted_index.Conclusions: | 204 |
| abstract_inverted_index.Hierarchical | 99 |
| abstract_inverted_index.lnc-NUDT12-4 | 212 |
| abstract_inverted_index.pathogenesis | 216 |
| abstract_inverted_index.proportional | 84 |
| abstract_inverted_index.lnc-FAM84A-1, | 208 |
| abstract_inverted_index.significantly | 17, 148 |
| abstract_inverted_index.time-to-death | 39, 151 |
| abstract_inverted_index.understanding | 228 |
| abstract_inverted_index.time-to-death. | 203 |
| abstract_inverted_index.state-of-the-art | 43 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.29471186 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |