Amirehsan Ghasemi, Soheil Hashtarkhani, David L. Schwartz, Arash Shaban-Nejad
With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians and is considered a black-box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer-reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model-agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree-based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree-based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI-enabled health systems and medical devices and, ultimately, the quality of care and outcomes.
{"title":"Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review","authors":"Amirehsan Ghasemi, Soheil Hashtarkhani, David L. Schwartz, Arash Shaban-Nejad","doi":"10.1002/cai2.136","DOIUrl":"https://doi.org/10.1002/cai2.136","url":null,"abstract":"<p>With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians and is considered a black-box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer-reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model-agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree-based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree-based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI-enabled health systems and medical devices and, ultimately, the quality of care and outcomes.</p>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiong Chen, Qinchang Chen, Yuanfang Liu, Ya Qiu, Lin Lv, Zhengtao Zhang, Xuntao Yin, Fangpeng Shu
Background
Bone marrow is the leading site for metastasis from neuroblastoma and affects the prognosis of patients with neuroblastoma. However, the accurate diagnosis of bone marrow metastasis is limited by the high spatial and temporal heterogeneity of neuroblastoma. Radiomics analysis has been applied in various cancers to build accurate diagnostic models but has not yet been applied to bone marrow metastasis of neuroblastoma.
Methods
We retrospectively collected information from 187 patients pathologically diagnosed with neuroblastoma and divided them into training and validation sets in a ratio of 7:3. A total of 2632 radiomics features were retrieved from venous and arterial phases of contrast-enhanced computed tomography (CT), and nine machine learning approaches were used to build radiomics models, including multilayer perceptron (MLP), extreme gradient boosting, and random forest. We also constructed radiomics-clinical models that combined radiomics features with clinical predictors such as age, gender, ascites, and lymph gland metastasis. The performance of the models was evaluated with receiver operating characteristics (ROC) curves, calibration curves, and risk decile plots.
Results
The MLP radiomics model yielded an area under the ROC curve (AUC) of 0.97 (95% confidence interval [CI]: 0.95–0.99) on the training set and 0.90 (95% CI: 0.82–0.95) on the validation set. The radiomics-clinical model using an MLP yielded an AUC of 0.93 (95% CI: 0.89–0.96) on the training set and 0.91 (95% CI: 0.85–0.97) on the validation set.
Conclusions
MLP-based radiomics and radiomics-clinical models can precisely predict bone marrow metastasis in patients with neuroblastoma.
{"title":"Radiomics models to predict bone marrow metastasis of neuroblastoma using CT","authors":"Xiong Chen, Qinchang Chen, Yuanfang Liu, Ya Qiu, Lin Lv, Zhengtao Zhang, Xuntao Yin, Fangpeng Shu","doi":"10.1002/cai2.135","DOIUrl":"10.1002/cai2.135","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Bone marrow is the leading site for metastasis from neuroblastoma and affects the prognosis of patients with neuroblastoma. However, the accurate diagnosis of bone marrow metastasis is limited by the high spatial and temporal heterogeneity of neuroblastoma. Radiomics analysis has been applied in various cancers to build accurate diagnostic models but has not yet been applied to bone marrow metastasis of neuroblastoma.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We retrospectively collected information from 187 patients pathologically diagnosed with neuroblastoma and divided them into training and validation sets in a ratio of 7:3. A total of 2632 radiomics features were retrieved from venous and arterial phases of contrast-enhanced computed tomography (CT), and nine machine learning approaches were used to build radiomics models, including multilayer perceptron (MLP), extreme gradient boosting, and random forest. We also constructed radiomics-clinical models that combined radiomics features with clinical predictors such as age, gender, ascites, and lymph gland metastasis. The performance of the models was evaluated with receiver operating characteristics (ROC) curves, calibration curves, and risk decile plots.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The MLP radiomics model yielded an area under the ROC curve (AUC) of 0.97 (95% confidence interval [CI]: 0.95–0.99) on the training set and 0.90 (95% CI: 0.82–0.95) on the validation set. The radiomics-clinical model using an MLP yielded an AUC of 0.93 (95% CI: 0.89–0.96) on the training set and 0.91 (95% CI: 0.85–0.97) on the validation set.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>MLP-based radiomics and radiomics-clinical models can precisely predict bone marrow metastasis in patients with neuroblastoma.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11212276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141474386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yihao Wang, Kaixin Yan, Han Duan, Ning Tao, Shaoning Zhu, Yuning Zhang, Yonggang You, Zhen Zhang, Hua Wang, Shunying Hu
Background
Clinical studies have shown that atherosclerotic cardiovascular disease and cancer often co-exist in the same individual. The present study aimed to investigate the role of high-fat-diet (HFD)-induced obesity in the coexistence of the two diseases and the underlying mechanism in apolipoprotein E-knockout (ApoE−/−) mice.
Methods
Male ApoE−/− mice were fed with a HFD or a normal diet (ND) for 15 weeks. On the first day of Week 13, the mice were inoculated subcutaneously in the right axilla with Lewis lung cancer cells. At Weeks 12 and 15, serum lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) and vascular endothelial growth factor levels were measured by enzyme-linked immunosorbent assay, and blood monocytes and macrophages were measured by fluorescence-activated cell sorting. At Week 15, the volume and weight of the local subcutaneous lung cancer and metastatic lung cancer and the amount of aortic atherosclerosis were measured.
Results
At Week 15, compared with mice in the ND group, those in the HFD group had a larger volume of local subcutaneous cancer (p = 0.0004), heavier tumors (p = 0.0235), more metastatic cancer in the lungs (p < 0.0001), a larger area of lung involved in metastatic cancer (p = 0.0031), and larger areas of atherosclerosis in the aorta (p < 0.0001). At Week 12, serum LOX-1, serum vascular endothelial growth factor, and proportions of blood monocytes and macrophages were significantly higher in the HFD group than those in the ND group (p = 0.0002, p = 0.0029, p = 0.0480, and p = 0.0106, respectively); this trend persisted until Week 15 (p = 0.0014, p = 0.0012, p = 0.0001, and p = 0.0204).
Conclusions
In this study, HFD-induced obesity could simultaneously promote progression of lung cancer and atherosclerosis in the same mouse. HFD-induced upregulation of LOX-1 may play an important role in the simultaneous progression of these two conditions via the inflammatory response and VEGF.
{"title":"High-fat-diet-induced obesity promotes simultaneous progression of lung cancer and atherosclerosis in apolipoprotein E-knockout mice","authors":"Yihao Wang, Kaixin Yan, Han Duan, Ning Tao, Shaoning Zhu, Yuning Zhang, Yonggang You, Zhen Zhang, Hua Wang, Shunying Hu","doi":"10.1002/cai2.127","DOIUrl":"https://doi.org/10.1002/cai2.127","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Clinical studies have shown that atherosclerotic cardiovascular disease and cancer often co-exist in the same individual. The present study aimed to investigate the role of high-fat-diet (HFD)-induced obesity in the coexistence of the two diseases and the underlying mechanism in apolipoprotein E-knockout (ApoE<sup>−/−</sup>) mice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Male ApoE<sup>−/−</sup> mice were fed with a HFD or a normal diet (ND) for 15 weeks. On the first day of Week 13, the mice were inoculated subcutaneously in the right axilla with Lewis lung cancer cells. At Weeks 12 and 15, serum lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) and vascular endothelial growth factor levels were measured by enzyme-linked immunosorbent assay, and blood monocytes and macrophages were measured by fluorescence-activated cell sorting. At Week 15, the volume and weight of the local subcutaneous lung cancer and metastatic lung cancer and the amount of aortic atherosclerosis were measured.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>At Week 15, compared with mice in the ND group, those in the HFD group had a larger volume of local subcutaneous cancer (<i>p</i> = 0.0004), heavier tumors (<i>p</i> = 0.0235), more metastatic cancer in the lungs (<i>p</i> < 0.0001), a larger area of lung involved in metastatic cancer (<i>p</i> = 0.0031), and larger areas of atherosclerosis in the aorta (<i>p</i> < 0.0001). At Week 12, serum LOX-1, serum vascular endothelial growth factor, and proportions of blood monocytes and macrophages were significantly higher in the HFD group than those in the ND group (<i>p</i> = 0.0002, <i>p</i> = 0.0029, <i>p</i> = 0.0480, and <i>p</i> = 0.0106, respectively); this trend persisted until Week 15 (<i>p</i> = 0.0014, <i>p</i> = 0.0012, <i>p</i> = 0.0001, and <i>p</i> = 0.0204).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In this study, HFD-induced obesity could simultaneously promote progression of lung cancer and atherosclerosis in the same mouse. HFD-induced upregulation of LOX-1 may play an important role in the simultaneous progression of these two conditions via the inflammatory response and VEGF.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The current standard of care for advanced human epidermal growth factor receptor 2 (HER2)-positive breast cancer is pertuzumab plus trastuzumab and docetaxel as first-line therapy. However, with the development of newer treatment regimens, there is a lack of evidence regarding which is the optimal treatment strategy. The aim of this network meta-analysis was to evaluate the efficacy and safety of first-line regimens for advanced HER2-positive breast cancer by indirect comparisons.
Methods
A systematic review and Bayesian network meta-analysis were conducted. The PubMed, EMBASE, and Cochrane Library databases were searched for relevant articles published through to December 2023. The hazard ratio (HR) and 95% credible interval (CrI) were used to compare progression-free survival (PFS) between treatments, and the odds ratio and 95% CrI were used to compare the objective response rate (ORR) and safety.
Results
Twenty randomized clinical trials that included 15 regimens and 7094 patients were analyzed. Compared with the traditional trastuzumab and docetaxel regimen, PFS was longer on the pyrotinib and trastuzumab plus docetaxel regimen (HR: 0.41, 95% CrI: 0.22–0.75) and the pertuzumab and trastuzumab plus docetaxel regimen (HR: 0.65, 95% CrI: 0.43–0.98). Consistent with the results for PFS, the ORR was better on the pyrotinib and trastuzumab plus docetaxel regimen and the pertuzumab and trastuzumab plus docetaxel regimen than on the traditional trastuzumab and docetaxel regimen. The surface under the cumulative ranking curve indicated that the pyrotinib and trastuzumab plus docetaxel regimen was most likely to rank first in achieving the best PFS and ORR. Comparable results were found for grade ≥3 AE rates of ≥10%.
Conclusions
Our results suggest that the pyrotinib and trastuzumab plus docetaxel regimen is most likely to be the optimal first-line therapy for patients with HER2-positive breast cancer.
{"title":"Efficacy and safety of first-line regimens for advanced HER2-positive breast cancer: A Bayesian network meta-analysis","authors":"Lixi Li, Yun Wu, Bo Lan, Fei Ma","doi":"10.1002/cai2.126","DOIUrl":"https://doi.org/10.1002/cai2.126","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The current standard of care for advanced human epidermal growth factor receptor 2 (HER2)-positive breast cancer is pertuzumab plus trastuzumab and docetaxel as first-line therapy. However, with the development of newer treatment regimens, there is a lack of evidence regarding which is the optimal treatment strategy. The aim of this network meta-analysis was to evaluate the efficacy and safety of first-line regimens for advanced HER2-positive breast cancer by indirect comparisons.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A systematic review and Bayesian network meta-analysis were conducted. The PubMed, EMBASE, and Cochrane Library databases were searched for relevant articles published through to December 2023. The hazard ratio (HR) and 95% credible interval (CrI) were used to compare progression-free survival (PFS) between treatments, and the odds ratio and 95% CrI were used to compare the objective response rate (ORR) and safety.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Twenty randomized clinical trials that included 15 regimens and 7094 patients were analyzed. Compared with the traditional trastuzumab and docetaxel regimen, PFS was longer on the pyrotinib and trastuzumab plus docetaxel regimen (HR: 0.41, 95% CrI: 0.22–0.75) and the pertuzumab and trastuzumab plus docetaxel regimen (HR: 0.65, 95% CrI: 0.43–0.98). Consistent with the results for PFS, the ORR was better on the pyrotinib and trastuzumab plus docetaxel regimen and the pertuzumab and trastuzumab plus docetaxel regimen than on the traditional trastuzumab and docetaxel regimen. The surface under the cumulative ranking curve indicated that the pyrotinib and trastuzumab plus docetaxel regimen was most likely to rank first in achieving the best PFS and ORR. Comparable results were found for grade ≥3 AE rates of ≥10%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our results suggest that the pyrotinib and trastuzumab plus docetaxel regimen is most likely to be the optimal first-line therapy for patients with HER2-positive breast cancer.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuan-yuan Sun, Hai-cheng Gao, Peng Guo, Na Sun, Chan Peng, Zhi-hua Cheng, Jing Gu, Jin-yi Liu, Fei Han
Background
Non-small cell lung cancer (NSCLC), including the lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes, is a malignant tumor type with a poor 5-year survival rate. The identification of new powerful diagnostic biomarkers, prognostic biomarkers, and potential therapeutic targets in NSCLC is urgently required.
Methods
The UCSC Xena, UALCAN, and GEO databases were used to screen and analyze differentially expressed genes, regulatory modes, and genetic/epigenetic alterations in NSCLC. The UCSC Xena database, GEO database, tissue microarray, and immunohistochemistry staining analyses were used to evaluate the diagnostic and prognostic values. Gain-of-function assays were performed to examine the roles. The ESTIMATE, TIMER, Linked Omics, STRING, and DAVID algorithms were used to analyze potential molecular mechanisms.
Results
NR3C2 was identified as a potentially important molecule in NSCLC. NR3C2 is expressed at low levels in NSCLC, LUAD, and LUSC tissues, which is significantly related to the clinical indexes of these patients. Receiver operating characteristic curve analysis suggests that the altered NR3C2 expression patterns have diagnostic value in NSCLC, LUAD, and especially LUSC patients. Decreased NR3C2 expression levels can help predict poor prognosis in NSCLC and LUAD patients but not in LUSC patients. These results have been confirmed both with database analysis and real-world clinical samples on a tissue microarray. Copy number variation contributes to low NR3C2 expression levels in NSCLC and LUAD, while promoter DNA methylation is involved in its downregulation in LUSC. Two NR3C2 promoter methylation sites have high sensitivity and specificity for LUSC diagnosis with clinical application potential. NR3C2 may be a key participant in NSCLC development and progression and is closely associated with the tumor microenvironment and immune cell infiltration. NR3C2 co-expressed genes are involved in many cancer-related signaling pathways, further supporting a potentially significant role of NR3C2 in NSCLC.
Conclusions
NR3C2 is a novel potential diagnostic and prognostic biomarker and therapeutic target in NSCLC.
{"title":"Identification of NR3C2 as a functional diagnostic and prognostic biomarker and potential therapeutic target in non-small cell lung cancer","authors":"Yuan-yuan Sun, Hai-cheng Gao, Peng Guo, Na Sun, Chan Peng, Zhi-hua Cheng, Jing Gu, Jin-yi Liu, Fei Han","doi":"10.1002/cai2.122","DOIUrl":"https://doi.org/10.1002/cai2.122","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Non-small cell lung cancer (NSCLC), including the lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes, is a malignant tumor type with a poor 5-year survival rate. The identification of new powerful diagnostic biomarkers, prognostic biomarkers, and potential therapeutic targets in NSCLC is urgently required.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The UCSC Xena, UALCAN, and GEO databases were used to screen and analyze differentially expressed genes, regulatory modes, and genetic/epigenetic alterations in NSCLC. The UCSC Xena database, GEO database, tissue microarray, and immunohistochemistry staining analyses were used to evaluate the diagnostic and prognostic values. Gain-of-function assays were performed to examine the roles. The ESTIMATE, TIMER, Linked Omics, STRING, and DAVID algorithms were used to analyze potential molecular mechanisms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>NR3C2 was identified as a potentially important molecule in NSCLC. NR3C2 is expressed at low levels in NSCLC, LUAD, and LUSC tissues, which is significantly related to the clinical indexes of these patients. Receiver operating characteristic curve analysis suggests that the altered NR3C2 expression patterns have diagnostic value in NSCLC, LUAD, and especially LUSC patients. Decreased NR3C2 expression levels can help predict poor prognosis in NSCLC and LUAD patients but not in LUSC patients. These results have been confirmed both with database analysis and real-world clinical samples on a tissue microarray. Copy number variation contributes to low NR3C2 expression levels in NSCLC and LUAD, while promoter DNA methylation is involved in its downregulation in LUSC. Two NR3C2 promoter methylation sites have high sensitivity and specificity for LUSC diagnosis with clinical application potential. NR3C2 may be a key participant in NSCLC development and progression and is closely associated with the tumor microenvironment and immune cell infiltration. NR3C2 co-expressed genes are involved in many cancer-related signaling pathways, further supporting a potentially significant role of NR3C2 in NSCLC.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>NR3C2 is a novel potential diagnostic and prognostic biomarker and therapeutic target in NSCLC.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141073706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Increasing evidence has shown that connexins are involved in the regulation of tumor development, immune escape, and drug resistance. This study investigated the gene expression patterns, prognostic values, and potential mechanisms of connexins in breast cancer.
Methods
We conducted a comprehensive analysis of connexins using public gene and protein expression databases and clinical samples from our institution. Connexin mRNA expressions in breast cancer and matched normal tissues were compared, and multiomics studies were performed.
Results
Gap junction beta-2 mRNA was overexpressed in breast cancers of different pathological types and molecular subtypes, and its high expression was associated with poor prognosis. The tumor membrane of the gap junction beta-2 mutated group was positive, and the corresponding protein was expressed. Somatic mutation and copy number variation of gap junction beta-2 are rare in breast cancer. The gap junction beta-2 transcription level in the p110α subunit of the phosphoinositide 3-kinase mutant subgroup was higher than that in the wild-type subgroup. Gap junction beta-2 was associated with the phosphoinositide 3-kinase-Akt signaling pathway, extracellular matrix–receptor interaction, focal adhesion, and proteoglycans in cancer. Furthermore, gap junction beta-2 overexpression may be associated with phosphoinositide 3-kinase and histone deacetylase inhibitor resistance, and its expression level correlated with infiltrating CD8+ T cells, macrophages, neutrophils, and dendritic cells.
Conclusions
Gap junction beta-2 may be a promising therapeutic target for targeted therapy and immunotherapy and may be used to predict breast cancer prognosis.
{"title":"Integrative analyses identified gap junction beta-2 as a prognostic biomarker and therapeutic target for breast cancer","authors":"Di Zhang, Lixi Li, Fei Ma","doi":"10.1002/cai2.128","DOIUrl":"https://doi.org/10.1002/cai2.128","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Increasing evidence has shown that connexins are involved in the regulation of tumor development, immune escape, and drug resistance. This study investigated the gene expression patterns, prognostic values, and potential mechanisms of connexins in breast cancer.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a comprehensive analysis of connexins using public gene and protein expression databases and clinical samples from our institution. Connexin mRNA expressions in breast cancer and matched normal tissues were compared, and multiomics studies were performed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Gap junction beta-2 mRNA was overexpressed in breast cancers of different pathological types and molecular subtypes, and its high expression was associated with poor prognosis. The tumor membrane of the gap junction beta-2 mutated group was positive, and the corresponding protein was expressed. Somatic mutation and copy number variation of gap junction beta-2 are rare in breast cancer. The gap junction beta-2 transcription level in the p110α subunit of the phosphoinositide 3-kinase mutant subgroup was higher than that in the wild-type subgroup. Gap junction beta-2 was associated with the phosphoinositide 3-kinase-Akt signaling pathway, extracellular matrix–receptor interaction, focal adhesion, and proteoglycans in cancer. Furthermore, gap junction beta-2 overexpression may be associated with phosphoinositide 3-kinase and histone deacetylase inhibitor resistance, and its expression level correlated with infiltrating CD8+ T cells, macrophages, neutrophils, and dendritic cells.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Gap junction beta-2 may be a promising therapeutic target for targeted therapy and immunotherapy and may be used to predict breast cancer prognosis.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141069113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Increased glycolytic activity and lactate production are characteristic features of triple-negative breast cancer (TNBC). The aim of this study was to determine whether a subset of lactate-responsive genes (LRGs) could be used to classify TNBC subtypes and predict patient outcomes.
Methods
Lactate levels were initially measured in different breast cancer (BC) cell types. Subsequently, MDA-MB-231 cells treated with 2-Deoxy-d-glucose or l-lactate were subjected to RNA sequencing (RNA-seq). The gene set variation analysis algorithm was utilized to calculate the lactate-responsive score, conduct a differential analysis, and establish an association with the extent of immune infiltration. Consensus clustering was then employed to classify TNBC patients. Tumor immune dysfunction and exclusion, cibersort, single-sample gene set enrichment analysis, and EPIC, were used to compare the tumor-infiltrating immune cells between TNBC subtypes and predict the response to immunotherapy. Furthermore, a prognostic model was developed by combining 98 machine learning algorithms, to assess the predictive significance of the LRG signature. The predictive value of immune infiltration and the immunotherapy response was also assessed. Finally, the association between lactate and various anticancer drugs was examined based on expression profile similarity principles.
Results
We found that the lactate levels of TNBC cells were significantly higher than those of other BC cell lines. Through RNA-seq, we identified 14 differentially expressed LRGs in TNBC cells under varying lactate levels. Notably, this LRG signature was associated with interleukin-17 signaling pathway dysregulation, suggesting a link between lactate metabolism and immune impairment. Furthermore, the LRG signature was used to categorize TNBC into two distinct subtypes, whereby Subtype A was characterized by immunosuppression, whereas Subtype B was characterized by immune activation.
Conclusion
We identified an LRG signature in TNBC, which could be used to predict the prognosis of patients with TNBC and gauge their response to immunotherapy. Our findings may help guide the precision treatment of patients with TNBC.
{"title":"A lactate-responsive gene signature predicts the prognosis and immunotherapeutic response of patients with triple-negative breast cancer","authors":"Kaixiang Feng, Youcheng Shao, Jun Li, Xiaoqing Guan, Qin Liu, Meishun Hu, Mengfei Chu, Hui Li, Fangfang Chen, Zongbi Yi, Jingwei Zhang","doi":"10.1002/cai2.124","DOIUrl":"https://doi.org/10.1002/cai2.124","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Increased glycolytic activity and lactate production are characteristic features of triple-negative breast cancer (TNBC). The aim of this study was to determine whether a subset of lactate-responsive genes (LRGs) could be used to classify TNBC subtypes and predict patient outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Lactate levels were initially measured in different breast cancer (BC) cell types. Subsequently, MDA-MB-231 cells treated with 2-Deoxy-<span>d</span>-glucose or <span>l</span>-lactate were subjected to RNA sequencing (RNA-seq). The gene set variation analysis algorithm was utilized to calculate the lactate-responsive score, conduct a differential analysis, and establish an association with the extent of immune infiltration. Consensus clustering was then employed to classify TNBC patients. Tumor immune dysfunction and exclusion, cibersort, single-sample gene set enrichment analysis, and EPIC, were used to compare the tumor-infiltrating immune cells between TNBC subtypes and predict the response to immunotherapy. Furthermore, a prognostic model was developed by combining 98 machine learning algorithms, to assess the predictive significance of the LRG signature. The predictive value of immune infiltration and the immunotherapy response was also assessed. Finally, the association between lactate and various anticancer drugs was examined based on expression profile similarity principles.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We found that the lactate levels of TNBC cells were significantly higher than those of other BC cell lines. Through RNA-seq, we identified 14 differentially expressed LRGs in TNBC cells under varying lactate levels. Notably, this LRG signature was associated with interleukin-17 signaling pathway dysregulation, suggesting a link between lactate metabolism and immune impairment. Furthermore, the LRG signature was used to categorize TNBC into two distinct subtypes, whereby Subtype A was characterized by immunosuppression, whereas Subtype B was characterized by immune activation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We identified an LRG signature in TNBC, which could be used to predict the prognosis of patients with TNBC and gauge their response to immunotherapy. Our findings may help guide the precision treatment of patients with TNBC.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141069180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Di Wang, Jing Zhang, Jianchao Wang, Zhonglin Cai, Shanfeng Jin, Gang Chen
Background
Gastric cancer is a highly heterogeneous disease, presenting a major obstacle to personalized treatment. Effective markers of the immune checkpoint blockade response are needed for precise patient classification. We, therefore, divided patients with gastric cancer according to collagen gene expression to indicate their prognosis and treatment response.
Methods
We collected data for 1250 patients with gastric cancer from four cohorts. For the TCGA-STAD cohort, we used consensus clustering to stratify patients based on expression levels of 44 collagen genes and compared the prognosis and clinical characteristics between collagen subtypes. We then identified distinct transcriptomic and genetic alteration signatures for the subtypes. We analyzed the associations of collagen subtypes with the responses to chemotherapy, immunotherapy, and targeted therapy. We also established a platform-independent collagen-subtype predictor. We verified the findings in three validation cohorts (GSE84433, GSE62254, and GSE15459) and compared the collagen subtyping method with other molecular subtyping methods.
Results
We identified two subtypes of gastric adenocarcinoma: a high-expression collagen subtype (CS-H) and a low-expression collagen subtype (CS-L). Collagen subtype was an independent prognostic factor, with better overall survival in the CS-L subgroup. The inflammatory response, angiogenesis, and phosphoinositide 3-kinase (PI3K)/Akt pathways were transcriptionally active in the CS-H subtype, while DNA repair activity was significantly greater in the CS-L subtype. PIK3CA was frequently amplified in the CS-H subtype, while PIK3C2A, PIK3C2G, and PIK3R1 were frequently deleted in the CS-L subtype. CS-H subtype tumors were more sensitive to fluorouracil, while CS-L subtype tumors were more sensitive to immune checkpoint blockade. CS-L subtype was predicted to be more sensitive to HER2-targeted drugs, and CS-H subtype was predicted to be more sensitive to vascular endothelial growth factor and PI3K pathway-targeting drugs. Collagen subtyping also has the potential to be combined with existing molecular subtyping methods for better patient classification.
Conclusions
We classified gastric cancers into two subtypes based on collagen gene expression and validated these subtypes in three validation cohorts. The collagen subgroups differed in terms of prognosis, clinical characteristics, transc
{"title":"Identification of collagen subtypes of gastric cancer for distinguishing patient prognosis and therapeutic response","authors":"Di Wang, Jing Zhang, Jianchao Wang, Zhonglin Cai, Shanfeng Jin, Gang Chen","doi":"10.1002/cai2.125","DOIUrl":"https://doi.org/10.1002/cai2.125","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Gastric cancer is a highly heterogeneous disease, presenting a major obstacle to personalized treatment. Effective markers of the immune checkpoint blockade response are needed for precise patient classification. We, therefore, divided patients with gastric cancer according to collagen gene expression to indicate their prognosis and treatment response.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We collected data for 1250 patients with gastric cancer from four cohorts. For the TCGA-STAD cohort, we used consensus clustering to stratify patients based on expression levels of 44 collagen genes and compared the prognosis and clinical characteristics between collagen subtypes. We then identified distinct transcriptomic and genetic alteration signatures for the subtypes. We analyzed the associations of collagen subtypes with the responses to chemotherapy, immunotherapy, and targeted therapy. We also established a platform-independent collagen-subtype predictor. We verified the findings in three validation cohorts (GSE84433, GSE62254, and GSE15459) and compared the collagen subtyping method with other molecular subtyping methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We identified two subtypes of gastric adenocarcinoma: a high-expression collagen subtype (CS-H) and a low-expression collagen subtype (CS-L). Collagen subtype was an independent prognostic factor, with better overall survival in the CS-L subgroup. The inflammatory response, angiogenesis, and phosphoinositide 3-kinase (PI3K)/Akt pathways were transcriptionally active in the CS-H subtype, while DNA repair activity was significantly greater in the CS-L subtype. <i>PIK3CA</i> was frequently amplified in the CS-H subtype, while <i>PIK3C2A</i>, <i>PIK3C2G</i>, and <i>PIK3R1</i> were frequently deleted in the CS-L subtype. CS-H subtype tumors were more sensitive to fluorouracil, while CS-L subtype tumors were more sensitive to immune checkpoint blockade. CS-L subtype was predicted to be more sensitive to HER2-targeted drugs, and CS-H subtype was predicted to be more sensitive to vascular endothelial growth factor and PI3K pathway-targeting drugs. Collagen subtyping also has the potential to be combined with existing molecular subtyping methods for better patient classification.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We classified gastric cancers into two subtypes based on collagen gene expression and validated these subtypes in three validation cohorts. The collagen subgroups differed in terms of prognosis, clinical characteristics, transc","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140949137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Boyu Qin, Qi Xiong, Lingli Xin, Ke Li, Weiwei Shi, Qi Song, Qiong Sun, Jiakang Shao, Jing Zhang, Xiao Zhao, Jinyu Liu, Jinliang Wang, Bo Yang
Background
Pancreatic ductal adenocarcinoma (PDAC) is in urgent need of a second-line or later-line treatment strategy. We aimed to analyze the efficacy and safety of additional anlotinib, specifically anlotinib in combination with immunotherapy, in patients with PDAC who have failed first-line therapy.
Methods
Patients with pathological diagnosis of PDAC were additionally treated with anlotinib, and some patients were treated with anti-PD-1 agents at the same time, which could be retrospectively analyzed. The efficacy and safety of additional anlotinib were evaluated.
Results
A total of 23 patients were included. In patients treated with additional anlotinib, the overall median progression-free survival (PFS) was 1.8 months and the median overall survival (OS) was 6.3 months, regardless of anti-PD-1 agents. Among patients receiving additional anlotinib in combination with anti-PD-1 agents, median PFS and OS were 1.8 and 6.5 months, respectively. Adverse events (AEs) were observed in 16 patients (69.6%). In patients treated with additional anlotinib, the majority of AEs were grade 1–3. Univariate analysis revealed that patients with baseline red blood cell distribution width (RDW) <14% treated with additional anlotinib plus anti-PD-1 agents had significantly longer OS than patients with baseline RDW ≥14% (p = 0.025). Patients with additional anlotinib plus anti-PD-1 agents as second-line therapy had a longer OS than those treated as later-line therapy (p = 0.012). Multivariate analysis showed that baseline RDW was the only independent risk factor for OS (p = 0.042).
Conclusion
The combination of anlotinib and immunotherapy represents an effective add-on therapy with tolerable AEs as second- or later-line therapy in patients with PDAC, particularly in patients with baseline RDW <14%.
{"title":"Synergistic effect of additional anlotinib and immunotherapy as second-line or later-line treatment in pancreatic cancer: A retrospective cohort study","authors":"Boyu Qin, Qi Xiong, Lingli Xin, Ke Li, Weiwei Shi, Qi Song, Qiong Sun, Jiakang Shao, Jing Zhang, Xiao Zhao, Jinyu Liu, Jinliang Wang, Bo Yang","doi":"10.1002/cai2.123","DOIUrl":"https://doi.org/10.1002/cai2.123","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Pancreatic ductal adenocarcinoma (PDAC) is in urgent need of a second-line or later-line treatment strategy. We aimed to analyze the efficacy and safety of additional anlotinib, specifically anlotinib in combination with immunotherapy, in patients with PDAC who have failed first-line therapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Patients with pathological diagnosis of PDAC were additionally treated with anlotinib, and some patients were treated with anti-PD-1 agents at the same time, which could be retrospectively analyzed. The efficacy and safety of additional anlotinib were evaluated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 23 patients were included. In patients treated with additional anlotinib, the overall median progression-free survival (PFS) was 1.8 months and the median overall survival (OS) was 6.3 months, regardless of anti-PD-1 agents. Among patients receiving additional anlotinib in combination with anti-PD-1 agents, median PFS and OS were 1.8 and 6.5 months, respectively. Adverse events (AEs) were observed in 16 patients (69.6%). In patients treated with additional anlotinib, the majority of AEs were grade 1–3. Univariate analysis revealed that patients with baseline red blood cell distribution width (RDW) <14% treated with additional anlotinib plus anti-PD-1 agents had significantly longer OS than patients with baseline RDW ≥14% (<i>p</i> = 0.025). Patients with additional anlotinib plus anti-PD-1 agents as second-line therapy had a longer OS than those treated as later-line therapy (<i>p</i> = 0.012). Multivariate analysis showed that baseline RDW was the only independent risk factor for OS (<i>p</i> = 0.042).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The combination of anlotinib and immunotherapy represents an effective add-on therapy with tolerable AEs as second- or later-line therapy in patients with PDAC, particularly in patients with baseline RDW <14%.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140919250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Chen, Haiyan Zhou, Mingyu Zhang, Yafei Shi, Taifeng Li, Di Qian, Jun Yang, Feng Yu, Guohui Li
Background
The rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy effectiveness. Importantly, paclitaxel clearance varies among individuals, primarily because of genetic polymorphisms. This metabolic variability arises from a nonlinear process that is influenced by multiple single nucleotide polymorphisms (SNPs). Conventional bioinformatics methods struggle to accurately analyze this complex process and, currently, there is no established efficient algorithm for investigating SNP interactions.
Methods
We developed a novel machine-learning approach called GEP-CSIs data mining algorithm. This algorithm, an advanced version of GEP, uses linear algebra computations to handle discrete variables. The GEP-CSI algorithm calculates a fitness function score based on paclitaxel clearance data and genetic polymorphisms in patients with nonsmall cell lung cancer. The data were divided into a primary set and a validation set for the analysis.
Results
We identified and validated 1184 three-SNP combinations that had the highest fitness function values. Notably, SERPINA1, ATF3 and EGF were found to indirectly influence paclitaxel clearance by coordinating the activity of genes previously reported to be significant in paclitaxel clearance. Particularly intriguing was the discovery of a combination of three SNPs in genes FLT1, EGF and MUC16. These SNPs-related proteins were confirmed to interact with each other in the protein–protein interaction network, which formed the basis for further exploration of their functional roles and mechanisms.
Conclusion
We successfully developed an effective deep-learning algorithm tailored for the nuanced mining of SNP interactions, leveraging data on paclitaxel clearance and individual genetic polymorphisms.
{"title":"Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer","authors":"Wei Chen, Haiyan Zhou, Mingyu Zhang, Yafei Shi, Taifeng Li, Di Qian, Jun Yang, Feng Yu, Guohui Li","doi":"10.1002/cai2.110","DOIUrl":"https://doi.org/10.1002/cai2.110","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy effectiveness. Importantly, paclitaxel clearance varies among individuals, primarily because of genetic polymorphisms. This metabolic variability arises from a nonlinear process that is influenced by multiple single nucleotide polymorphisms (SNPs). Conventional bioinformatics methods struggle to accurately analyze this complex process and, currently, there is no established efficient algorithm for investigating SNP interactions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We developed a novel machine-learning approach called GEP-CSIs data mining algorithm. This algorithm, an advanced version of GEP, uses linear algebra computations to handle discrete variables. The GEP-CSI algorithm calculates a fitness function score based on paclitaxel clearance data and genetic polymorphisms in patients with nonsmall cell lung cancer. The data were divided into a primary set and a validation set for the analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We identified and validated 1184 three-SNP combinations that had the highest fitness function values. Notably, <i>SERPINA1</i>, <i>ATF3</i> and <i>EGF</i> were found to indirectly influence paclitaxel clearance by coordinating the activity of genes previously reported to be significant in paclitaxel clearance. Particularly intriguing was the discovery of a combination of three SNPs in genes <i>FLT1</i>, <i>EGF</i> and <i>MUC16</i>. These SNPs-related proteins were confirmed to interact with each other in the protein–protein interaction network, which formed the basis for further exploration of their functional roles and mechanisms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We successfully developed an effective deep-learning algorithm tailored for the nuanced mining of SNP interactions, leveraging data on paclitaxel clearance and individual genetic polymorphisms.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140914783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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