Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers

Jin, Wenyi; Yang, Qian; Chi, Hao; Wei, Kongyuan; Zhang, Pengpeng; Zhao, Guodong; Chen, Shi; Xia, Zhijia; Li, Xiaosong

doi:10.3389/fimmu.2022.1025330

Cited by 37 publications

(36 citation statements)

References 40 publications

(48 reference statements)

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“…[23][24][25] Although immunotherapy has brought many benefits to patients with different types of cancer, developing a reliable method to identify immune therapy response is of significant clinical importance. [26,27] This study confirms the potential of a TMG-derived HNSCC subtype as an approach for identifying immune therapy response.…”

Section: Discussionsupporting

confidence: 71%

Telomere maintenance genes-derived prognosis signature characterizes immune landscape and predicts prognosis of head and neck squamous cell carcinoma

et al. 2023

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Telomere dysfunction has been identified as a biological marker of cancer progression in several types of cancer, including Head and Neck Squamous Cell Carcinoma (HNSCC). This study aimed to characterize the telomere maintenance genes (TMG)-related signature in prognosis and treatment response in HNSCC. The transcriptome and clinical data of HNSCC were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases, respectively. Non-negative matrix factorization (NMF) was used to identify molecular subtypes derived from TMG. Gene set enrichment analysis (GSEA) was performed to analyze the differentially expressed pathways between subtypes, and a risk score model derived from TMG was established. Kaplan-Meier survival analysis was used to evaluate inter-group prognostic features, and the correlation between TMG-derived molecular subtypes and risk score model with immune infiltration, immunotherapy, and chemosensitivity was assessed. Two HNSCC subtypes were identified based on 59 TMG-related genes, which exhibit significant heterogeneity in prognosis, immune cell infiltration, and treatment response. Additionally, a TMG-derived risk signature containing 9 genes was developed to assess the prognosis of HNSCC patients. The signature had significant predictive ability for HNSCC prognosis and was significantly correlated with immune cell infiltration and immunotherapy response. A nomogram integrating the risk signature, N stage and radiotherapy was constructed to predict 1-, 3-, and 5-year overall survival (OS) of HNSCC patients, which had better performance than other prognostic models and included TMG-derived risk score, radiotherapy, and N stage. This study identified TMG-derived molecular subtypes in HNSCC and developed a novel prognostic score model, highlighting the potential value of TMG in HNSCC prognosis and immunotherapy.

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Section: Discussionsupporting

confidence: 71%

Telomere maintenance genes-derived prognosis signature characterizes immune landscape and predicts prognosis of head and neck squamous cell carcinoma

et al. 2023

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“…The use of gene expression profiling to classify tumor samples has been well-established in previous research (68)(69)(70)(71)(72)(73)(74). Building on this approach, we classified clinical cohorts of HCC patients based on the expression levels of six specific genes associated with sphingolipids.…”

Section: Discussionmentioning

confidence: 99%

Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma

Zhang

Zhuge²,

Liu

et al. 2023

Front. Immunol.

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BackgroundHepatocellular carcinoma (HCC) is a complex disease with a poor outlook for patients in advanced stages. Immune cells play an important role in the progression of HCC. The metabolism of sphingolipids functions in both tumor growth and immune infiltration. However, little research has focused on using sphingolipid factors to predict HCC prognosis. This study aimed to identify the key sphingolipids genes (SPGs) in HCC and develop a reliable prognostic model based on these genes.MethodsThe TCGA, GEO, and ICGC datasets were grouped using SPGs obtained from the InnateDB portal. A prognostic gene signature was created by applying LASSO-Cox analysis and evaluating it with Cox regression. The validity of the signature was verified using ICGC and GEO datasets. The tumor microenvironment (TME) was examined using ESTIMATE and CIBERSORT, and potential therapeutic targets were identified through machine learning. Single-cell sequencing was used to examine the distribution of signature genes in cells within the TME. Cell viability and migration were tested to confirm the role of the key SPGs.ResultsWe identified 28 SPGs that have an impact on survival. Using clinicopathological features and 6 genes, we developed a nomogram for HCC. The high- and low-risk groups were found to have distinct immune characteristics and response to drugs. Unlike CD8 T cells, M0 and M2 macrophages were found to be highly infiltrated in the TME of the high-risk subgroup. High levels of SPGs were found to be a good indicator of response to immunotherapy. In cell function experiments, SMPD2 and CSTA were found to enhance survival and migration of Huh7 cells, while silencing these genes increased the sensitivity of Huh7 cells to lapatinib.ConclusionThe study presents a six-gene signature and a nomogram that can aid clinicians in choosing personalized treatments for HCC patients. Furthermore, it uncovers the connection between sphingolipid-related genes and the immune microenvironment, offering a novel approach for immunotherapy. By focusing on crucial sphingolipid genes like SMPD2 and CSTA, the efficacy of anti-tumor therapy can be increased in HCC cells.

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“…In recent years, significant progress has been made in the field of bioinformatics for CRC research ( 31 , 32 ). Studies have employed various bioinformatics tools, including transcriptomics, genomics, proteomics, and metabolomics, to investigate the molecular mechanisms underlying CRC development and progression ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of SLC4A4 and its correlation with the microsatellite instability in colorectal cancer

et al. 2023

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ObjectiveTo explore new biomarkers related to microsatellite instability in order to better predict prognosis and guide medication.MethodsThe “limma” R package was used to identify differentially expressed genes in GSE24514, and then weighted correlation network analysis was used to select key genes. Different cell types in the tumor microenvironment were identified and analyzed by single-cell sequencing, with a Lasso regression model used to screen prognostic variables. Furthermore, the correlation between microsatellite instability and potential prognostic variables was explored, as well as the expression characteristics and clinical characteristics of the prognostic variables in the TCGA, UALCAN, and HPA databases. PCR assay was used to investigate the expression of SLC4A4 in colorectal cancer cell lines. Finally, we further verified the expression of SLC4A4 by immunohistochemistry.ResultsFirst, 844 differentially expressed genes in GSE24514 were identified. Subsequently, weighted co-expression network analysis (WGCNA) of GSE24514 obtained all the genes significantly associated with microsatellite instability (MSI), a total of 1452. Analysis of GSE166555 single cell sequencing data set yielded 1564 differentially expressed genes. The gene sets obtained from the above three analysis processes were intersected, and 174 genes were finally obtained. The Lasso regression model revealed two potential prognostic genes, TIMP1 and SLC4A4, of which, there was a stronger correlation between microsatellite instability and SLC4A4. The mRNA and protein expression of SLC4A4 was significantly decreased in tumors, and patients with low SLC4A4 expression had a poor prognosis. In addition, SLC4A4 was specifically expressed in epithelial cells. In the microenvironment of colorectal cancer, malignant cells have a strong interaction with different stromal cells. PCR showed that SLC4A4 was significantly down-regulated in colorectal cancer cell lines Caco-2, HCT116 and HT29 compared with normal control NCM460 cell lines. Immunohistochemistry also showed low expression of SLC4A4 in colorectal cancer.ConclusionSLC4A4, as a tumor suppressor gene, is significantly downregulated and positively correlated with microsatellite instability, thus it may be combined with microsatellite instability to guide colorectal cancer treatment.

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Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers

Cited by 37 publications

References 40 publications

Telomere maintenance genes-derived prognosis signature characterizes immune landscape and predicts prognosis of head and neck squamous cell carcinoma

Telomere maintenance genes-derived prognosis signature characterizes immune landscape and predicts prognosis of head and neck squamous cell carcinoma

Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma

Prognostic value of SLC4A4 and its correlation with the microsatellite instability in colorectal cancer

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