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Biomolecules
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Cancer Omics: Novel Insights and Emerging Diagnostic and Therapeutic Strategies
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Cancer Omics: Novel Insights and Emerging Diagnostic and Therapeutic Strategies

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 13932

Special Issue Editor

Dr. Bairong Shen

E-Mail Website
Guest Editor
West China School of Medicine, West China Hospital of Sichuan University, Chengdu, China
Interests: translational informatics; hormone sensitive cancers; non-coding RNAs; ontology and data sharing

Special Issue Information

Dear Colleagues,

Multi-omics technologies are expediting the paradigm shift from traditional evidence-based medicine to precision medicine. The former is a mode based on the large population-level averaging of genotype-phenotype interactions, while the latter is focused on the deep phenotyping of personalized multi-omics data for precision diagnosis, prognosis, and treatment.

Translational informatics is a multidisciplinary field involving the mining of data collected at multi-omics levels and integrating chemical informatics, bioinformatics, and medical informatics to promote translational research. We are facing many challenges in the translation of data-intensive scientific discovery to clinical and healthcare applications in cancer medicine, such as the standardization of diverse data, preserving the privacy of the shared data, systems-level integration, and explainable artificial intelligence modeling.

This Special Issue will focus on informatics for the integration and modeling of diverse data at the molecular, cellular, tissue/organ, and patient levels to discover the patterns hidden in the data or to establish and apply the structured data for similarity identification in the data-driven diagnosis and treatment of cancers.

Dr. Bairong Shen
Guest Editor

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Keywords

  • translational informatics
  • ontology and knowledge graphs
  • data sharing and privacy-preserving
  • deep phenotyping
  • explainable artificial intelligence

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Published Papers (4 papers)

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Research

21 pages, 5900 KiB  
Article
Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Gastric Cancer
by Min Jiang, Changyin Fang and Yongping Ma
Biomolecules 2023, 13(3), 469; https://doi.org/10.3390/biom13030469 - 3 Mar 2023
Cited by 6 | Viewed by 2481
Abstract
Gastric cancer (GC) is a malignant tumor with a low survival rate, high recurrence rate, and poor prognosis. With respect to this, pyroptosis is a type of programmed cell death that can affect the occurrence and development of tumors. Indeed, long non-coding RNAs [...] Read more.
Gastric cancer (GC) is a malignant tumor with a low survival rate, high recurrence rate, and poor prognosis. With respect to this, pyroptosis is a type of programmed cell death that can affect the occurrence and development of tumors. Indeed, long non-coding RNAs (lncRNAs) were broadly applied for the purposes of early diagnosis, treatment, and prognostic analysis in regard to cancer. Based on the association of these three purposes, we developed a novel prognosis risk model based on pyroptosis-related lncRNAs (PRlncRNAs) for GC. The PRlncRNAs were obtained via univariate and multivariate Cox regression in order to build the predictive signatures. The Kaplan–Meier and gene set enrichment analysis (GSEA) methods were used to evaluate the overall survival (OS) and functional differences between the high- and low-risk groups. Moreover, the correlation of the signatures with immune cell infiltration was determined through single-sample gene set enrichment analysis (ssGSEA). Finally, we analyzed this correlation with the treatment responses in the GC patients; then, we performed quantitative reverse transcription polymerase chain reactions (qRT-PCRs) in order to verify the risk model. The high-risk group received a worse performance in terms of prognosis and OS when compared to the low-risk group. With respect to this, the area under the receiver operating characteristic curve (ROC) was found to be 0.808. Through conducting the GSEA, it was found that the high-risk groups possessed a significant enrichment in terms of tumor–immunity pathways. Furthermore, the ssGSEA revealed that the predictive features possessed strong associations with immune cell infiltration in regard to GC. In addition, we highlighted that anti-immune checkpoint therapy, combined with conventional chemotherapy drugs, may be more suitable for high-risk patients. The expression levels of LINC01315, AP003392.1, AP000695.2, and HAGLR were significantly different between the GC cell lines and the normal cell lines. As such, the six PRlncRNAs could be regarded as important prognostic biomarkers for the purposes of subsequent diagnoses, treatments, prognostic predictions, and the mechanism research of GC. Full article
(This article belongs to the Special Issue Cancer Omics: Novel Insights and Emerging Diagnostic and Therapeutic Strategies)
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19 pages, 9450 KiB  
Article
Identification of an Autophagy-Related Signature for Prognosis and Immunotherapy Response Prediction in Ovarian Cancer
by Jinye Ding, Chunyan Wang, Yaoqi Sun, Jing Guo, Shupeng Liu and Zhongping Cheng
Biomolecules 2023, 13(2), 339; https://doi.org/10.3390/biom13020339 - 9 Feb 2023
Cited by 23 | Viewed by 5463
Abstract
Background: Ovarian cancer (OC) is one of the most malignant tumors in the female reproductive system, with a poor prognosis. Various responses to treatments including chemotherapy and immunotherapy are observed among patients due to their individual characteristics. Applicable prognostic markers could make it [...] Read more.
Background: Ovarian cancer (OC) is one of the most malignant tumors in the female reproductive system, with a poor prognosis. Various responses to treatments including chemotherapy and immunotherapy are observed among patients due to their individual characteristics. Applicable prognostic markers could make it easier to refine risk stratification for OC patients. Autophagy is closely implicated in the occurrence and development of tumors, including OC. Whether autophagy -related genes can be used as prognostic markers for OC patients remains unclear. Methods: The gene transcriptome data of 374 OC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The correlation between the autophagy levels and outcomes of OC patients was identified through the single sample gene set enrichment analysis (ssGSEA). Recognized molecular markers of autophagy in different clinical specimens were detected by immunohistochemistry (IHC) assay. The gene set enrichment analysis (GSEA), ESTIMATE, and CIBERSORT analysis were applied to explore the correlation of autophagy with the tumor immune microenvironment (TIME). Single-cell RNA-sequencing (scRNA-seq) data from seven OC patients were included for characterizing cell-cell interaction patterns of autophagy-high or low tumor cells. Machine learning, Stepwise Cox regression and LASSO-Cox analysis were used to screen autophagy hub genes, which were used to establish an autophagy-related signature for prognosis evaluation. Four tumor immunotherapy cohorts were obtained from the GEO (Gene Expression Omnibus) database and the literature for autophagy risk score validation. Results: The autophagy levels were closely related to the prognosis of the OC patients. Additionally, the autophagy levels were correlated with TIME status including immune score, and immune-cell infiltration. The scRNA-seq analysis found that tumor cells with high or low autophagy levels had different interactions with immune cells, especially macrophages. Eight autophagy-hub genes (ZFYVE1, AMBRA1, LAMP2, TRAF6, PDPK1, ATG2B, DAPK1 and TP53INP2) were screened for an autophagy-related signature. According to this signature, higher risk score was correlated with poor prognosis and better immunotherapy response in the OC patients. Conclusions: The autophagy-related signature is applicable to predict the prognosis and immune checkpoint inhibitors (ICIs) therapy efficiency in OC patients. It is possible to identify OC patients who will respond to ICIs therapy and have a favorable prognosis, although more verification is needed. Full article
(This article belongs to the Special Issue Cancer Omics: Novel Insights and Emerging Diagnostic and Therapeutic Strategies)
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22 pages, 5697 KiB  
Article
The Cancer-Associated Fibroblasts-Related Gene COMP Is a Novel Predictor for Prognosis and Immunotherapy Efficacy and Is Correlated with M2 Macrophage Infiltration in Colon Cancer
by He Ma, Qingqing Qiu, Dan Tan, Qiaofeng Chen, Yaping Liu, Bing Chen and Mingliang Wang
Biomolecules 2023, 13(1), 62; https://doi.org/10.3390/biom13010062 - 28 Dec 2022
Cited by 17 | Viewed by 3220
Abstract
Background: Colon cancer is characterized by a sophisticated tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which make up the majority of the stromal cells in TME, participate in tumor development and immune regulation. Further investigations of CAFs would facilitate an in-depth understanding of its [...] Read more.
Background: Colon cancer is characterized by a sophisticated tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which make up the majority of the stromal cells in TME, participate in tumor development and immune regulation. Further investigations of CAFs would facilitate an in-depth understanding of its role in colon cancer TME. Methods: In this study, we estimated CAF abundance based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases using the Microenvironment Cell Populations-counter (MCP-counter) algorithm. CAF-related genes were identified by differential gene expression analysis combined with weighted gene coexpression network analysis. For further selection, the least absolute shrinkage and selection operator (LASSO)-Cox regression was used, and the prognostic value of the selected gene was confirmed in numerous external cohorts. The function enrichment, immunological characteristics, tumor mutation signature, immunotherapy response, and drug sensitivity of the selected gene were subsequently explored. The bioinformatics analysis results were validated using immunohistochemistry on clinical samples from our institution. Results: According to our findings, cartilage oligomeric matrix protein (COMP) was uncovered as a candidate CAFs-driven biomarker in colon cancer and plays an important role in predicting prognosis in colon cancer. COMP upregulation was associated with enhanced stromal and immune activation, and immune cell infiltration, especially M2 macrophages. Genes that mutated differently between the high- and low-COMP expression subgroups may be correlated with TME change. Following verification, COMP reliably predicted the immunotherapy response and drug response. In addition, our experimental validation demonstrated that COMP overexpression is associated with colon cancer carcinogenesis and is strongly associated with CAFs and M2 macrophage infiltration. Conclusion: Our study uncovered that COMP was a key CAFs-driven gene associated with M2 macrophage infiltration and acted as a convincing predictor for prognosis and immunotherapy response in colon cancer patients. Full article
(This article belongs to the Special Issue Cancer Omics: Novel Insights and Emerging Diagnostic and Therapeutic Strategies)
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15 pages, 4129 KiB  
Article
A Novel and Validated 8-Pyroptosis-Related Genes Based Risk Prediction Model for Diffuse Large B Cell Lymphoma
by Junrui Ma, Wenhan Wang, Jiao Ma and Zizhen Xu
Biomolecules 2022, 12(12), 1835; https://doi.org/10.3390/biom12121835 - 8 Dec 2022
Cited by 3 | Viewed by 1826
Abstract
Background: Diffuse large B-cell lymphoma (DLBCL), the most common type of Non-Hodgkin’s Lymphoma (NHL), has a lethal nature. Thus, the establishment of a novel model to predict the prognosis of DLBCL and guide its therapy is an urgency. Meanwhile, pyroptosis is engaged in [...] Read more.
Background: Diffuse large B-cell lymphoma (DLBCL), the most common type of Non-Hodgkin’s Lymphoma (NHL), has a lethal nature. Thus, the establishment of a novel model to predict the prognosis of DLBCL and guide its therapy is an urgency. Meanwhile, pyroptosis is engaged in the progression of DLBCL with further investigations required to reveal the underlying mechanism. Methods: LASSO regression was conducted to establish a risk model based on those PRGs. External datasets, RT-qPCR and IHC images from The Human Protein Alta (HPA) database were utilized to validate the model. ssGSEA was utilized to estimate the score of immune components in DLBCL. Results: A model based on 8 PRGs was established to generate a risk score. Validation of the model confirmed its robust performance. The risk score was associated with advanced clinical stages and shorter overall survivals. Two novel second-line chemotherapies were found to be potential treatments for high-risk patients. The risk score was also found to be correlated with immune components in DLBCL. Conclusion: This novel model can be utilized in clinical practices to predict the prognosis of DLBCL and guide the treatment of patients at high risk, providing an overview of immune regulatory program via pyroptosis in DLBCL. Full article
(This article belongs to the Special Issue Cancer Omics: Novel Insights and Emerging Diagnostic and Therapeutic Strategies)
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