Deep Learning-Based Pan-Cancer Classification Model Reveals Tissue-of-Origin Specific Gene Expression Signatures

Divate, Mayur; Tyagi, Aayush Kumar; Richard, Derek J.; Prasad, Prathosh A.; Gowda, Harsha; Nagaraj, Shivashankar H.

doi:10.3390/cancers14051185

Cited by 30 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, accumulating evidence has demonstrated the inevitable association between ER stress and the development of multiple cancers, including KIRC (Chen and Cubillos-Ruiz, 2021;Varone et al, 2021). Recently, according to numerous research studies, multiple gene models, which are applied to predict outcomes and therapeutic effect, seem to have high credibility (Divate et al, 2022;Zhai et al, 2022). However, most of the research studies focus on the effect of ER stress in cancer progression and metastasis, and few studies have illustrated the prognostic value of ER stress-related genes in cancers, especially in KIRC.…”

Section: Discussionmentioning

confidence: 99%

Construction of an endoplasmic reticulum stress-related gene model for predicting prognosis and immune features in kidney renal clear cell carcinoma

Shen

Cao

Zhou

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

Background: Kidney renal clear cell carcinoma (KIRC) is one of the most lethal malignant tumors with a propensity for poor prognosis and difficult treatment. Endoplasmic reticulum (ER) stress served as a pivotal role in the progression of the tumor. However, the implications of ER stress on the clinical outcome and immune features of KIRC patients still need elucidation.Methods: We identified differentially expressed ER stress-related genes between KIRC specimens and normal specimens with TCGA dataset. Then, we explored the biological function and genetic mutation of ER stress-related differentially expressed genes (DEGs) by multiple bioinformatics analysis. Subsequently, LASSO analysis and univariate Cox regression analysis were applied to construct a novel prognostic model based on ER stress-related DEGs. Next, we confirmed the predictive performance of this model with the GEO dataset and explored the potential biological functions by functional enrichment analysis. Finally, KIRC patients stratified by the prognostic model were assessed for tumor microenvironment (TME), immune infiltration, and immune checkpoints through single-sample Gene Set Enrichment Analysis (ssGSEA) and ESTIMATE analysis.Results: We constructed a novel prognostic model, including eight ER stress-related DEGs, which could stratify two risk groups in KIRC. The prognostic model and a model-based nomogram could accurately predict the prognosis of KIRC patients. Functional enrichment analysis indicated several biological functions related to the progression of KIRC. The high-risk group showed higher levels of tumor infiltration by immune cells and higher immune scores.Conclusion: In this study, we constructed a novel prognostic model based on eight ER stress-related genes for KIRC patients, which would help predict the prognosis of KIRC and provide a new orientation to further research studies on personalized immunotherapy in KIRC.

show abstract

Section: Discussionmentioning

confidence: 99%

Construction of an endoplasmic reticulum stress-related gene model for predicting prognosis and immune features in kidney renal clear cell carcinoma

Shen

Cao

Zhou

et al. 2022

Front. Mol. Biosci.

View full text Add to dashboard Cite

show abstract

“…Schaefera et al explore why some genetic alterations are only relevant in specific types of cancer, concluding that the tissue microenvironment is a determining factor in this process (44). Two other studies have demonstrated that expression signatures can help classify the tissue for cancers of unknown primary origin, which presents a possible application of using the transcriptome signatures with tissue specificity in oncology (45, 46). Our work, besides adding novel knowledge to this field, corroborates studies such as that from Hu et al, which showed that in cancer, there is a decrease in the expression of some tissue-specific genes, and Pei et al, which showed that it is common for cancers to acquire specific expression profiles from other organs (47, 48).…”

Section: Discussionmentioning

confidence: 99%

Pan-cancer transcriptomic analysis reveals a tissue-specific loss of identity

Santos

Chatsirisupachai

Avelar

et al. 2022

Preprint

View full text Add to dashboard Cite

Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx. We found a loss of cell identity during ageing in around 40% of tissues. In cancer, we observed a pattern of downregulation of genes specific to the tissue of origin of the tumor for most cancer types. Moreover, we observed an activation of genes not usually expressed in a tissue as well as an upregulation of genes specific to other tissues in cancer. These patterns in cancer were associated with patient survival. The age of the patient, however, does not influence these patterns. Thus, we identified a moderate but statistically significant pattern of loss of cell identity in ageing, and a clear pattern in cancer, where during tumorigenesis, cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

show abstract

“…Cancers could be categorized as per the main point of action, such as breast, lung, prostate, liver, kidney, and brain (Divate et al, 2022 ).…”

Section: Human Cancer Categorizationsmentioning

confidence: 99%

A Survey on Human Cancer Categorization Based on Deep Learning

Ahmad

Mohamed

Maher

et al. 2022

Front. Artif. Intell.

View full text Add to dashboard Cite

In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.

show abstract

Deep Learning-Based Pan-Cancer Classification Model Reveals Tissue-of-Origin Specific Gene Expression Signatures

Cited by 30 publications

References 27 publications

Construction of an endoplasmic reticulum stress-related gene model for predicting prognosis and immune features in kidney renal clear cell carcinoma

Construction of an endoplasmic reticulum stress-related gene model for predicting prognosis and immune features in kidney renal clear cell carcinoma

Pan-cancer transcriptomic analysis reveals a tissue-specific loss of identity

A Survey on Human Cancer Categorization Based on Deep Learning

Contact Info

Product

Resources

About