Novel gene signatures for stage classification of the squamous cell carcinoma of the lung

Juarez-Flores, Angel; Zamudio, Gabriel S.; José, Marco V.

doi:10.1038/s41598-021-83668-1

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…Machine learning is a multidisciplinary technique that combines statistics and computer science and uses a variety of strategies and algorithms to arrive at the best model ( 11 ). Compared with traditional statistical methods that concentrate on the causality of hypothesis testing and the significance of model features, machine learning focuses more on the downscaling of high-dimensional data and the predictive performance and generalization of models ( 12 , 13 ). As a result, machine learning is better suited for analyzing complex and large quantities of data (e.g., gene expression analysis, image feature extraction, drug sensitivity prediction, etc.).…”

Section: Introductionmentioning

confidence: 99%

Identification and verification of the temozolomide resistance feature gene DACH1 in gliomas

Li²,

Yao³

et al. 2023

Front. Oncol.

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IntroductionThe most important chemotherapy treatment for glioma patients is temozolomide. However, the development of drug resistance severely restricts the use of temozolomide. Therefore, elucidating the mechanism of temozolomide resistance, enhancing temozolomide sensitivity, and extending patient survival are urgent tasks for researchers.MethodsTemozolomide resistance hub differential genes were identified using differential analysis and protein interaction analysis from the GEO datasets (GSE100736 and GSE113510). These genes were further studied in glioma patients treated with temozolomide in the TCGA and CGGA databases. Patients from the mRNAseq_325 dataset (CGGA) were considered as the training set to construct a risk model for predicting glioma sensitivity to temozolomide, while patients from the mRNAseq_693 dataset (CGGA) and TCGA-GBM dataset were considered as the validation set to evaluate the performance of models. PCR and western blot were performed to determine the difference in expression of the feature gene DACH1 between glioma cells and temozolomide-resistant glioma cells. The alterations in the sensitivity of tumor cells to temozolomide were also observed after DACH1 was silenced. The patients were then divided into two groups based on the expression of DACH1, and the differences in patient survival rates, molecular pathway activation, and level of immune infiltration were compared.ResultsBased on four signature genes (AHR, DACH1, MGMT, and YAP1), a risk model for predicting glioma sensitivity to temozolomide was constructed, and the results of timeROC in both the training and validation sets showed that the model had good predictive performance. The expression of the signature gene DACH1 was significantly downregulated in temozolomide-resistant cells, according to the results of the PCR and western blot experiments. The sensitivity of tumor cells to temozolomide was significantly reduced after DACH1 was silenced. DACH1 probably regulates temozolomide resistance in glioblastoma through the transcriptional dysregulation in cancer and ECM.DiscussionThis study constructs a risk model that can predict glioma susceptibility to temozolomide and validates the function of the feature gene DACH1, which provides a promising target for the research of temozolomide resistance.

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Section: Introductionmentioning

confidence: 99%

Identification and verification of the temozolomide resistance feature gene DACH1 in gliomas

Li²,

Yao³

et al. 2023

Front. Oncol.

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“…In recent decades, non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), have gained attention. Various non-coding RNAs have been identified as effective prognostic or diagnostic molecular signatures, particularly in the field of oncology ( 15 , 16 ). Over 80% of transcripts in the human genome are not translated into proteins and remain as non-coding sequences ( 17 ).…”

Section: Introductionmentioning

confidence: 99%

An Oxidative Stress-Related Gene Pair (CCNB1/PKD1), Competitive Endogenous RNAs, and Immune-Infiltration Patterns Potentially Regulate Intervertebral Disc Degeneration Development

et al. 2021

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Oxidative stress (OS) irreversibly affects the pathogenesis of intervertebral disc degeneration (IDD). Certain non-coding RNAs act as competitive endogenous RNAs (ceRNAs) that regulate IDD progression. Analyzing the signatures of oxidative stress-related gene (OSRG) pairs and regulatory ceRNA mechanisms and immune-infiltration patterns associated with IDD may enable researchers to distinguish IDD and reveal the underlying mechanisms. In this study, OSRGs were downloaded and identified using the Gene Expression Omnibus database. Functional-enrichment analysis revealed the involvement of oxidative stress-related pathways and processes, and a ceRNA network was generated. Differentially expressed oxidative stress-related genes (De-OSRGs) were used to construct De-OSRG pairs, which were screened, and candidate De-OSRG pairs were identified. Immune cell-related gene pairs were selected via immune-infiltration analysis. A potential long non-coding RNA–microRNA–mRNA axis was determined, and clinical values were assessed. Eighteen De-OSRGs were identified that were primarily related to intricate signal-transduction pathways, apoptosis-related biological processes, and multiple kinase-related molecular functions. A ceRNA network consisting of 653 long non-coding RNA–microRNA links and 42 mRNA–miRNA links was constructed. Three candidate De-OSRG pairs were screened out from 13 De-OSRG pairs. The abundances of resting memory CD4+ T cells, resting dendritic cells, and CD8+ T cells differed between the control and IDD groups. CD8+ T cell infiltration correlated negatively with cyclin B1 (CCNB1) expression and positively with protein kinase D1 (PKD1) expression. CCNB1–PKD1 was the only pair that was differentially expressed in IDD, was correlated with CD8+ T cells, and displayed better predictive accuracy compared to individual genes. The PKD1–miR-20b-5p–AP000797 and CCNB1–miR-212-3p–AC079834 axes may regulate IDD. Our findings indicate that the OSRG pair CCNB1–PKD1, which regulates oxidative stress during IDD development, is a robust signature for identifying IDD. This OSRG pair and increased infiltration of CD8+ T cells, which play important roles in IDD, were functionally associated. Thus, the OSRG pair CCNB1–PKD1 is promising target for treating IDD.

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“…In recent decades, non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), have gained attention. Various non-coding RNAs have been identified as effective prognostic or diagnostic molecular signatures, particularly in the field of oncology (15,16). Over 80% of transcripts in the human genome are not translated into proteins and remain as noncoding sequences (17).…”

Section: Introductionmentioning

confidence: 99%

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Novel gene signatures for stage classification of the squamous cell carcinoma of the lung

Cited by 5 publications

References 49 publications

Identification and verification of the temozolomide resistance feature gene DACH1 in gliomas

Identification and verification of the temozolomide resistance feature gene DACH1 in gliomas

An Oxidative Stress-Related Gene Pair (CCNB1/PKD1), Competitive Endogenous RNAs, and Immune-Infiltration Patterns Potentially Regulate Intervertebral Disc Degeneration Development

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