Tumor DNA Methylation Profiles Enable Diagnosis, Prognosis Prediction, and Screening for Cervical Cancer

Tu, Jiannan; Chen, Shengchi; Wu, Shizhen; Wu, Ting; Fan, Renliang; Kuang, Zhixing

doi:10.2147/ijgm.s352373

Cited by 13 publications

(7 citation statements)

References 48 publications

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“…It is said that India has the highest unemployment rate. You fill the house with cigarette smoke, which you call anxiety [27][28]. The children who sit on your lap and inhale nicotine are more likely to get lung cancer.…”

Section: Introductionmentioning

confidence: 99%

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Logeshwaran¹,

Adhikari

Joshi³

et al. 2022

ijhs

156

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In general, the various medical systems currently available provide insights into changes in the tumor genome of patients with tumor sequencing. Most of the tumor DNA sequencing can also be referred to as genetic specification or genetic testing. The sequence results help clinical decision-making to develop a personalized cancer treatment plan based on the molecular characteristics of the tumor rather than a one-size-fits-all treatment approach. The tumor sequencing also plays a major role in cancer research. In this paper, an improved method based on machine learning was proposed to analyze the sequencing and tumor sequencing patterns of the human gene. This proposed method analyzes the circulatory problems of patients with different tumor types for analysis in the public domain. It also constantly monitors large data sets of cancer or tumor genetic sequences to calculate tumor size and location. This allows the doctor to get an accurate report on the type of tumor and the problems it can cause to the patient. The Analysis of these datasets of cancer tumor gene sequences reveals that the genetic makeup of each patient is different and that no two cancers are the same.

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Section: Introductionmentioning

confidence: 99%

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Logeshwaran¹,

Adhikari

Joshi³

et al. 2022

ijhs

156

View full text Add to dashboard Cite

show abstract

“…Cervical cancer is easily preventable with early screening and diagnosis. It is acknowledged that the bulk of deep learning-based artificial intelligence has been used in cytology, colposcopy, and DNA methylation research for cervical intraepithelial lesions [28][29][30]. Jian et al [31] showed that machine learning can identify methylation signals associated with the development of cervical cancer at qualitative and quantitative levels.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions

Ding

et al. 2023

Diagnostics

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Cervical squamous intraepithelial lesions (SILs) are precursor lesions of cervical cancer, and their accurate diagnosis enables patients to be treated before malignancy manifests. However, the identification of SILs is usually laborious and has low diagnostic consistency due to the high similarity of pathological SIL images. Although artificial intelligence (AI), especially deep learning algorithms, has drawn a lot of attention for its good performance in cervical cytology tasks, the use of AI for cervical histology is still in its early stages. The feature extraction, representation capabilities, and use of p16 immunohistochemistry (IHC) among existing models are inadequate. Therefore, in this study, we first designed a squamous epithelium segmentation algorithm and assigned the corresponding labels. Second, p16-positive area of IHC slides were extracted with Whole Image Net (WI-Net), followed by mapping the p16-positive area back to the H&E slides and generating a p16-positive mask for training. Finally, the p16-positive areas were inputted into Swin-B and ResNet-50 to classify the SILs. The dataset comprised 6171 patches from 111 patients; patches from 80% of the 90 patients were used for the training set. The accuracy of the Swin-B method for high-grade squamous intraepithelial lesion (HSIL) that we propose was 0.914 [0.889–0.928]. The ResNet-50 model for HSIL achieved an area under the receiver operating characteristic curve (AUC) of 0.935 [0.921–0.946] at the patch level, and the accuracy, sensitivity, and specificity were 0.845, 0.922, and 0.829, respectively. Therefore, our model can accurately identify HSIL, assisting the pathologist in solving actual diagnostic issues and even directing the follow-up treatment of patients.

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“…26 Furthermore, the amount of DNA methylation in NPCs and its link to tumor growth were studied at the epigenetic level. Cervical cancer, 27 colorectal cancer, 28 gastric cancer, 29,30 and pancreatic cancer, 31 among others, have been identified to contain particular genetic changes, and this signature is employed as a predictive marker for cancer. According to our findings, the difference in NPCs methylation levels in most malignancies can predict the prognosis of cancer patients.…”

Section: Discussionmentioning

confidence: 99%

Focusing on the Abnormal Events of NPC1, NPC2, and NPC1L1 in Pan-Cancer and Further Constructing LUAD and KICH Prediction Models

Chen,

Zhang,

Sun

et al. 2023

J. Proteome Res.

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Cancer’s high incidence and death rate jeopardize human health and life, and it has become a global public health issue. Some members of NPCs have been studied in a few cancers, but comprehensive and prognostic analysis is lacking in most cancers. In this study, we used the Cancer Genome Atlas (TCGA) data genomics and transcriptome technology to examine the differential expression and prognosis of NPCs in 33 cancer samples, as well as to investigate NPCs mutations and their effect on patient prognosis and to evaluate the methylation level of NPCs in cancer. The linked mechanisms and medication resistance were subsequently investigated in order to investigate prospective tumor therapy approaches. The relationships between NPCs and immune infiltration, immune cells, immunological regulatory substances, and immune pathways were also investigated. Finally, the LUAD and KICH prognostic prediction models were built using univariate and multivariate COX regression analysis. Additionally, the mRNA and protein levels of NPCs were also identified.

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Tumor DNA Methylation Profiles Enable Diagnosis, Prognosis Prediction, and Screening for Cervical Cancer

Cited by 13 publications

References 48 publications

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions

Focusing on the Abnormal Events of NPC1, NPC2, and NPC1L1 in Pan-Cancer and Further Constructing LUAD and KICH Prediction Models

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