DNA Damage Repair-Related Genes Signature for Immune Infiltration and Outcome in Cervical Cancer

Wang, Xinghao; Xu, Chen; Sun, Hongzan

doi:10.3389/fgene.2022.733164

Cited by 7 publications

(5 citation statements)

References 58 publications

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“…While fully considering the internal differences of their subclassifications, clustering parameters are designed to evaluate the correlation or eccentricity of different subgroups (spatial subregions). For different parameters, they represent different perspectives to evaluate the heterogeneity components within tumors, which is undoubtedly consistent with previous studies [ 36 – 39 ]. In addition to the absolute characterization of tumors, we pay attention to the direct internal relations of different subregions, and their ability to respond to internal relations is stronger than direct indicators.…”

Section: Discussionsupporting

confidence: 87%

A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas

Wang

Sun

et al. 2022

BioMed Research International

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Purpose. We aim to develop and validate a machine learning model by enhanced MRI to determine the pathological grading of meningiomas with unsupervised clustering image analysis method, which are multihabitat to reflect the inherent heterogeneity of tumors. Materials and Methods. A total of 120 patients with meningiomas confirmed by postoperative pathology were included in the study, including 60 patients with low-grade meningiomas (WHO grade I) and 60 patients with high-grade meningiomas (WHO grade II and WHO grade III). All patients underwent complete head enhanced magnetic resonance scans before surgery or any anti-tumor treatment. Enrolled patients in the group received surgical resection and obtained postoperative pathological data. The patients in the training group (84 people) and the test group (36 people) were randomly divided into two groups according to the ratio of 7 to 3. Multi-habitat features were extracted from MRI images based on enhanced T1. Machine learning method was used to model, which was used to distinguish high-grade meningioma from low-grade meningioma. At the same time, the obtained machine learning model was calibrated and evaluated. Results. In patients with low-grade meningioma and high-grade meningioma, we found significant differences in Silhouette coefficient (P<0.05). In the machine learning model, the area under the curve was 0.838 in the training group (sensitivity, 67.65%; specificity, 88.82%) and 0.73 in the test group (sensitivity, 69.05%; specificity, 71.43%). After the analysis of calibration curve and decision curve analysis, the model had shown the potential of great application value. Conclusions. Multi-habitat analysis based on enhanced MRI (T1) could accurately predict the pathological grading of meningiomas. This unsupervised image-based method could reflect the direct heterogeneity between high-grade meningiomas and low-grade meningiomas, which is of great significance for patients’ treatment and prevention of recurrence.

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Section: Discussionsupporting

confidence: 87%

A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas

Wang

Sun

et al. 2022

BioMed Research International

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“…Zhu et al identified two subtypes of HPV + CC based on the most varied 50 genes across CC samples 8 . Furthermore, an increasing number of studies have identified molecular units (including DNA, RNA, and proteins) as biomarkers for the diagnosis and treatment of CC [9][10][11] . But these studies were based on bulk sequencing data, thus overlooking the extensive cellular heterogeneity of CC.…”

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confidence: 99%

Single-cell transcriptomics reveals cellular heterogeneity and molecular stratification of cervical cancer

Guo

et al. 2022

Commun Biol

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Cervical cancer (CC) is the most common gynecological malignancy, whose cellular heterogeneity has not been fully understood. Here, we performed single-cell RNA sequencing (scRNA-seq) to survey the transcriptomes of 57,669 cells derived from three CC tumors with paired normal adjacent non-tumor (NAT) samples. Single-cell transcriptomics analysis revealed extensive heterogeneity in malignant cells of human CCs, wherein epithelial subpopulation exhibited different genomic and transcriptomic signatures. We also identified cancer-associated fibroblasts (CAFs) that may promote tumor progression of CC, and further distinguished inflammatory CAF (iCAF) and myofibroblastic CAF (myCAF). CD8+ T cell diversity revealed both proliferative (MKI67+) and non-cycling exhausted (PDCD1+) subpopulations at the end of the trajectory path. We used the epithelial signature genes derived from scRNA-seq to deconvolute bulk RNA-seq data of CC, identifying four different CC subtypes, namely hypoxia (S-H subtype), proliferation (S-P subtype), differentiation (S-D subtype), and immunoactive (S-I subtype) subtype. The S-H subtype showed the worst prognosis, while CC patients of the S-I subtype had the longest overall survival time. Our results lay the foundation for precision prognostic and therapeutic stratification of CC.

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“…The observed enrichment in immune-related gene sets most likely implies a primary role of interferon-dependent and other innate immunity mechanisms, which could be elicited by genomic instability and, in turn, could foster the adaptive immunity arm with tumor progression. Similarly, a marked up-regulation of innate immunity pathways (such as DNA sensormediated or interferon-induced), along with DDR "heating-up" the immune TME, has been evidenced by other researchers who explored transcriptomic profiles of more advanced CeCa stages using publicly available databases [7,12,13].…”

Section: иммунофенотип рака шейки матки ранних стадийmentioning

confidence: 90%

Characteristics of immune-active and immune-silent phenotypes of early-stage cervical carcinoma as revealed by transcriptome sequencing

2023

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Molecular classification, immuneheterogeneity, and the existence of distinct immunophenotypes of virus-associated cervical cancer (CeCa) remain as-yet weakly explored issues, and this is particularly true of its earliest clinical stages and pre-invasive forms: cervical intraepithelial neoplastic (CIN) lesions. The goal of the study was to identify transcriptomic landscapes of invasive CeCa at its initial progression that differ substantially in their immune-related characteristics, patterns of signaling pathways and composition of the microenvironment. Transcriptome profiling was carried out using RNA-sequencing on Illumina platform. A panel of surgical-derived tissue samples comprised human papillomavirus-positive CIN grade 1-3, cancer of FIGO IA1-IIB stages, and morphologically normal epithelium. Transcriptomic profiles were analyzed with the use of bioinformatics tools, such as gene set enrichment (GAGE) for signaling pathways, xCell enrichment for cell composition identification, and PREDA positional analysis of genomic data. Hierarchical clustering revealed heterogeneity of transcriptomic profiles within the early-stage CeCa, namely, the existence of two clusters of tumor samples and three functional patterns of genes showing coordinately altered expression. Pathway enrichment analysis on genes differently expressed between the two clusters/groups of CeCa samples (‘A' and ‘B') and CIN (group ‘C') suggested that invasive tumor progression in groups ‘A' and ‘B' might rely on immunologically dissimilar mechanisms. xCell analysis confirmed heterogeneity of changes in the abundancies of cell populations when comparing CeCa sample groups and CIN, along with differences in immune and stromal scores. PREDA demonstrated that these transcriptomic differences could be linked to different chromosomal regions and co-localized with particular gene families and potentially the reported virus integration hotspots. Overall, the existence and detectability of different transcriptomic immune-based phenotypes of invasive CeCa at its initial stages of progression is shown, which may provide new options to broaden the knowledge and applicability of target and immune anti-cancer therapy.

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DNA Damage Repair-Related Genes Signature for Immune Infiltration and Outcome in Cervical Cancer

Cited by 7 publications

References 58 publications

A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas

A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas

Single-cell transcriptomics reveals cellular heterogeneity and molecular stratification of cervical cancer

Characteristics of immune-active and immune-silent phenotypes of early-stage cervical carcinoma as revealed by transcriptome sequencing

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