Single-cell analysis revealed that IL4I1 promoted ovarian cancer progression

Zhao, Hongyu; Teng, Yuee; Hao, Wende; Li, Jie; Li, Zhefeng; Chen, Qi; Yin, Chenghong; Yue, Wentao

doi:10.1186/s12967-021-03123-7

Cited by 28 publications

(30 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prolif-TAMs were identified in a pan-cancer study [12] and in studies of several cancer types, including gastric cancer [42], CRC [12], fibrosarcoma [37], prostate cancer [66], NSCLC [27,61], OVC [67], and CNS tumors [34,52]. This population is characterized by the expression of proliferation marker Ki-67 (MKI67), and cell cycle genes, such as cyclin-dependent kinase 1 (CDK1) and cell division cycle 45 (CDC45).…”

Section: Trends In Immunologymentioning

confidence: 99%

“…Notably, TAM clusters feature various metabolic patterns, as inferred from scRNA-seq data through pathway analysis. For example, studies on human NSCLC, PDAC, and OVC indicated that Angio-TAMs highly express hypoxia-related metabolic genes, such as HIFA and SLC2A1 [28,49,67]. In addition, IFN-TAMs were reported to express genes of tryptophan catabolism in a human pan-cancer study [12].…”

Section: Metabolic Diversity Of Tamsmentioning

confidence: 99%

See 1 more Smart Citation

Macrophage diversity in cancer revisited in the era of single-cell omics

Black

Qian

2022

Trends in Immunology

248

225

View full text Add to dashboard Cite

Section: Trends In Immunologymentioning

confidence: 99%

Section: Metabolic Diversity Of Tamsmentioning

confidence: 99%

Macrophage diversity in cancer revisited in the era of single-cell omics

Black

Qian

2022

Trends in Immunology

248

225

View full text Add to dashboard Cite

“…So far, there have been no studies reporting an association between HTLV-1 and OC. However, HTLV-1 infection can promote T cell proliferation by tampering with several core signalling pathways of T cell function [57], and the relationship between T cell function and OC has been demonstrated in several studies [49,50,52]. From this, we speculate that HTLV- We developed a prognostic pro le of four lncRNAs associated with iron toxicity, and found it could effectively identify patients with high-risk OC.…”

Section: Discussionmentioning

confidence: 85%

“…found that HSP10 produced by ovarian tumour cells suppressed the expression of CD3-zeta, , a key component of T cell activation, in T cells [50], and OC patients with lower T cell CD3-zeta expression have been found to exhibit shorter survival times [51]. In addition, IL4I1 has been found to impair T-cell activation, block T cell proliferation and differentiation, and thus promote OC proliferation, migration, and invasion [52]. From this, we hypothesized that T cell activation may prolong OC patient survival, while T cell suppression may be closely associated with low survival in OC patients.…”

Section: Discussionmentioning

confidence: 99%

A Novel Prognostic Risk Score Based on Ferroptosis-related LncRNAs Predicting Ovarian Cancer Patient Survival

Keyu

2021

Preprint

View full text Add to dashboard Cite

Background: Long non-coding RNAs (lncRNAs) are thought to be associated with several processes during cancer development and have been shown to be involved in the regulation of ferroptosis. Ovarian cancer is highly malignant tumour with a poor prognosis. The identification biomarkers with prognostic value in ovarian cancer may improve patient outcomes and can help to elucidate potential future therapeutic targets.Results: We report differential expression of 187 ferroptosis-related lncRNAs in normal and ovarian cancer tissue. Using univariate and multivariable Cox regression analysis, we identified four lncRNAs that were strongly associated with prognosis. We constructed a prognostic risk score based on these four lncRNAs which was effectively able to distinguish between low- and high-risk OC patients based on survival time. Univariate and multivariable Cox regression analyses and time-related receiver operating characteristic curve analyses revealed that this risk score represented an independent prognostic factor in patients with ovarian cancer. For clinical implementation, we developed a nomogram based on the prognostic feature and patient age. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the four ferroptosis-related lncRNAs were related to tumour immunity.Conclusions: we identify four novel ferroptosis-related lncRNAs as predictors of ovarian cancer prognosis and potential future therapeutic targets for ovarian cancer.

show abstract

“…As technology advances, single-cell sequencing can be used to identify the expression patterns in individual cell clusters. A recent study attempted to apply scRNA-seq-based signatures in prognostic prediction in ovarian cancer ( 45 ). The scRNA-seq-based signatures revealed the roles of macrophages in ovarian cancer progression and showed robust performance in predicting patient prognosis.…”

Section: Discussionmentioning

confidence: 99%

Development of a Chemoresistant Risk Scoring Model for Prechemotherapy Osteosarcoma Using Single-Cell Sequencing

Zeng

Zhang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

BackgroundChemoresistance is one of the leading causes that severely limits the success of osteosarcoma treatment. Evaluating chemoresistance before chemotherapy poses a new challenge for researchers. We established an effective chemoresistance risk scoring model for prechemotherapy osteosarcoma using single-cell sequencing.MethodsWe comprehensively analyzed osteosarcoma data from the bulk mRNA sequencing dataset TARGET-OS and the single-cell RNA sequencing (scRNA-seq) dataset GSE162454. Chemoresistant tumor clusters were identified using enrichment analysis and AUCell scoring. Its differentiated trajectory was achieved with inferCNV and pseudotime analysis. Ligand–receptor interactions were annotated with iTALK. Furthermore, we established a chemoresistance risk scoring model using LASSO regression based on scRNA-seq-based markers of chemoresistant tumor clusters. The TARGET-OS dataset was used as the training group, and the bulk mRNA array dataset GSE33382 was used as the validation group. Finally, the performance was verified for its discriminatory ability and calibration.ResultsUsing bulk RNA data, we found that osteogenic expression was upregulated in chemoresistant osteosarcoma as compared to chemosensitive osteosarcoma. Then, we transferred the bulk RNA findings to scRNA-seq and noticed osteosarcoma tumor clusters C14 and C25 showing osteogenic cancer stem cell expression patterns, which fit chemoresistant characteristics. C14 and C25 possessed bridge roles in interactions with other clusters. On the one hand, they received various growth factor stimulators and could potentially transform into a proliferative state. On the other hand, they promote local tumor angiogenesis, bone remodeling and immunosuppression. Next, we identified a ten-gene signature from the C14 and C25 markers and constructed a chemoresistant risk scoring model using LASSO regression model. Finally, we found that chemoresistant osteosarcoma had higher chemoresistance risk score and that the model showed good discriminatory ability and calibration in both the training and validation groups (AUCtrain = 0.82; AUCvalid = 0.84). Compared with that of the classic bulk RNA-based model, it showed more robust performance in validation environment (AUCvalid-scRNA = 0.84; AUCvalid-bulk DEGs = 0.54).ConclusionsOur work provides insights into understanding chemoresistant osteosarcoma tumor cells and using single-cell sequencing to establish a chemoresistance risk scoring model. The model showed good discriminatory ability and calibration and provided us with a feasible way to evaluate chemoresistance in prechemotherapy osteosarcoma.

show abstract

Single-cell analysis revealed that IL4I1 promoted ovarian cancer progression

Cited by 28 publications

References 53 publications

Macrophage diversity in cancer revisited in the era of single-cell omics

Macrophage diversity in cancer revisited in the era of single-cell omics

A Novel Prognostic Risk Score Based on Ferroptosis-related LncRNAs Predicting Ovarian Cancer Patient Survival

Development of a Chemoresistant Risk Scoring Model for Prechemotherapy Osteosarcoma Using Single-Cell Sequencing

Contact Info

Product

Resources

About