The prognostic significance of tumor-infiltrating lymphocytes in cervical cancer

He, Mengdi; Wang, Yiying; Zhang, Guodong; Cao, Kankan; Yang, Moran; Liu, Haiou

doi:10.3802/jgo.2021.32.e32

Cited by 11 publications

(8 citation statements)

References 42 publications

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“…In this study, we screened out the T cells CD8 and Neutrophils to construct a Cox proportional hazards regression model, which evaluation of its AUC suggests has clear clinical value. Some studies also explored the relationship between immune cells and renal cell carcinoma, and similar to our research, found high CD8 T cell in ltration is signi cantly associated with a good prognosis in CESC patients and Neutrophils were negatively associated with long term survival [31,32]. CD8 T cells are a major component of tumor-in ltrating lymphocytes.…”

Section: Discussionsupporting

confidence: 85%

Prognostic Nomogram Based on an Immune-Related Biomarker Model Based on the circRNA-Associated Regulatory Network for Cervical cancer

Guo

Chen

Wang

et al. 2022

Preprint

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Cervical cancer (CC) is a common cancer among women with poor prognosis. Circular RNA (circRNA), as a ceRNA, plays an important role in regulating microRNAs (miRNAs) in many cancers. The immune microenvironment of CC and the regulatory mechanisms at the transcriptomic level have not been fully explored. This study integrated the expression data of circRNA, Long non-coding RNA (lncRNA), miRNA and mRNA from TCGA database, GTEx database and GEO database, and mapped the ceRNA network through variation analysis and correlation analysis. Based on ceRNA network and tumor immune cell infiltration, we constructed two nomograms that can predict CC patients. Through co expression analysis and gene function enrichment, we explored the relationship between prognostic genes and immune cells in the model and the overall function of network node genes. In addition, we screened the drug RO-3306 with sensitivity to high-risk patients and predicted potential drugs with anti CC function by CMap. In conclusion, the research provides relevant information about the diagnosis, treatment and prognosis of CC patients, and several related drugs for reference. Prospective studies are necessary to validate the prognostic ability of this model.

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Section: Discussionsupporting

confidence: 85%

Prognostic Nomogram Based on an Immune-Related Biomarker Model Based on the circRNA-Associated Regulatory Network for Cervical cancer

Guo

Chen

Wang

et al. 2022

Preprint

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“…The ROC results suggested definite clinical value. Similar to our research, some studies discover that high CD8+ T cell infiltration level is significantly related to the favorable prognosis of patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), and neutrophil infiltration level is negatively correlated with long-term survival [ 42 , 43 ]. CD8+ T cells are the major component in the TIICs.…”

Section: Discussionsupporting

confidence: 85%

CircRNA-Based Cervical Cancer Prognosis Model, Immunological Validation and Drug Prediction

et al. 2022

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Background: Cervical cancer (CC) is a common cancer in female, which is associated with problems like poor prognosis. Circular RNA (circRNA) is a kind of competing endogenous RNA (ceRNA) that has an important role in regulating microRNA (miRNA) in many cancers. The regulatory mechanisms of CC immune microenvironment and the transcriptome level remain to be fully explored. Methods: In this study, we constructed the ceRNA network through the interaction data and expression matrix of circRNA, miRNA and mRNA. Meanwhile, based on the gene expression matrix, CIBERSORT algorithm was used to reveal contents of tumor-infiltrating immune cells (TIICs). Then, we screened prognostic markers based on ceRNA network and immune infiltration and constructed two nomograms. In order to find immunological differences between the high- and low-risk CC samples, we examined multiple immune checkpoints and predicted the effect of PD-L1 ICI immunotherapy. In addition, the sensitive therapeutics for high-risk patients were screened, and the potential agents with anti-CC activity were predicted by Connective Map (CMap). Results: We mapped a ceRNA network including 5 circRNAs, 17 miRNAs and 129 mRNAs. From the mRNA nodes of the network six genes and two kind of cells were identified as prognostic makers for CC. Among them, there was a significant positive correlation between CD8+ T cells and SNX10 gene. The results of TIDE and single sample GSEA (ssGSEA) showed that T cells CD8 do play a key role in inhibiting tumor progression. Further, our study screened 24 drugs that were more sensitive to high-risk CC patients and several potential therapeutic agents for reference. Conclusions: Our study identified several circRNA-miRNA-mRNA regulatory axes and six prognostic genes based on the ceRNA network. In addition, through TIIC, survival analysis and a series of immunological analyses, T cells were proved to be good prognostic markers, besides play an important role in the immune process. Finally, we screened 24 potentially more effective drugs and multiple potential drug compounds for high- and low-risk patients.

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“…[17][18][19] It has been discovered that neutrophils, lymphocytes, monocytes, and platelets could predict the prognosis and treatment response of malignancies. [20][21][22][23][24] In ammatory markers, such as NLR, PLR, and SII, are also potential prognostic markers for a variety of tumors. [25][26][27][28] Several studies have identi ed an increase in systemic in ammatory markers such as NLR and PLR associated with poor prognosis in patients with mCRC treated with chemotherapy in combination with bevacizumab.…”

Section: Discussionmentioning

confidence: 99%

A nomogram combining NLR, PLR and SII to predict progression-free survival of cetuximab-based first-line therapy in patients with metastatic colorectal cancer

Shen

Dang

et al. 2023

Preprint

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Objective: To establish an effective prognostic nomogram combining neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) to predict progression-free survival (PFS) of metastatic colorectal cancer (mCRC) patientstreated with cetuximab-based first-line therapy. Materials and methods: 143 patients with mCRC were admitted to our hospital and received cetuximab-based first-line therapy. The patients were separated into training and validation cohorts. Patients' baseline blood parameters and clinical characteristics were collected. In the training cohort, Kaplan-Meier analysis, univariate analysis, and multivariate analysis were used to identify factors associated with PFS and to screen for independent predictors. A prognostic nomogram was constructed, and the model's predictive efficacy, stability and net benefit were evaluated. Validation was performed in the validation cohort. Results: Kaplan-Meier analysis showed that patients in the NLR≥3.9 group, PLR≥152.2 group and SII≥464.3 group had worse PFS than those in the NLR<3.9 group, PLR<152.2 group and SII<464.3 group, respectively (P<0.001). Univariate analysis revealed that the resected primary tumor, liver metastases, NLR, PLR and SII were significantly correlated with PFS. Multivariate analysis revealed that the resected primary tumor (HR: 0.551, 95%CI: 0.329-0.924, P=0.024), liver metastases (HR: 2.033, 95%CI: 1.212-3.407, P=0.007), NLR (HR: 2.596, 95%CI: 1.378-4.888, P=0.003), PLR (HR: 2.002, 95%CI: 1.235-3.246, P=0.005) and SII (HR: 2.202, 95%CI: 1.292-3.751, P=0.004) were independent prognostic factors affecting PFS. A prognostic nomogram model was developed and revealed the greatest predictive efficacy (AUC=0.870). The nomogram revealed excellent stability and predictive value in both training (C-index=0.827) and validation cohort (C-index=0.870). Decision curve analysis (DCA) proved that the prognostic nomogram could be clinically valuable. Conclusions: The nomogram combining the resected primary tumor, liver metastases, NLR, PLR, and SII can be used to predict the PFS of mCRC patients treated with cetuximab-based first-line therapy.

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The prognostic significance of tumor-infiltrating lymphocytes in cervical cancer

Cited by 11 publications

References 42 publications

Prognostic Nomogram Based on an Immune-Related Biomarker Model Based on the circRNA-Associated Regulatory Network for Cervical cancer

Prognostic Nomogram Based on an Immune-Related Biomarker Model Based on the circRNA-Associated Regulatory Network for Cervical cancer

CircRNA-Based Cervical Cancer Prognosis Model, Immunological Validation and Drug Prediction

A nomogram combining NLR, PLR and SII to predict progression-free survival of cetuximab-based first-line therapy in patients with metastatic colorectal cancer

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