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Characteristics of Infiltrating Immune Cells and a Predictive Immune Model for Cervical Cancer.

ABSTRACT: The role of infiltrating immune cells within the tumor microenvironment has received considerable attention, but their function in cervical cancer remains to be elucidated; thus, comprehensive evaluation of their predictive value is needed. Using cervical cancer samples from 406 patients, immune cell infiltration was evaluated via immunohistochemistry. CD3+, CD4+, CD8+, CD20+, CD57+, CD68+, and CD163+ cell infiltration was compared in samples from adjacent tissues and the tumor center. The associations between immune cell distributions in the tumor center, clinicopathological features, and prognosis were correlated among immune cell types. Using three immune features, an immune model was constructed based on a Cox regression analysis with the least absolute shrinkage and selection operator (lasso) penalty to derive immune risk scores. Immune cells that infiltrated the tumor center correlated with clinicopathological characteristics and prognosis. The immune risk scores were an independent prognostic indicator and were found to predict cervical cancer prognosis as well as the effects of chemoradiotherapy. We classified patients into either high- or low-risk subgroups (namely CD4+^highCD163+^highCD57+^low and CD4+^lowCD163+^lowCD57+^high, respectively) based on their immune scores. Significant differences were found in the 3-year overall survival of patients with high- and low-risk scores (83.0% vs. 96.6%; P < 0.001). High immune risk scores resulted in decreased overall survival for patients in stages IB1+IIA1, IB2+IIA2, and IIB-IV (P = 0.001, P = 0.008, and P = 0.044, respectively). Overall survival was significantly worse following chemoradiotherapy in high-scoring patients in stages IB1+IIA1 and IB2+IIA2 (P = 0.001 and P=0.008, respectively). Moreover, overall survival was significantly worse after radiotherapy or chemotherapy in high-scoring patients in stage IB1+IIA1 (P = 0.03). Our work reveals that the distribution of infiltrating immune cells affects their function in cervical cancer. Our tumor center-centric immune model effectively predicted survival, suggesting its potential use in identifying suitable candidates for chemoradiotherapy.

SUBMITTER: Zou R

PROVIDER: S-EPMC8120169 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objective: Immune cells infiltrating has been proved to be associated with prognosis in gastric cancer (GC) by studies. This study aims to explore the prognosis value of infiltrating immune cells in gastric cancer. Methods: In our study, the CIBERSORT algorithm was used to calculate the fraction of 22 tumor-infiltrating immune cells (TIIC) in 100 normal and 300 tumor samples from the GEO cohort and 30 normal and 344 tumor samples from the TCGA cohort. Univariate and multivariate Cox regression were used to construct an immune risk score model. Multivariate cox regression was also used to validate whether our risk score model could predict prognosis in GC independently. Furthermore, the model was validated in different patient subgroups to test its independence. P<0.05 was considered statistically significant. Results: The results showed that the fraction of 3 immune cells increased in tumor tissues compared with normal tissues in both the GEO and TCGA cohort. Univariate cox regression analysis showed four cells significantly correlated with survival rate in GC (P<0.05). The immune risk score model was constructed based on the four cells through multivariate cox regression and further validated. The KM survival curve suggested that patients with high risk had poor prognosis than patients with low risk (P<0.05). ROC curve indicated the model was reliable (AUC= 0.67 in the GEO cohort, AUC = 0.65 in the TCGA cohort). Furthermore, multivariate Cox regression showed the model was an independent factor for overall survival predicting in GC (hazard ratio (HR) = 2.35, 95% confidence interval (CI) = 1.63~3.40 in the GEO cohort, HR = 2.87, 95% CI = 1.94~4.25 in the TCGA cohort). Finally, we validated the model in patient subgroups by the KM survival curve. Conclusion: In summary, tumor-infiltrating immune cells play an essential role in GC progression and affect the outcome of GC patients. The immune risk score can predict overall survival for GC independently, and high immune risk score is associated with poor prognosis.

Project description:AimTo investigate the correlation between clinicopathological features and risk stratification in cervical cancer patients, and evaluate the feasibility of tumor-infiltrating immune cells as prognostic biomarkers in clinical practice.MethodsCD3+ tumor infiltrating T cells (TILs), CD45RO+ TILs, CD4+ TILs, CD8+ TILs, FOXP3+ TILs (regulatory T cells, Tregs), CD68+ tumor associated macrophages (TAMs), CD163+ TAMs, and PD-L1+ tumor cells were immunostained in formalin-fixed paraffin-embedded (PPFE) tissues from 96 cervical cancer patients. Immunostaining density and other clinicopathological features such as age, FIGO stage, histopathologic type, Ki67 index, HPV status, lymhovasular invasion status (LVI), lymph node metastasis, tumor size, stromal invasion status, surgical margin status, and parametrial invasion, were evaluated for their roles in risk stratification of cervical cancer patients.ResultsThe results showed that significant differences of lymph node metastasis (p = 0.003), surgical margin status (p = 0.020), and stromal invasion status (p = 0.004) existed between lVI(-) and LVI(+) patients. CD3+ TILs in the central tumor area (p = 0.010), CD4+ TILs in the central tumor area (p = 0.045), CD8 + TILs in the central tumor area (p = 0.033), and CD8+ TILs in the invasive margin area (p = 0.004) showed significant differences between lVI(-) and LVI(+) patients. When patients were grouped by status of lymph node metastasis, significant differences of FIGO stage (p = 0.005), LVI status (p = 0.003), CD3+ TILs in the central tumor area (p = 0.045), CD45RO+ TILs in the central tumor area (p = 0.033), and CD45RO+ TILs in the invasive margin area (p = 0.028) were also observed. After the patients were stratified into low-, intermediate-, and high risk groups, significant differences of FIGO stage (p = 0.018), status of lymph node metastasis (p = 0.000), LVI status (p = 0.000), parametrial invasion status (p=0.012), stromal invasion status (p = 0.000), tumor growth pattern (p = 0.015) and tumor size (p = 0.000) were identified among 3 groups of patients, while only CD45RO+ TILs in the invasive margin area (p = 0.018) and FOXP3+ TILs in the central tumor area (p = 0.009) were statistically different among three groups of patients. Spearman's correlation analysis demonstrated that FIGO stage, LVI status, status of lymph node metastasis, parametrial invasion, stromal invasion status, and tumor size positively correlated with risk stratification (P = 0.005, 0.020, 0.000, 0.022, 0.000, and 0.000 respectively), while CD45RO+ TILs in the invasive margin area and FOXP3+ TILs in the central tumor area showed statistically negative correlation with risk stratification (P = 0.031, 0.009 respectively).ConclusionOur study suggested that CD45RO+ TILs in the invasive margin area and FOXP3+ TILs in the central tumor area might be useful biomarkers for risk stratification in cervical cancer patients. Large cohort studies of cervical cancer patients are required to validate our hypothesis.

Project description:BackgroundThis study was to explore the infiltration pattern of immune cells in the prostate cancer (PCa) microenvironment and evaluate the possibility of specific infiltrating immune cells as potential prognostic biomarkers in PCa.MethodsInfiltrating percentage of 22 immune cells were extracted from 27 normalized datasets by CIBERSORT algorithm. Samples with CIBERSORT p-value < 0.05 were subsequently merged and divided into normal or tumor groups. The differences of 22 immune cells between normal and tumor tissues were analyzed along with potential infiltrating correlations among 22 immune cells and Gleason grades. SNV data from TCGA was used to calculate the TMB score. A univariate and multivariate regression were used to evaluate the prognostic effects of immune cells in PCa.ResultsTen immune cells with significant differences were identified, including seven increased and three decreased infiltrating immune cells from 190 normal prostate tissues and 537 PCa tissues. Among them, the percentage of infiltration of resting NK cells increased the most, whereas the percentage of infiltration of resting mast cells decreased the most. In normal tissues, CD8+ T cells had the strongest infiltrating correlation with monocytes, while activated NK cells and naive B cells were the highest in PCa tissues. Moreover, the infiltration of five immune cells was significantly associated with TMB score and mutations of immune gene change the infiltration of immune cells. The Area Under Curve (AUC) of the multivariate regression model for the five- and 10-year survival prediction of PCa reached 0.796 and 0.862. The validation cohort proved that the model was reproducible.ConclusionsThis study demonstrated that different infiltrating immune cells in prostate cancer, especially higher infiltrating M1 macrophages and neutrophils in PCa tissue, are associated with patients' prognosis, suggesting that these two immune cells might be potential targets for PCa diagnosis and prognosis of treatment.

Project description:Purpose: The role of 5-methylcytosine-related long non-coding RNAs (m5C-lncRNAs) in breast cancer (BC) remains unclear. Here, we aimed to investigate the prognostic value, gene expression characteristics, and correlation between m5C-lncRNA risk model and tumor immune cell infiltration in BC. Methods: The expression matrix of m5C-lncRNAs in BC was obtained from The Cancer Genome Atlas database, and the lncRNAs were analyzed using differential expression analysis as well as univariate and multivariate Cox regression analysis to eventually obtain BC-specific m5C-lncRNAs. A risk model was developed based on three lncRNAs using multivariate Cox regression and the prognostic value, accuracy, as well as reliability were verified. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment of the risk model. CIBERSORT algorithm and correlation analysis were used to explore the characteristics of the BC tumor-infiltrating immune cells. Finally, reverse transcription-quantitative polymerase chain reaction was performed to detect the expression level of three lncRNA in clinical samples. Results: A total of 334 differential m5C-lncRNAs were identified, and three BC-specific m5C-lncRNAs were selected, namely AP005131.2, AL121832.2, and LINC01152. Based on these three lncRNAs, a highly reliable and specific risk model was constructed, which was proven to be closely related to the prognosis of patients with BC. Therefore, a nomogram based on the risk score was built to assist clinical decisions. GSEA revealed that the risk model was significantly enriched in metabolism-related pathways and was associated with tumor immune cell infiltration based on the analysis with the CIBERSORT algorithm. Conclusion: The efficient risk model based on m5C-lncRNAs associated with cancer metabolism and tumor immune cell infiltration could predict the survival prognosis of patients, and AP005131.2, AL121832.2, and LINC01152 could be novel biomarkers and therapeutic targets for BC.

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Characteristics of Infiltrating Immune Cells and a Predictive Immune Model for Cervical Cancer.

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