Correlation of CCL8 expression with immune cell infiltration of skin cutaneous melanoma: potential as a prognostic indicator and therapeutic pathway

Abstract Background The tumor microenvironment (TME) is critical in the progression and metastasis of skin cutaneous melanoma (SKCM). Differences in tumor-infiltrating immune cells (TICs) and their gene expression have been linked to cancer prognosis. Given that immunotherapy can be effective against SKCM, we aimed to identify key genes that regulate the immunological state of the TME in SKCM. Methods Data from 471 SKCM patients in the The Cancer Genome Atlas were analyzed using ESTIMATE algorithms to generate an ImmuneScore, StromalScore, and EstimateScore for each patient. Patients were classified into low- or high-score groups based on median values, then compared in order to identify differentially expressed genes (DEGs). Then a protein–protein interaction (PPI) network was developed, and a prognostic model was created using uni- and multivariate Cox regression as well as the least absolute shrinkage and selection operator (LASSO). Key DEGs were identified using the web-based tool GEPIA. Profiles of TIC subpopulations in each patient were analyzed using CIBORSORT, and possible correlations between key DEG expression and TICs were explored. Levels of CCL8 were determined in SKCM and normal skin tissue using immunohistochemistry. Results Two scores correlated positively with the prognosis of SKCM patients. Comparison of the low- and high-score groups revealed 1684 up-regulated and 18 down-regulated DEGs, all of which were enriched in immune-related functions. The prognostic model identified CCL8 as a key gene, which CIBERSORT found to correlate with M1 macrophages. Immunohistochemistry revealed strong expression in SKCM tissue, but failed to detect the protein in normal skin tissue. Conclusions CCL8 is a potential prognostic marker for SKCM, and it may become an effective target for melanoma in which M1 macrophages play an important role.

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Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/2191709 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Jimin He ◽

Chun Zeng ◽

Yong Long

Keyword(s):

Risk Stratification ◽

Prognostic Model ◽

Cox Regression ◽

Prognostic Indicator ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Cox Regression Analysis ◽

Immune Related Genes ◽

Genome Atlas

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients’ overall survival (OS). The turquoise module ( cor = 0.67 ; P < 0.001 ) and its genes ( n = 1092 ) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome ( P < 0.0001 ). Also, this IRRS model was found to be an independent prognostic indicator of gliomas’ survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

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A Ferroptosis-Related Gene Model Predicts Prognosis and Immune Microenvironment for Cutaneous Melanoma

Frontiers in Genetics ◽

10.3389/fgene.2021.697043 ◽

2021 ◽

Vol 12 ◽

Author(s):

Congcong Xu ◽

Hao Chen

Keyword(s):

Cutaneous Melanoma ◽

Prognostic Model ◽

Cox Regression ◽

Tumor Biology ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Related Gene ◽

Immune Microenvironment ◽

Cancer Genome Atlas ◽

Prognostic Prediction

BackgroundCutaneous melanoma is a common but aggressive tumor. Ferroptosis is a recently discovered cell death with important roles in tumor biology. Nevertheless, the prognostic power of ferroptosis-linked genes remained unclear in cutaneous melanoma.MethodsCutaneous melanoma patients of TCGA (The Cancer Genome Atlas) were taken as the training cohort while GSE65904 and GSE22153 as the validation cohorts. Multifactor Cox regression model was used to build a prognostic model, and the performance of the model was assessed. Functional enrichment and immune infiltration analysis were used to clarify the mechanisms.ResultsA five ferroptosis-linked gene predictive model was developed. ALOX5 and GCH1 were illustrated as independent predictive factors. Functional assessment showed enriched immune-linked cascades. Immune infiltrating analysis exhibited the distinct immune microenvironment.ConclusionHerein, a novel ferroptosis-related gene prognostic model was built in cutaneous melanoma. This model could be used for prognostic prediction, and maybe helpful for the targeted and immunotherapies.

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Comprehensive Analysis of the Immune Effect of TGFβ1 in Colorectal Adenocarcinoma:A TGFβ1-related Prognostic Model of Tumor Microenvironment

10.21203/rs.3.rs-477295/v1 ◽

2021 ◽

Author(s):

Yucheng Qian ◽

Lina Zhang ◽

Jihang Wen ◽

Yanxia Mei ◽

Jingsun Wei ◽

...

Keyword(s):

Tumor Microenvironment ◽

Survival Data ◽

Prognostic Model ◽

Immune Cell ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

High Expression ◽

Immune Checkpoints ◽

Cox Regression Analysis

Abstract ColorColorectal cancer is one of the most common cancer worldwide. Recently, tumor microenvironment (TME), especially its remoulding , is thought to control the colorectal cancer genesis and progression. In this study, we use ESTIMATEscore to make out the proportion of immune and stromal components in colorectal adenocarcinoma (CRA) samples from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were found by COX regression analysis and protein-protein interaction (PPI) network, among which TGFβ1 was supposed to be a prognosis factor and tumor environment indicator. Continuous analysis showed that TGFβ1 expression is positively correlated with lymph node metastasis (N stage) but negatively correlated with survival. Gene Set Enrichment Analysis (GSEA) revealed that the genes of the high-expression TGFβ1 group were mainly enriched in immune-related activities. Cluster analysis divided the samples into 2 subgroups. 24 HLA-related genes and 8 immune checkpoint genes were found upregulated in the high immunity group as well as TGFβ1, which suggests the possibility of novel therapies targeting immune checkpoints combined with TGFβ1. Tumor-infiltrating immune cell (TIC) profile of CRA patients was described by CIBERSORT analysis. Further analysis showed that the infiltration of Tregs and Neutrophils were positively correlated with TGFβ1 high expression. Then 3 TGFβ1-related genes were picked out to construct a prognostic model, which matches the survival data well.

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Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis

Cancer Cell International ◽

10.1186/s12935-021-01867-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Qian Chen ◽

Bingqing Qiu ◽

Xiaoyun Zeng ◽

Lang Hu ◽

Dongping Huang ◽

...

Keyword(s):

Cervical Cancer ◽

Tumor Microenvironment ◽

Prognostic Model ◽

Immune Cell ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Risk Scores ◽

Kaplan Meier ◽

Cox Analysis

Abstract Background Previous studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer. Methods We scored 307 CESC samples from The Cancer Genome Atlas (TCGA) and divided them into high and low matrix and immune scores using the ESTIMATE algorithm for differential gene analysis. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The multigene signature prognostic model was constructed by Cox analyses. Multivariate Cox analysis was applied to evaluate the significance of the multigene signature for cervical cancer prognosis. Prognosis was assessed by Kaplan–Meier curves comparing the different groups, and the accuracy of the prognostic model was analyzed by receiver operating characteristic-area under the curve (ROC-AUC) analysis and calibration curve. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the relationship between the multigene signature and immune cell infiltration. Results We obtained 420 differentially expressed genes in the tumor microenvironment from 307 patients with cervical cancer. A three-gene signature (SLAMF1, CD27, SELL) model related to the tumor microenvironment was constructed to assess patient survival. Kaplan–Meier analysis showed that patients with high risk scores had a poor prognosis. The ROC-AUC value indicated that the model was an accurate predictor of cervical cancer prognosis. Multivariate cox analysis showed the three-gene signature to be an independent risk factor for the prognosis of cervical cancer. A nomogram combining the three-gene signature and clinical features was constructed, and calibration plots showed that the nomogram resulted in an accurate prognosis for patients. The three-gene signature was associated with T stage, M stage and degree of immune infiltration in patients with cervical cancer. Conclusions This research suggests that the developed three-gene signature may be applied as a biomarker to predict the prognosis of and personalized therapy for CESC.

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Prognostic value of intratumoral lymphocyte-to-monocyte ratio and M0 macrophage enrichment in tumor immune microenvironment of melanoma

Melanoma Management ◽

10.2217/mmt-2020-0019 ◽

2020 ◽

Vol 7 (4) ◽

pp. MMT51

Author(s):

Neil K Jairath ◽

Mark W Farha ◽

Ruple Jairath ◽

Paul W Harms ◽

Lam C Tsoi ◽

...

Keyword(s):

Cutaneous Melanoma ◽

Immune Cell ◽

Cox Regression ◽

Primary Melanoma ◽

The Cancer Genome Atlas ◽

Significant Heterogeneity ◽

Sequencing Data ◽

Melanoma Tumor ◽

Poor Prognostic Factor ◽

Lymphocyte To Monocyte Ratio

Skin cutaneous melanoma is characterized by significant heterogeneity in its molecular, genomic and immunologic features. Whole transcriptome RNA sequencing data from The Cancer Genome Atlas of skin cutaneous melanoma (n = 328) was utilized. CIBERSORT was used to identify immune cell type composition, on which unsupervised hierarchical clustering was performed. Analysis of overall survival was performed using Kaplan–Meier estimates and multivariate Cox regression analyses. Membership in the lymphocyte:monocytelow, monocytehigh and M0high cluster was an independently poor prognostic factor for survival (HR: 3.03; 95% CI: 1.12–8.20; p = 0.029) and correlated with decreased predicted response to immune checkpoint blockade. In conclusion, an M0-macrophage-enriched, lymphocyte-to-monocyte-ratio-low phenotype in the primary melanoma tumor site independently characterizes an aggressive phenotype that may differentially respond to treatment.

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Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-021-08314-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dongsheng He ◽

Shengyin Liao ◽

Lifang Cai ◽

Weiming Huang ◽

Xuehua Xie ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Factors ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Calibration Plot ◽

Clinical Parameters ◽

T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

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RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.650499 ◽

2022 ◽

Vol 12 ◽

Author(s):

Lan-Xin Mu ◽

You-Cheng Shao ◽

Lei Wei ◽

Fang-Fang Chen ◽

Jing-Wei Zhang

Keyword(s):

Breast Cancer ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Impact ◽

Immune Microenvironment ◽

Model Based ◽

Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

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Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.675197 ◽

2021 ◽

Vol 12 ◽

Author(s):

Min Zhou ◽

Shasha Hong ◽

Bingshu Li ◽

Cheng Liu ◽

Ming Hu ◽

...

Keyword(s):

Ovarian Cancer ◽

Dna Methylation ◽

Mrna Expression ◽

Risk Score ◽

Cox Regression ◽

Expression Profiles ◽

Figo Stage ◽

Prognostic Indicator ◽

Tissue Expression ◽

The Cancer Genome Atlas

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

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Combination the Gene Expression of Adjacent Normal and Tumor Makes more Robust Gene Signature as Cancer Prognosis Biomarker

10.21203/rs.3.rs-132449/v1 ◽

2020 ◽

Author(s):

Wei Ma ◽

Dandan Li ◽

Changjian Zhang ◽

Ming Xiong ◽

Yuanyuan Qiao

Keyword(s):

Expression Profile ◽

Normal Tissue ◽

Cox Regression ◽

Expression Profiles ◽

Lung Squamous Cell Carcinoma ◽

Enrichment Analysis ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Adjacent Normal Tissue

Abstract Purpose: We tried to explore new gene signature via the combination of tumor-derived expression profile and the adjacent normal-derived expression profile to find more robust cancer biomarker. Methods: Log2 transformed ratio of tumor tissue and the adjacent normal tissue (Log2TN) expression, tumor-derived expression, and normal-derived expression were used to do univariate Cox regression in The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma (LUSC) respectively. Then, we used factor analysis and least absolute shrinkage and selection operator Cox (LASSO-Cox) to select gene signature in TCGA LUSC for Log2TN, tumor, and adjacent normal respectively.Results: By comparing Log2TN with tumor and adjacent normal in LUSC, we found that genes derived from Log2TN show more robust (p = 0.006 and p = 0.001) and have lower p-values (p < 0.001). Gene signature selected from Log2TN shows the best generalization in the three GEO datasets even though only tumor-derived expression profiles were available in the three datasets. Enrichment analysis showed that the tumor cells mainly focus on proliferation with losing functional of metabolism.Conclusions: These results indicate that (1) Log2TN could get more robust genes and gene signature than tumor-derived expression profiles used traditionally; (2) the adjacent-normal tissue may also play an important role in the progress and outcome of the tumor.Implications for Cancer Survivors: By combined of tumor-derived expression profile and the adjacent normal-derived expression profile, we could find more robust gene signature than traditionally method. Using these robust gene signatures, robust cancer biomarkers could be constructed and will do great help to improve cancer prognosis.

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Clinical Value of lncRNA LUCAT1 Expression in Liver Cancer and its Potential Pathways

Journal of Gastrointestinal and Liver Diseases ◽

10.15403/jgld-356 ◽

2019 ◽

Vol 28 (4) ◽

pp. 439-447 ◽

Cited By ~ 7

Author(s):

Yan Jiao ◽

Yanqing Li ◽

Bai Ji ◽

Hongqiao Cai ◽

Yahui Liu

Keyword(s):

Liver Cancer ◽

Roc Curve ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Rank Test ◽

P Value ◽

Rna Seq ◽

Cox Regression Analysis

Background and Aims: Emerging studies indicate that long noncoding RNAs (lncRNAs) play a role as prognostic markers in many cancers, including liver cancer. Here, we focused on the lncRNA lung cancer-associated transcript 1 (LUCAT1) for liver cancer prognosis. Methods: RNA-seq and phenotype data were downloaded from the Cancer Genome Atlas (TCGA). Chisquare tests were used to evaluate the correlations between LUCAT1 expression and clinical features. Survival analysis and Cox regression analysis were used to compare different LUCAT1 expression groups (optimal cutoff value determined by ROC). The log-rank test was used to calculate the p-value of the Kaplan-Meier curves. A ROC curve was used to evaluate the diagnostic value. Gene Set Enrichment Analysis (GSEA) was performed, and competing endogenous RNA (ceRNA) networks were constructed to explore the potential mechanism. Results: Data mining of the TCGA -Liver Hepatocellular Carcinoma (LIHC) RNA-seq data of 371 patients showed the overexpression of LUCAT1 in cancerous tissue. High LUCAT1 expression was associated with age (p=0.007), histologic grade (p=0.009), T classification (p=0.022), and survival status (p=0.002). High LUCAT1 patients had a poorer overall survival and relapse-free survival than low LUCAT1 patients. Multivariate analysis identified LUCAT1 as an independent risk factor for poor survival. The ROC curve indicated modest diagnostic performance. GSEA revealed the related signaling pathways, and the ceRNA network uncovered the underlying mechanism. Conclusion: High LUCAT1 expression is an independent prognostic factor for liver cancer.

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