Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.MethodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).ResultsThere were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.ConclusionsThese results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

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Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.796729 ◽

2021 ◽

Vol 9 ◽

Author(s):

Peng Gu ◽

Lei Zhang ◽

Ruitao Wang ◽

Wentao Ding ◽

Wei Wang ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

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Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

10.21203/rs.3.rs-139597/v2 ◽

2021 ◽

Author(s):

Chen-jie Qiu ◽

Xue-bing Wang ◽

Zi-ruo Zheng ◽

Chao-zhi Yang ◽

Kai Lin ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cell ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Immune Cell ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Immune Cell Infiltration

Abstract Background: The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

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Prognostic Model to Predict Overall Survival for Metastatic Non-Small Cell Lung Cancer Patients Treated With Chemotherapy Combined With Concurrent Radiation Therapy to the Primary Tumor: Analysis From Two Prospective Studies

Frontiers in Oncology ◽

10.3389/fonc.2021.625688 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ling-Feng Liu ◽

Qing-Song Li ◽

Yin-Xiang Hu ◽

Wen-Gang Yang ◽

Xia-Xia Chen ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Prognostic Model ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Moderate Risk ◽

Metastatic Nsclc ◽

Nsclc Patients ◽

Group 1

PurposeThe role of radiotherapy, in addition to chemotherapy, has not been thoroughly determined in metastatic non-small cell lung cancer (NSCLC). The purpose of the study was to investigate the prognostic factors and to establish a model for the prediction of overall survival (OS) in metastatic NSCLC patients who received chemotherapy combined with the radiation therapy to the primary tumor.MethodsThe study retrospectively reviewed 243 patients with metastatic NSCLC in two prospective studies. A prognostic model was established based on the results of the Cox regression analysis.ResultsMultivariate analysis showed that being male, Karnofsky Performance Status score < 80, the number of chemotherapy cycles <4, hemoglobin level ≤120 g/L, the count of neutrophils greater than 5.8 ×109/L, and the count of platelets greater than 220 ×109/L independently predicted worse OS. According to the number of risk factors, patients were further divided into one of three risk groups: those having ≤ 2 risk factors were scored as the low-risk group, those having 3 risk factors were scored as the moderate-risk group, and those having ≥ 4 risk factors were scored as the high-risk group. In the low-risk group, 1-year OS is 67.7%, 2-year OS is 32.1%, and 3-year OS is 19.3%; in the moderate-risk group, 1-year OS is 59.6%, 2-year OS is 18.0%, and 3-year OS is 7.9%; the corresponding OS rates for the high-risk group were 26.2%, 7.9%, and 0% (P<0.001) respectively.ConclusionMetastatic NSCLC patients treated with chemotherapy in combination with thoracic radiation may be classified as low-risk, moderate-risk, or high-risk group using six independent prognostic factors. This prognostic model may help design the study and develop the plans of individualized treatment.

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Development of an Oncogenic Driver Alteration Associated Immune-Related Prognostic Model for Stage I-II Lung Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2020.593022 ◽

2021 ◽

Vol 10 ◽

Author(s):

Jian-Zhao Xu ◽

Chen Gong ◽

Zheng-Fu Xie ◽

Hua Zhao

Keyword(s):

High Risk ◽

Mast Cells ◽

Tumor Microenvironment ◽

Lung Adenocarcinoma ◽

Prognostic Model ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Stage I ◽

Oncogenic Driver

Lung adenocarcinoma (LUAD) needs to be stratified for its heterogeneity. Oncogenic driver alterations such as EGFR mutation, ALK translocation, ROS1 translocation, and BRAF mutation predict response to treatment for LUAD. Since oncogenic driver alterations may modulate immune response in tumor microenvironment that may influence prognosis in LUAD, the effects of EGFR, ALK, ROS1, and BRAF alterations on tumor microenvironment remain unclear. Immune-related prognostic model associated with oncogenic driver alterations is needed. In this study, we performed the Cox-proportional Hazards Analysis based on the L1-penalized (LASSO) Analysis to establish an immune-related prognostic model (IPM) in stage I-II LUAD patients, which was based on 3 immune-related genes (PDE4B, RIPK2, and IFITM1) significantly enriched in patients without EGFR, ALK, ROS1, and BRAF alterations in The Cancer Genome Atlas (TCGA) database. Then, patients were categorized into high-risk and low-risk groups individually according to the IPM defined risk score. The predicting ability of the IPM was validated in GSE31210 and GSE26939 downloaded from the Gene Expression Omnibus (GEO) database. High-risk was significantly associated with lower overall survival (OS) rates in 3 independent stage I-II LUAD cohorts (all P < 0.05). Moreover, the IPM defined risk independently predicted OS for patients in TCGA stage I-II LUAD cohort (P = 0.011). High-risk group had significantly higher proportions of macrophages M1 and activated mast cells but lower proportions of memory B cells, resting CD4 memory T cells and resting mast cells than low-risk group (all P < 0.05). In addition, the high-risk group had a significantly lower expression of CTLA-4, PDCD1, HAVCR2, and TIGIT than the low-risk group (all P < 0.05). In summary, we established a novel IPM that could provide new biomarkers for risk stratification of stage I-II LUAD patients.

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55例血管免疫母细胞性T细胞淋巴瘤的临床特征和预后模型

Blood ◽

10.1182/blood-2020-141699 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 37-38

Author(s):

Xiaohong Tan ◽

Jie Sun ◽

Sha He ◽

Chao Rong ◽

Hong Cen

Keyword(s):

High Risk ◽

Prognostic Model ◽

Risk Group ◽

Prognostic Index ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Intermediate Risk ◽

Ecog Ps ◽

Medical University

Angioimmunoblastic T-cell lymphoma (AITL) is a distinct subtype of peripheral T-cell lymphoma with unique clinical and pathological features. This study aim to analyze the characteristics of AITL and to design a prognostic model specifically for AITL, providing risk stratification in affected patients. We retrospectively analyzed 55 newly diagnosed AITL patients at the Affiliated Tumor Hospital of Guangxi Medical University from January 2007 to June 2016 and was permitted by the Ethics Committee of the Affiliated Tumor Hospital of Guangxi Medical University. Among these patients, the median age at diagnosis was 61 (27-85) and 54.55% (30/55) of the patients were older than 60 years. 43 patients were male, accounting for 78.18% of the whole. Among these, 92.73% (51/55) of the diagnoses were estimated at advanced stage. A total of 20 (36.36%) patients were scored >1 by the ECOG performance status. Systemic B symptoms were described in 16 (29.09%) patients. In nearly half of the patients (27/55; 49.09%) had extranodal involved sites. The most common extranodal site involved was BM (11/55; 20.00%). 38.18% (21/55) and 27.27% (15/55) patients had fever with body temperature ≥37.4℃ and pneumonia, respectively. 40% (22/55) patients had cavity effusion or edema. Laboratory investigations showed the presence of anemia (hemoglobin <120 g/L) in 60% (33/55), thrombocytopenia (platelet counts <150×109/L) in 29.09% (16/55), and elevated serum LDH level in 85.45% (47/55) of patients. Serum C-reactive protein and β2-microglobulin levels were found to be elevated in 60.98% (25/41) and 75.00% (36/48)of the patients, respectively. All patients had complete information for stratification into 4 risk subgroups by IPI score, in which scores of 0-1 point were low risk (9/55;16.36%), two points were low-intermediate risk (17/55; 30.92%), three points were high-intermediate risk (20/55; 36.36%), and four to five points were high risk (9/55; 16.36%). 55 patients were stratified by PIT score with 7.27% (4/55) of patients classified as low risk, 32.73% (18/55) as low-intermediate risk, 34.55% (19/55) as high-intermediate risk, and 25.45% (14/55) as high risk depending on the numbers of adverse prognostic factors.The estimated two-year and five-year overall survival (OS) rate for all patients were 50.50% and 21.70%. Univariate analysis suggested that ECOG PS (p= 0.000), Systemic B symptoms (p= 0.006), fever with body temperature ≥ 37.4℃ (p= 0.000), pneumonia (p= 0.001), cavity effusion or edema (p= 0.000), anemia (p= 0.013), and serum LDH (p= 0.007) might be prognostic factors (p< 0.05) for OS. Multivariate analysis found prognostic factors for OS were ECOG PS (p= 0.026), pneumonia (p= 0.045), and cavity effusion or edema(p= 0.003). We categorized three risk groups: low-risk group, no adverse factor; intermediate-risk group, one factor; and high-risk group, two or three factors. Five-year OS was 41.8% for low-risk group, 15.2% for intermediate-risk group, and 0.0% for high-risk group (p< 0.000). Patients with AITL had a poor outcome. This novel prognostic model balanced the distribution of patients into different risk groups with better predictive discrimination as compared to the International Prognostic Index and Prognostic Index for PTCL. Disclosures No relevant conflicts of interest to declare.

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Risk Stratification Using a New Prognostic Model for Patients with Secondary Acute Myeloid Leukemia - Results of the DSIL-AML96 Trial.

Blood ◽

10.1182/blood.v114.22.2652.2652 ◽

2009 ◽

Vol 114 (22) ◽

pp. 2652-2652

Author(s):

Friedrich Stölzel ◽

Walter E. Aulitzky ◽

Heinrich Bodenstein ◽

Martin Bornhäuser ◽

Michael Kramer ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Myeloid Leukemia ◽

Prognostic Model ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Allogeneic Hsct ◽

Secondary Acute Myeloid Leukemia ◽

Acute Myeloid

Abstract Abstract 2652 Poster Board II-628 Background: Secondary acute myeloid leukemia (sAML) following a myelodysplastic syndrome (mdsAML) or deriving as therapy-related AML (tAML) is regarded as an entity with a poor prognosis and patients are normally treated as high risk AML. However due to progress in elucidating the impact of molecular and cytogenetic markers and therefore combining biological and clinical data for prognosis and treatment outcome the aim of this analysis was to provide a prognostic scoring system for this entity by including clinical and laboratory data from patients being treated in the prospective AML96 trial of the DSIL study group. Patients and methods: A total of 318 patients with sAML (mdsAML = 239 and tAML = 79) were treated within the AML96 trial with a median follow-up for patients alive of 5.66 years (95% CI 4.426 – 6.895). All patients received double induction chemotherapy. Consolidation therapy contained high-dose cytosine arabinoside and for patients ' 60 years of age the option of autologous or allogeneic hematopoietic stem cell transplantation (HSCT) according to donor availability. Prognostic factors for survival were analyzed in the whole group of sAML patients in a multivariate Cox regression model for overall survival (OS) stratified by treatment groups (chemo-consolidation vs. allogeneic HSCT). Model selection was performed by backward selection applying the Likelihood-Ratio-Test. Results: Complete remission (CR) rate for all patients was 30.8% (n = 96). CR rate was lower in patients with mdsAML compared to patients with tAML (25.9% vs. 44.3%, p=.003). Patients with mdsAML were older and had a higher percentage of CD34+ blasts at diagnosis but to a lower extend aberrant karyotypes than patients with tAML. OS and disease free survival (DFS) at three years for all patients was 15.8% and 20.6%, respectively. While disease status (mdsAML vs. tAML) had no independent influence on survival, the dichotomized prognostic factors platelet count in the peripheral blood at diagnosis [HR = 0.535 (95% CI .415 – .689), p=<.000] as well as the NPM1 mutational status in the bone marrow at diagnosis [HR = 0.572 (95% CI .351 – .933), p=.025] were detected as independent predictors for overall survival. By combining these two variables, a prognostic model for OS with two risk groups for patients with sAML could be established with the low risk group being NPM1 positive or having platelets of >50 Gpt/l at diagnosis and the high risk group being NPM1 negative and having platelets of '50 Gpt/l at diagnosis. Three year OS for patients who received chemo-consolidation in the low risk group was 19.9% [95% CI = .128 - .270] and for patients in the high risk group 5.1% [95% CI = .014 - .088], p<.001. For patients who underwent allogeneic HSCT in first CR belonging to the low risk group the three year OS was 53.8% [95% CI = .346 - .730] and for patients in the high risk group 15.4% [95% CI = .000 - .35], p<.001. Conclusions: For patients with sAML we provide a new prognostic model for risk stratification: 1) NPM1+ or Platelets >50 Gpt/l defining a low risk group and 2) NPM1- and Platelets ' 50 Gpt/l defining a high risk group. Disclosures: No relevant conflicts of interest to declare.

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The Pyroptosis-Related 9 LncRNA Signature and LncRNA-miRNA-mRNA Regulatory Network in Breast Cancer: A Comprehensive Analysis Based On TCGA Database

10.21203/rs.3.rs-889312/v1 ◽

2021 ◽

Author(s):

Ye Tian ◽

Yanan Zhang ◽

Jing Dong ◽

Lin Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Regulatory Network ◽

Prognostic Model ◽

Immune Cell ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Tumor Immune Microenvironment ◽

Cox Analysis

Abstract Background: Pytoproptosis has been verified to participate in various malignancies. However, studies on pyroptosis-related lncRNAs in breast cancer and its effects on tumor immune micro-environment are still limited. Consequently, it was aimed in this study to construct a pyroptosis-related lncRNAs signature for prognostic prediction and explore the effect of the pyroptosis-related LncRNAs on tumor immune microenvironment through LncRNA-miRNA-mRNA regulatory network. Methods: The pyroptosis-related differentially expressed genes (DEGs) were discovered using differential expression analysis. The differentially expressed LncRNAs (DELncRNAs) associated with DEGs were discovered using correlation analysis. The function of DEGs was analyed using GO and KEGG analyses. The LncRNAs signature used as the prognostic model of breast cancer was constructed using univariate and multivariate Cox analysis, and the effectiveness was verified by K-M analysis and ROC curve. The risk score calculated using the prognostic model was proved as an independent factor by univariate Cox analysis, multivariate Cox analysis and PCA analysis, and used to predict patient prognosis through nomogram. The pathyways enriched in High risk group and Low risk group were analyzed by GSEA. The differences in immune cell distribution (B cell memory, T cell CD4+, T cell CD8+ among others) were analyzed using ssGSEA. The immune function (type I/II IFN response among others), immune checkpoint (ADORA2A among others) and m6A-related protein expression (FTO among others) of High risk group and Low risk group were compared. The regulatory network of pyroptosis-related LncRNA-miRNA-mRNA was constructed and the core network was extracted. The functions of the target genes of miRNA associated with DELncRNAs were explored using GO and KEGG analysis. Results: A 9 LncRNAs signature (LMNTD2-AS1, AL589765.4, AC079298.3, U62317.3, LINC02446, AL645608.7, HSD11B1-AS1, AC009119.1, AC087239.1) was constructed as the prognostic model of breast cancer. Significant differences were discovered in immune cell distribution, immune function, immune checkpointand m6A-related protein expression between High risk group and Low risk group. The regulatory network of LncRNA-miRNA-mRNA was constructed and found to participate in the crosstalk among apoptosis, pyroptosis and necroptosis of breast cancer. Conclusions: The 9 lncRNAs signature was valuable for predicting breast cancer prognosis, and the pyroptosis-related lncRNAs influenced tumor immune microenvironment of breast cancer through the LncRNA-miRNA-mRNA regulatory network.

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Identification and Validation of an Autophagy-Related lncRNA Signature for Patients With Breast Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.597569 ◽

2021 ◽

Vol 10 ◽

Author(s):

Ruyue Zhang ◽

Qingwen Zhu ◽

Detao Yin ◽

Zhe Yang ◽

Jinxiu Guo ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Cancer Patients ◽

Roc Curve ◽

Prognostic Model ◽

Risk Group ◽

High Risk Group ◽

Potential Functions ◽

Low Risk ◽

Breast Cancer Patients

BackgroundAutophagy is a “self-feeding” phenomenon of cells, which is crucial in mammalian development. Long non-coding RNA (lncRNA) is a new regulatory factor for cell autophagy, which can regulate the process of autophagy to affect tumor progression. However, poor attention has been paid to the roles of autophagy-related lncRNAs in breast cancer.ObjectiveThis study aimed to construct an autophagy-related lncRNA signature that can effectively predict the prognosis of breast cancer patients and explore the potential functions of these lncRNAs.MethodsThe RNA sequencing (RNA-Seq) data of breast cancer patients was collected from The Cancer Genome Atlas (TCGA) database and the GSE20685 database. Multivariate Cox analysis was implemented to produce an autophagy-related lncRNA signature in the TCGA cohort. The signature was then validated in the GSE20685 cohort. The receiver operator characteristic (ROC) curve was performed to evaluate the predictive ability of the signature. Gene set enrichment analysis (GSEA) was used to explore the potential functions based on the signature. Finally, the study developed a nomogram and internal verification based on the autophagy-related lncRNAs.ResultsA signature composed of 9 autophagy-related lncRNAs was determined as a prognostic model, and 1,109 breast cancer patients were divided into high-risk group and low-risk group based on median risk score of the signature. Further analysis demonstrated that the over survival (OS) of breast cancer patients in the high-risk group was poorer than that in the low-risk group based on the prognostic signature. The area under the curve (AUC) of ROC curve verified the sensitivity and specificity of this signature. Additionally, we confirmed the signature is an independent factor and found it may be correlated to the progression of breast cancer. GSEA showed gene sets were notably enriched in carcinogenic activation pathways and autophagy-related pathways. The qRT-PCR identified 5 lncRNAs with significantly differential expression in breast cancer cells based on the 9 lncRNAs of the prognostic model, and the results were consistent with the tissues.ConclusionIn summary, our signature has potential predictive value in the prognosis of breast cancer and these autophagy-related lncRNAs may play significant roles in the diagnosis and treatment of breast cancer.

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Prognostic Immune-Related Analysis Based on Differentially Expressed Genes in Left- and Right-Sided Colon Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.640196 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jun-Nan Guo ◽

Ming-Qi Li ◽

Shen-Hui Deng ◽

Chen Chen ◽

Yin Ni ◽

...

Keyword(s):

High Risk ◽

Differentially Expressed Genes ◽

Cox Regression ◽

Risk Group ◽

Colon Adenocarcinoma ◽

High Risk Group ◽

Low Risk ◽

Differentially Expressed ◽

Model Group ◽

Gene Pairs

BackgroundColon adenocarcinoma (COAD) can be divided into left-sided and right-sided COAD (LCCs and RCCs, respectively). They have unique characteristics in various biological aspects, particularly immune invasion and prognosis. The purpose of our study was to develop a prognostic risk scoring model (PRSM) based on differentially expressed immune-related genes (IRGs) between LCCs and RCCs, therefore the prognostic key IRGs could be identified.MethodsThe gene sets and clinical information of COAD patients were derived from TCGA and GEO databases. The comparison of differentially expressed genes (DEGs) of LCCs and RCCs were conducted with appliance of “Limma” analysis. The establishment about co-expression modules of DEGs related with immune score was conducted by weighted gene co-expression network analysis (WGCNA). Furthermore, we screened the module genes and completed construction of gene pairs. The analysis of the prognosis and the establishment of PRSM were performed with univariate- and lasso-Cox regression. We employed the PRSM in the model group and verification group for the purpose of risk group assignment and PRSM accuracy verification. Finally, the identification of the prognostic key IRGs was guaranteed by the adoption of functional enrichment, “DisNor” and protein-protein interaction (PPI).ResultsA total of 215 genes were screened out by differential expression analysis and WGCNA. A PRSM with 16 immune-related gene pairs (IRGPs) was established upon the genes pairing. Furthermore, we confirmed that the risk score was an independent factor for survival by univariate- and multivariate-Cox regression. The prognosis of high-risk group in model group (P < 0.001) and validation group (P = 0.014) was significantly worse than that in low-risk group. Treg cells (P < 0.001) and macrophage M0 (P = 0.015) were highly expressed in the high-risk group. The functional analysis indicated that there was significant up-regulation with regard of lymphocyte and cytokine related terms in low-risk group. Finally, we identified five prognostic key IRGs associated with better prognosis through PPI and prognostic analysis, including IL2RB, TRIM22, CIITA, CXCL13, and CXCR6.ConclusionThrough the analysis and screening of the DEGs between LCCs and RCCs, we constructed a PRSM which could predicate prognosis of LCCs and RCCs, and five prognostic key IRGs were identified as well. Therefore, the basis for identifying the benefits of immunotherapy and immunomodulatory was built.

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Identification and External Validation of a Transcription Factor-Related Prognostic Signature in Pediatric Neuroblastoma

Journal of Oncology ◽

10.1155/2021/1370451 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Rujia Wang ◽

Qian Wang

Keyword(s):

Transcription Factor ◽

Transcription Factors ◽

High Risk ◽

Prognostic Model ◽

Target Gene ◽

Cox Regression ◽

Risk Group ◽

External Validation ◽

High Risk Group ◽

Low Risk

Background. Neuroblastoma is a common solid tumor originating from the sympathetic nervous system, commonly found in children, and it is one of the leading causes of tumor-related deaths in children. In addition to pathological features, molecular-level features, such as how much gene expression is present and the mutational profile, may provide useful information for the precise treatment of neuroblastoma. Transcription factors (TFs) play an important regulatory role in all aspects of cellular life activities. But there are currently no studies on transcription factor-based biomarkers of neuroblastoma prognosis, and this study is much needed. Methods. We downloaded RNA transcriptome data and clinical data from the TARGET database to construct a prognostic model. The prognostic model was constructed by using univariate Cox analysis, LASSO, and multivariate Cox regression. We divided the patients into low-risk and high-risk groups using the median value of the risk score as the cut-off. Then, we validated the prognostic model with the dataset GSE49710. Results. We constructed a prognostic model consisting of eight genes (SATB1, ZNF564, SOX14, EN1, IKZF2, SLC2A4RG, FOXJ2, and ZNF521). Patients in the high-risk group had a lower survival rate than those in the low-risk group. The area under the 3-year ROC curve of the model reached 0.825, suggesting a good predictive efficacy. We performed target gene prediction for the eight transcription factors in the model using six online databases and found that TUT1 may be a target gene for transcription factor EN1 and is associated with immune infiltration. Conclusion. This prognostic model consisting of eight transcription factor-associated genes demonstrated reliable predictive efficacy. This prediction model may provide new potential targets for the treatment of neuroblastoma and personalized monitoring of neuroblastoma patients with high and low risk.

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