Construction and Validation of an Immune-Related Prognostic Model Based on TP53 Status in Colorectal Cancer

Growing evidence has indicated that prognostic biomarkers have a pivotal role in tumor and immunity biological processes. TP53 mutation can cause a range of changes in immune response, progression, and prognosis of colorectal cancer (CRC). Thus, we aim to build an immunoscore prognostic model that may enhance the prognosis of CRC from an immunological perspective. We estimated the proportion of immune cells in the GSE39582 public dataset using the CIBERSORT (Cell type identification by estimating relative subset of known RNA transcripts) algorithm. Prognostic genes that were used to establish the immunoscore model were generated by the LASSO (Least absolute shrinkage and selection operator) Cox regression model. We established and validated the immunoscore model in GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) cohorts, respectively; significant differences of overall survival analysis were found between the low and high immunoscore groups or TP53 subgroups. In the multivariable Cox analysis, we observed that the immunoscore was an independent prognostic factor both in the GEO cohort (HR (Hazard ratio) 1.76, 95% CI (confidence intervals): 1.26–2.46) and the TCGA cohort (HR 1.95, 95% CI: 1.20–3.18). Furthermore, we established a nomogram for clinical application, and the results suggest that the nomogram is a better predictive model for prognosis than immunoscore or TNM staging.

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Deciphering N6-Methyladenosine-Related Genes Signature to Predict Survival in Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2020/2514230 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Jie Zhu ◽

Min Wang ◽

Daixing Hu

Keyword(s):

Lung Adenocarcinoma ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Lasso Regression ◽

Gene Expressions ◽

Tumor Tissues ◽

Cancer Genome Atlas ◽

Selection Operator ◽

Cox Analysis

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Among these, lung adenocarcinoma (LUAD) accounts for most cases. Due to the improvement of precision medicine based on molecular characterization, the treatment of LUAD underwent significant changes. With these changes, the prognosis of LUAD becomes diverse. N6-methyladenosine (m6A) is the most predominant modification in mRNAs, which has been a research hotspot in the field of oncology. Nevertheless, little has been studied to reveal the correlations between the m6A-related genes and prognosis in LUAD. Thus, we conducted a comprehensive analysis of m6A-related gene expressions in LUAD patients based on The Cancer Genome Atlas (TCGA) database by revealing their relationship with prognosis. Different expressions of the m6A-related genes in tumor tissues and non-tumor tissues were confirmed. Furthermore, their relationship with prognosis was studied via Consensus Clustering Analysis, Principal Components Analysis (PCA), and Least Absolute Shrinkage and Selection Operator (LASSO) Regression. Based on the above analyses, a m6A-based signature to predict the overall survival (OS) in LUAD was successfully established. Among the 479 cases, we found that most of the m6A-related genes were differentially expressed between tumor and non-tumor tissues. Six genes, HNRNPC, METTL3, YTHDC2, KIAA1429, ALKBH5, and YTHDF1 were screened to build a risk scoring signature, which is strongly related to the clinical features pathological stages (p<0.05), M stages (p<0.05), T stages (p < 0.05), gender (p=0.04), and survival outcome (p=0.02). Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor, revealing that the m6A-related genes signature has great predictive value. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database.

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Downregulated SPINK4 is associated with poor survival in colorectal cancer

BMC Cancer ◽

10.1186/s12885-019-6484-5 ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Xiaojie Wang ◽

Qian Yu ◽

Waleed M. Ghareeb ◽

Yiyi Zhang ◽

Xingrong Lu ◽

...

Keyword(s):

Colorectal Cancer ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Expression Data ◽

Poor Survival ◽

Protein Levels ◽

Normal Tissues ◽

Human Protein Atlas ◽

Cancer Genome Atlas ◽

Independent Indicator

Abstract Background SPINK4 is known as a gastrointestinal peptide in the gastrointestinal tract and is abundantly expressed in human goblet cells. The clinical significance of SPINK4 in colorectal cancer (CRC) is largely unknown. Methods We retrieved the expression data of 1168 CRC patients from 3 Gene Expression Omnibus (GEO) datasets (GSE24551, GSE39582, GSE32323) and The Cancer Genome Atlas (TCGA) to compare the expression level of SPINK4 between CRC tissues and normal colorectal tissues and to evaluate its value in predicting the survival of CRC patients. At the protein level, these results were further confirmed by data mining in the Human Protein Atlas and by immunohistochemical staining of samples from 81 CRC cases in our own center. Results SPINK4 expression was downregulated in CRC compared with that in normal tissues, and decreased SPINK4 expression at both the mRNA and protein levels was associated with poor prognosis in CRC patients from all 3 GEO datasets, the TCGA database and our cohort. Additionally, lower SPINK4 expression was significantly related to higher TNM stage. Moreover, in multivariate regression, SPINK4 was confirmed as an independent indicator of poor survival in CRC patients in all databases and in our own cohort. Conclusions We concluded that reduced expression of SPINK4 relates to poor survival in CRC, functioning as a novel indicator.

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A Novel Prognostic Model for Clear Cell Renal Cell Carcinoma with Differentially Expressed Immune-related lncRNA Pairs Based on the Cancer Genome Atlas

10.21203/rs.3.rs-728356/v1 ◽

2021 ◽

Author(s):

Chen Zhao ◽

Kewei Xiong ◽

Fengming Liu ◽

Xiangpan Li

Keyword(s):

Renal Cell Carcinoma ◽

Renal Cell ◽

Prognostic Model ◽

Cox Regression ◽

Clear Cell ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Cell Renal Cell Carcinoma ◽

Cancer Genome Atlas ◽

Genome Atlas

Abstract Objective: To construct a novel prognostic model of immune-related lncRNA (irlncRNA) pairs in clear cell renal cell carcinoma (ccRCC). Methods: RNA-seq and clinical data were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed irlncRNAs (DEirlncRNAs) were obtained by co-expression strategy with immune genes. A 0-1 matrix was constructed according to DEirlncRNAs relevant expression levels. Univariate cox regression was used to select potential target pairs. Lasso regression with cross validation and multivariate cox regression were carried out to extract the final biomarker pairs for risk score calculation. Through calculating the optimal cutoff of AUCs, patients were divided into high and low risk group. Model validation was conducted by independent prognostic analysis, survival analysis, tumor-infiltrating and chemosensitivity analysis. Results: A total of 42 DEirlncRNAs were identified and 12 target pairs were included to construct the final model. The risk score were both significantly different according to univariate (p<0.001, HR=1.391, 95%CI [1.313–1.475]) and multivariate cox regression (p<0.001, HR=1.3104, 95%CI [1.227-1.399]). The AUC reached 0.765 at 1-year, 0.724 at 3-year and 0.785 at 5-year. Patients in the high-risk group had significantly poor survival, higher level of CD8+T infiltration, lower drug sensitivity of sunitinib and temsirolimus but higher sensitivity of lapatinib and pazopanib.Conclusion: The novel prognostic model constructed by paring irlncRNAs showed an effective clinical prediction in ccRCC patients.

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Identification of immune-related subtypes of colorectal cancer to improve antitumor immunotherapy

Scientific Reports ◽

10.1038/s41598-021-98966-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xiaobo Zheng ◽

Yong Gao ◽

Chune Yu ◽

Guiquan Fan ◽

Pengwu Li ◽

...

Keyword(s):

Colorectal Cancer ◽

Checkpoint Inhibitors ◽

Molecular Classification ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Training Cohort ◽

Immune System Activation ◽

Antitumor Immunotherapy ◽

Cancer Genome Atlas ◽

Tumor Management

AbstractImmunotherapy involving immune checkpoint inhibitors (ICIs) for enhancing immune system activation is promising for tumor management. However, the patients’ responses to ICIs are different. Here, we applied a non-negative matrix factorization algorithm to establish a robust immune molecular classification system for colorectal cancer (CRC). We obtained data of 1503 CRC patients (training cohort: 488 from The Cancer Genome Atlas; validation cohort: 1015 from the Gene Expression Omnibus). In the training cohort, 42.8% of patients who exhibited significantly higher immunocyte infiltration and enrichment of immune response-associated signatures were subdivided into immune classes. Within the immune class, 53.1% of patients were associated with a worse overall prognosis and belonged to the immune-suppressed subclass, characterized by the activation of stroma-related signatures, genes, immune-suppressive cells, and signaling. The remaining immune class patients belonged to the immune-activated subclass, which was associated with a better prognosis and response to anti-PD-1 therapy. Immune-related subtypes were associated with different copy number alterations, tumor-infiltrating lymphocyte enrichment, PD-1/PD-L1 expression, mutation landscape, and cancer stemness. These results were validated in patients with microsatellite instable CRC. We described a novel immune-related class of CRC, which may be used for selecting candidate patients with CRC for immunotherapy and tailoring optimal immunotherapeutic treatment.

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Immune implication of an autophagy-related prognostic signature in uveal melanoma

Bioscience Reports ◽

10.1042/bsr20211098 ◽

2021 ◽

Author(s):

Samuel Chuah ◽

Valerie Chew

Keyword(s):

Uveal Melanoma ◽

Immune Cell ◽

Cox Regression ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

High Rate ◽

Lasso Regression ◽

Cancer Hallmarks ◽

Selection Operator ◽

Stat3 Signalling

Uveal Melanoma (UM) is a rare cancer deriving from melanocytes within the uvea. It has a high rate of metastasis, especially to the liver, and a poor prognosis thereafter. Autophagy, an intracellular programmed digestive process, has been associated with the development and progression of cancers, with controversial pro- and anti-tumour roles. Although previous studies have been conducted on autophagy-related genes (ARGs) in various cancer types, its role in UM requires a deeper understanding for improved diagnosis and development of novel therapeutics. In the current study, Zheng et al. used univariate Cox regression followed by least absolute shrinkage and selection operator (Lasso) regression to identify a robust 9-ARG signature prognostic of survival in a total of 230 patients with UM. The authors used the Cancer Genome Atlas (TCGA) UM cohort as a training cohort (n=80) to identify the signature and validated it in another four independent cohorts of 150 UM patients from the Gene Expression Omnibus (GEO) repository (GSE22138, GSE27831, GSE44295 and GSE84976). This 9-ARG signature was also significantly associated with the enrichment of cancer hallmarks, including angiogenesis, IL6-KJAK-STAT3 signalling, reactive oxygen species pathway and oxidative phosphorylation. More importantly, this signature is associated with immune-related functional pathways and immune cell infiltration. Thus, this 9-ARG signature predicts prognosis and provides deeper insights into the immune mechanisms in UM, with potential implications for future immunotherapy.

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The Bioinformatics-Based Analysis Identifies 7 Immune-Related Genes as Prognostic Biomarkers for Colon Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.726701 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jie Pan ◽

Zongqi Weng ◽

Chaorong Xue ◽

Bingqiang Lin ◽

Mengxin Lin

Keyword(s):

Colon Cancer ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Prognostic Biomarkers ◽

Protein Protein Interaction ◽

Causative Factors ◽

Cancer Genome Atlas ◽

Selection Operator ◽

Cox Analysis ◽

Immune Related Genes

Colon cancer poses a great threat to human health. Currently, there is no effective treatment for colon cancer due to its complex causative factors. Immunotherapy has now become a new method for tumor treatment. In this study, 487 DEGs were screened from The Cancer Genome Atlas (TCGA) database and ImmPort database, and GeneOntology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. Hierarchical clustering of all samples revealed a significant correlation between colon cancer and immunity. The weighted gene co-expression network analysis (WGCNA) algorithm was used to identify key gene modules associated with immunity in colon cancer, here, module grey60 showed the highest correlation. A protein-protein interaction (PPI) network was constructed using the STRING database to screen hub genes, and subsequently, 7 immune-related genes the most closely associated with colon cancer were identified by differential expression in cancer and paracancer. Finally, a risk prediction model was developed using least absolute shrinkage and selection operator (LASSO) COX analysis, and the accuracy of the model was validated by GSE14333. This study determined that IRF4 and TNFRSF17 were immune-related genes in colon cancer, providing immune-related prognostic biomarkers for colon cancer.

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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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Development and validation of the trans-omics model for pancreatic adenocarcinoma

Epigenomics ◽

10.2217/epi-2020-0184 ◽

2020 ◽

Author(s):

Weiguo Huang ◽

Wanqing Weng ◽

Boda Wu ◽

Tingbo Ye ◽

Zhuo Lin ◽

...

Keyword(s):

Overall Survival ◽

Pancreatic Adenocarcinoma ◽

Cox Regression ◽

Operator Method ◽

Comprehensive Analysis ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Cancer Genome Atlas ◽

Selection Operator ◽

Development And Validation

Aim: To develop a trans-omics-based molecular clinicopathological algorithm for predicting pancreatic adenocarcinoma prognosis, we performed a comprehensive analysis of the expression levels of mRNA, DNA methylation and DNA copy number in The Cancer Genome Atlas dataset. Materials & methods: Based on the least absolute shrinkage and selection operator method – COX regression analysis, a trans-omics-based classifier was established to predict overall survival. Nomogram was constructed by combining the classifier band clinical pathological characterization. Results: Based on trans-omics, we developed a 10-gene-based classifier and a molecular-clinicopathologic nomogram for predicting overall survival with satisfactory accuracy. Conclusion: Trans-omics-based classifier and molecule-clinicopathological nomogram based on the classifier can accurately predict the prognosis of pancreatic adenocarcinoma patients

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Identification of lnc RNAs Related to Prognosis of Patients With Colorectal Cancer

Technology in Cancer Research & Treatment ◽

10.1177/1533033820962120 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096212

Author(s):

Yuqi Sun ◽

Peng Peng ◽

Lanlan He ◽

Xueren Gao

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

High Expression ◽

Cox Regression Analysis ◽

Cancer Genome Atlas ◽

Score Model ◽

Prognostic Prediction

The purpose of this study was to identify long noncoding RNAs (lncRNAs) related to prognosis of patients with colorectal cancer (CRC) and develop a prognostic prediction model for CRC. Transcriptome data and survival information of CRC patients were downloaded from The Cancer Genome Atlas. The differentially expressed lncRNAs (DElncRNAs) between CRC and normal colorectal tissues were identified by the edgeR package. The association of DElncRNAs expression with prognosis of CRC patients was analyzed by the survival package. A nomogram predicting 3- and 5- year overall survival of CRC patients was drawn by the rms package. A total of 1046 DElncRNAs were identified, including 271 down-regulated and 775 up-regulated lncRNAs in CRC. Multivariate Cox regression analysis showed 10 lncRNAs related to the prognosis of CRC patients. Thereinto high expression of AC004009.1, LHX1-DT, ELFN1-AS1, AL136307.1, AC087379.2, RBAKDN and AC078820.1 was associated with poorer prognosis of CRC patients. High expression of LINC01055, AL590483.1 and AC008514.1 was associated with better prognosis of CRC patients. Furthermore, the risk score model developed based on the 10 lncRNAs could effectively predict overall survival of CRC patients. In conclusion, 10 prognostic biomarkers for CRC were identified, which would be helpful to understand the role of lncRNAs in CRC progression.

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Tumor Immune Microenvironment Landscape in Glioma Identifies a Prognostic and Immunotherapeutic Signature

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.717601 ◽

2021 ◽

Vol 9 ◽

Author(s):

Chunyu Zhang ◽

Lirui Guo ◽

Zhongzhou Su ◽

Na Luo ◽

Yinqiu Tan ◽

...

Keyword(s):

Large Scale ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Mutation Burden ◽

Tumor Immune Microenvironment ◽

Cancer Genome Atlas ◽

Patient Prognosis ◽

Selection Operator ◽

Cox Analysis ◽

Genome Atlas

The tumor immune microenvironment (TIME) has been recognized to be associated with sensitivity to immunotherapy and patient prognosis. Recent research demonstrates that assessing the TIME patterns on large-scale samples will expand insights into TIME and will provide guidance to formulate immunotherapy strategies for tumors. However, until now, thorough research has not yet been reported on the immune infiltration landscape of glioma. Herein, the CIBERSORT algorithm was used to unveil the TIME landscape of 1,975 glioma observations. Three TIME subtypes were established, and the TIMEscore was calculated by least absolute shrinkage and selection operator (LASSO)–Cox analysis. The high TIMEscore was distinguished by an elevated tumor mutation burden (TMB) and activation of immune-related biological process, such as IL6-JAK-STAT3 signaling and interferon gamma (IFN-γ) response, which may demonstrate that the patients with high TIMEscore were more sensitive to immunotherapy. Multivariate analysis revealed that the TIMEscore could strongly and independently predict the prognosis of gliomas [Chinese Glioma Genome Atlas (CGGA) cohort: hazard ratio (HR): 2.134, p < 0.001; Gravendeel cohort: HR: 1.872, p < 0.001; Kamoun cohort: HR: 1.705, p < 0.001; The Cancer Genome Atlas (TCGA) cohort: HR: 2.033, p < 0.001; the combined cohort: HR: 1.626, p < 0.001], and survival advantage was evident among those who received chemotherapy. Finally, we validated the performance of the signature in human tissues from Wuhan University (WHU) dataset (HR: 15.090, p = 0.008). Our research suggested that the TIMEscore could be applied as an effective predictor for adjuvant therapy and prognosis assessment.

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