N6-Methylandenosine-Related lncRNAs in Tumor Microenvironment Are Potential Prognostic Biomarkers in Colon Cancer

BackgroundLncRNA dysregulation and the tumor microenvironment (TME) have been shown to play a vital role in the progression and prognosis of colon cancer (CC). We aim to reveal the potential molecular mechanism from the perspective of lncRNA in the TME and provide the candidate biomarkers for CC prognosis.MethodsESTIMATE analysis was used to divide the CC patients into high and low immune or stromal score groups. The expression array of lncRNA was re-annotated by Seqmap. Microenvironment-associated lncRNAs were filtered through differential analysis. The m6A-associated lncRNAs were screened by Pearson correlation analysis. Lasso Cox regression analyses were performed to construct the m6A- and tumor microenvironment-related lncRNA prognostic model (m6A-TME-LM). Survival analysis was used to assess the prognostic efficacy of candidate lncRNAs. Enrichment analyses annotated the candidate genes’ functions.ResultsWe obtained 25 common differentially expressed lncRNAs (DELs) associated with immune microenvironment and m6A-related genes for subsequent lasso analysis. Four out of these DELs were selected for the m6A-TME-LM. All the four lncRNAs were related to overall survival, and a test set testified the result. Further stratification analysis of the m6A-TME-LM retained its ability to predict OS for male and chemotherapy adjuvant patients and performed an excellent prognostic efficacy in the TNM stage III and IV subgroups. Network analysis also found the four lncRNAs mediated co-expression network was associated with tumor development.ConclusionWe constructed the m6A-TME-LM, which could provide a better prognostic prediction of CC.

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Deciphering the Pyroptosis-Related Prognostic Signature and Immune Cell Infiltration Characteristics of Colon Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.755384 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ran Wei ◽

Shuofeng Li ◽

Guanhua Yu ◽

Xu Guan ◽

Hengchang Liu ◽

...

Keyword(s):

Colon Cancer ◽

Tumor Microenvironment ◽

Immune Cell ◽

Cox Regression ◽

Tumor Development ◽

Assessment Model ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Immune Microenvironment ◽

Molecular Patterns

Background: Colon cancer (CC) remains one of the most common malignancies with a poor prognosis. Pyroptosis, referred to as cellular inflammatory necrosis, is thought to influence tumor development. However, the potential effects of pyroptosis-related regulators (PRRs) on the CC immune microenvironment remain unknown.Methods: In this study, 27 PRRs reported in the previous study were used to cluster the 1,334 CC samples into three pyroptosis-related molecular patterns. Through subtype pattern differential analysis and structure network mining using Weighted Gene Co-expression Network Analysis (WGCNA), 854 signature genes associated with the PRRs were discovered. Further LASSO-penalized Cox regression of these genes established an eight-gene assessment model for predicting prognosis.Results: The CC patients were subtyped based on three distinct pyroptosis-related molecular patterns. These pyroptosis-related patterns were correlated with different clinical outcomes and immune cell infiltration characteristics in the tumor microenvironment. The pyroptosis-related eight-signature model was established and used to assess the prognosis of CC patients with medium-to-high accuracy by employing the risk scores, which was named “PRM-scores.” Greater inflammatory cell infiltration was observed in tumors with low PRM-scores, indicating a potential benefit of immunotherapy in these patients.Conclusions: This study suggests that PRRs have a significant effect on the tumor immune microenvironment and tumor development. Evaluating the pyroptosis-related patterns and related models will promote our understanding of immune cell infiltration characteristics in the tumor microenvironment and provide a theoretical basis for future research targeting pyroptosis in cancer.

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Identification and Validation of a Seven m6A-related lncRNAs Signature Predicting Prognosis of Ovarian Cancer

10.21203/rs.3.rs-915700/v1 ◽

2021 ◽

Author(s):

Yan Li ◽

Xiaoying Wang ◽

Yue Han ◽

Xun Li

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Correlation Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Pearson Correlation ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Pearson Correlation Analysis

Abstract Background: Long non-coding RNAs (lncRNAs) play an important role in angiogenesis, immune response, inflammatory response and tumor development and metastasis. m6 A (N6 - methyladenosine) is one of the most common RNA modifications in eukaryotes. The aim of our research was to investigate the potential prognostic value of m6A-related lncRNAs in ovarian cancer (OC).Methods: The data we need for our research was downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Pearson correlation analysis between 21 m6A regulators and lncRNAs was performed to identify m6A-related lncRNAs. Univariate Cox regression analysis was implemented to screen for lncRNAs with prognostic value. A least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses was used to further reduct the lncRNAs with prognostic value and construct a m6A-related lncRNAs signature for predicting the prognosis of OC patients. Results: 275 m6A-related lncRNAs were obtained using pearson correlation analysis. 29 m6A-related lncRNAs with prognostic value was selected through univariate Cox regression analysis. Then, a seven m6A-related lncRNAs signature was identified by LASSO Cox regression. Each patient obtained a riskscore through multivariate Cox regression analyses and the patients were classified into high-and low-risk group using the median riskscore as a cutoff. Kaplan-Meier curve revealed that the patients in high-risk group have poor outcome. The receiver operating characteristic curve revealed that the predictive potential of the m6A-related lncRNAs signature for OC was powerful. The predictive potential of the m6A-related lncRNAs signature was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the m6A-related lncRNAs signature was an independent prognostic factor for OC patients. Moreover, a nomogram based on the expression level of the seven m6A-related lncRNAs was established to predict survival rate of patients with OC. Finally, a competing endogenous RNA (ceRNA) network associated with the seven m6A-related lncRNAs was constructed to understand the possible mechanisms of the m6A-related lncRNAs involed in the progression of OC.Conclusions: In conclusion, our research revealed that the m6A-related lncRNAs may affect the prognosis of OC patients and identified a seven m6A-related lncRNAs signature to predict the prognosis of OC patients.

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A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

BioMed Research International ◽

10.1155/2020/4845360 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Dakui Luo ◽

Zezhi Shan ◽

Qi Liu ◽

Sanjun Cai ◽

Qingguo Li ◽

...

Keyword(s):

Colon Cancer ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Differentially Expressed ◽

Metabolic Signature ◽

Metabolic Genes ◽

Prognostic Prediction

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.

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Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.784553 ◽

2021 ◽

Vol 8 ◽

Author(s):

Chen Jin ◽

Rui Li ◽

Tuo Deng ◽

Jialiang Li ◽

Yan Yang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prediction Model ◽

Risk Score ◽

Cox Regression ◽

Pearson Correlation ◽

Vital Role ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Prediction Ability ◽

Cox Regression Analysis

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

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The miR-200c/FUT4 axis prevents the proliferation of colon cancer cells by downregulating the Wnt/β-catenin pathway

10.21203/rs.3.rs-26901/v2 ◽

2020 ◽

Author(s):

Jinchun Cong ◽

Chuanjia Yang ◽

Zhixiu Xia ◽

Jian Gong ◽

Hong Zhang

Keyword(s):

Colon Cancer ◽

Correlation Analysis ◽

Cancer Cells ◽

Pearson Correlation ◽

Signalling Pathway ◽

Migration And Invasion ◽

Colon Cancer Cells ◽

Pearson Correlation Analysis ◽

Sw480 Cells ◽

Related Proteins

Abstract BackgroundTo investigate the effects of miR-200c, which targets fucosyltransferase 4 (FUT4), on the proliferation, migration and invasion of colon cancer cells and to further explore its mechanism.MethodsWe assessed the miR-200c and FUT4 mRNA levels in LoVo and SW480 cells by quantitative real-time polymerase chain reaction (qRT-PCR), and their correlation was analysed by Pearson correlation analysis. LipofectamineTM 2000 transfection reagent was used to transfect miR-200c mimic, FUT4 siRNA, FUT4 mimic and FUT4 mimic negative control into LoVo and SW480 cells, and RT-PCR was used to analyse the transfection efficiency. Cell Counting Kit-8 (CCK-8), colony formation and transwell assays were used to detect the migration, invasion and proliferation of LoVo and SW480 cells. Immunofluorescence was used to analyse the expression of the Ki-67 protein. Moreover, the expression of Wnt/β-catenin signalling pathway-related proteins was detected by western blots. A double luciferase experiment was performed to verify the targeting relationship between miR-200c and FUT4. In vivo, tumour growth and Wnt/β-catenin signalling pathway-related proteins were also analysed. ResultsPearson correlation analysis showed that miR-200c and FUT4 were negatively correlated in LoVo and SW480 cells (correlation coefficients were -0.9046 and -0.9236, respectively). MiR-200c overexpression inhibited the proliferation, migration and invasion of LoVo cells by downregulating FUT4. The Ki67-positive cells and Wnt/β-catenin signalling pathway-related proteins were reduced in the miR-200c overexpression and FUT4 silencing groups. A bioinformatics analysis and a dual luciferase reporting system identified FUT4 as the target of miR-200c. ConclusionsIn conclusion, miR-200c overexpression inhibits FUT4 expression and downregulates the Wnt/β-catenin signalling pathway, thereby inhibiting the migration, invasion and proliferation of colon cancer cells.

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Identification and Validation of a Prognostic Gene Signature for Diffuse Large B-Cell Lymphoma Based on Tumor Microenvironment-Related Genes

Frontiers in Oncology ◽

10.3389/fonc.2021.614211 ◽

2021 ◽

Vol 11 ◽

Author(s):

Tao Pan ◽

Yizi He ◽

Huan Chen ◽

Junfei Pei ◽

Yajun Li ◽

...

Keyword(s):

Tumor Microenvironment ◽

B Cell ◽

Cox Regression ◽

Cell Lymphoma ◽

B Cell Lymphoma ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Large B Cell Lymphoma ◽

Prognostic Prediction ◽

Large B Cell

Diffuse large B-cell lymphoma (DLBCL) is an extremely heterogeneous tumor entity, which makes prognostic prediction challenging. The tumor microenvironment (TME) has a crucial role in fostering and restraining tumor development. Consequently, we performed a systematic investigation of the TME and genetic factors associated with DLBCL to identify prognostic biomarkers for DLBCL. Data for a total of 1,084 DLBCL patients from the Gene Expression Omnibus database were included in this study, and patients were divided into a training group, an internal validation group, and two external validation groups. We calculated the abundance of immune–stromal components of DLBCL and found that they were related to tumor prognosis and progression. Then, differentially expressed genes were obtained based on immune and stromal scores, and prognostic TME‐related genes were further identified using a protein–protein interaction network and univariate Cox regression analysis. These genes were analyzed by the least absolute shrinkage and selection operator Cox regression model to establish a seven-gene signature, comprising TIMP2, QKI, LCP2, LAMP2, ITGAM, CSF3R, and AAK1. The signature was shown to have critical prognostic value in the training and validation sets and was also confirmed to be an independent prognostic factor. Subgroup analysis also indicated the robust prognostic ability of the signature. A nomogram integrating the seven-gene signature and components of the International Prognostic Index was shown to have value for prognostic prediction. Gene set enrichment analysis between risk groups demonstrated that immune-related pathways were enriched in the low-risk group. In conclusion, a novel and reliable TME relevant gene signature was proposed and shown to be capable of predicting the survival of DLBCL patients at high risk of poor survival.

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Identification of a Hypoxia-Related Molecular Classification and Hypoxic Tumor Microenvironment Signature for Predicting the Prognosis of Patients with Triple-Negative Breast Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.700062 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiaoli Sun ◽

Huan Luo ◽

Chenbo Han ◽

Yu Zhang ◽

Cunli Yan

Keyword(s):

Breast Cancer ◽

Tumor Microenvironment ◽

Cell Lines ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

Molecular Classification ◽

Hypoxic Tumor ◽

Score Model ◽

Prognostic Prediction

PurposeThe hypoxic tumor microenvironment was reported to be involved in different tumorigenesis mechanisms of triple-negative breast cancer (TNBC), such as invasion, immune evasion, chemoresistance, and metastasis. However, a systematic analysis of the prognostic prediction models based on multiple hypoxia-related genes (HRGs) has not been established in TNBC before in the literature. We aimed to develop and verify a hypoxia gene signature for prognostic prediction in TNBC patients.MethodsThe RNA sequencing profiles and clinical data of TNBC patients were generated from the TCGA, GSE103091, and METABRIC databases. The TNBC-specific differential HRGs (dHRGs) were obtained from differential expression analysis of hypoxia cultured TNBC cell lines compared with normoxic cell lines from the GEO database. Non-negative matrix factorization (NMF) method was then performed on the TNBC patients using the dHRGs to explore a novel molecular classification on the basis of the dHRG expression patterns. Prognosis-associated dHRGs were identified by univariate and multivariate Cox regression analysis to establish the prognostic risk score model.ResultsBased on the expressions of 205 dHRGs, all the patients in the TCGA training cohort were categorized into two subgroups, and the patients in Cluster 1 demonstrated worse OS than those in Cluster 2, which was validated in two independent cohorts. Additionally, the effects of somatic copy number variation (SCNV), somatic single nucleotide variation (SSNV), and methylation level on the expressions of dHRGs were also analyzed. Then, we performed Cox regression analyses to construct an HRG-based risk score model (3-gene dHRG signature), which could reliably discriminate the overall survival (OS) of high-risk and low-risk patients in TCGA, GSE103091, METABRIC, and BMCHH (qRT-PCR) cohorts.ConclusionsIn this study, a robust predictive signature was developed for patients with TNBC, indicating that the 3-gene dHRG model might serve as a potential prognostic biomarker for TNBC.

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Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.655855 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jing Jia ◽

Yuhan Dai ◽

Qing Zhang ◽

Peiyu Tang ◽

Qiang Fu ◽

...

Keyword(s):

Colon Cancer ◽

Overall Survival ◽

Prediction Model ◽

Differentially Expressed Genes ◽

Cox Regression ◽

Gene Signature ◽

Differentially Expressed ◽

Model Based ◽

Auc Value ◽

Prognostic Prediction

BackgroundGrowing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.MethodsStromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.ResultsLow stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).ConclusionThe well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

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Pyroptosis-Mediated Molecular Subtypes and Tumor Microenvironment Infiltration Characterization in Colon Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.766503 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jiawei Rao ◽

Wen Li ◽

Chuangqi Chen

Keyword(s):

Colon Cancer ◽

T Cells ◽

Tumor Microenvironment ◽

Cancer Progression ◽

Immune Cells ◽

Cox Regression ◽

Molecular Subtypes ◽

Cytotoxic T Cells ◽

Growth Factor Receptor ◽

Molecular Patterns

The role of pyroptosis, which is also a kind of cell-intrinsic death mechanism, in tumorigenesis and cancer progression has been revolutionized. However, the expression of pyroptosis-related genes (PYGs) in colon cancer (CC) and their prognostic value remain unclear. In this study, we comprehensively identified two PYG-mediated molecular subtypes with a distinct tumor microenvironment (TME) in 1,415 CC samples, which were based on 10 PYGs. The six-gene signature (pyroptosis score, PY-score) was constructed to quantify the molecular patterns of individual tumors using a least absolute shrinkage and selection operator (LASSO)–Cox regression model through the differentially expressed genes between the two molecular subtypes. Significant infiltration of activated immune cells (such as M1 macrophages and cytotoxic T cells) was observed in the low PY-score group, while naive and suppressive immune cells (such as naive CD8+ T cells and M2 macrophages) dominated in the high PY-score group. CC patients in the low PY-score group showed not only significant survival advantage but also sensitivity to immune checkpoint inhibitor treatment, anti-epidermal growth factor receptor (EGFR) therapy, and chemotherapy. Overall, this work revealed that the PYGs played a vital role in the formation of heterogeneity in the TME. The analysis of the PYG-mediated molecular patterns helps in understanding the characterization of TME infiltration and provides insights into more effective therapeutic strategies.

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Development and validation of a prognostic nomogram for early-onset colon cancer

Bioscience Reports ◽

10.1042/bsr20181781 ◽

2019 ◽

Vol 39 (6) ◽

Cited By ~ 5

Author(s):

Chaoran Yu ◽

Yujie Zhang

Keyword(s):

Colon Cancer ◽

Early Onset ◽

Regression Models ◽

Cox Regression ◽

Seer Database ◽

Tumor Node Metastasis ◽

Cancer Specific Survival ◽

Discrimination Power ◽

Prognostic Prediction ◽

Development And Validation

AbstractThe present study was to develop a prognostic nomogram to predict overall survival (OS) and cancer-specific survival (CSS) in early-onset colon cancer (COCA, age < 50). Patients diagnosed as COCA between 2004 and 2015 were retrieved from the surveillance, epidemiology, and end results (SEER) database. All included patients were assigned into training and validation sets. Univariate and multivariate analysis were used to identify independent prognostic variables for the construction of nomogram. The discrimination and calibration plots were used to measure the accuracy of the nomogram. A total of 11220 patients were included from the SEER database. The nomograms were established based on the variables significantly associated with OS and CSS using cox regression models. Calibration plots indicated that both nomograms in OS and CSS exhibited high correlation to actual observed results. The nomograms also displayed improved discrimination power than tumor-node-metastasis (TNM) stage and SEER stage both in the training and validation sets. The monograms established in the present study provided an alternative tool to both OS and CSS prognostic prediction compared with TNM and SEER stages.

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