Identification and development of a novel invasion-related gene signature for prognosis prediction in colon adenocarcinoma

High expression levels of S1PR3 and PDGFRB indicates unfavorable clinical outcomes in colon adenocarcinoma

Background

Studies verified that sphingosine kinase 1 (SPHK1)/sphingosine 1-phosphate receptors (S1PRs) and platelet-derived growth factor receptors (PDGFRs) play important roles in tumor occurrence and progression. However, the expression and clinical value of SPHK1/S1PRs and PDGFRs in colon adenocarcinoma (COAD) remains unclear. This study aimed to explore the expression of SPHK1/S1PRs and PDGFRs in COAD and further investigate their roles in predicting the prognosis of patients with COAD.

Methods

SPHK1/S1PRs and PDGFRs expression in tissues from patient with COAD were analyzed using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Kaplan-Meier survival analysis was used to evaluate the prognostic roles of SPHK1/S1PRs and PDGFRs in patients with COAD. Spearman's correlation analysis was performed to assess the relationship between SPHK1/S1PRs and PDGFRs in COAD. Then, χ2 test was performed to analyze the correlation between SPHK1/S1PR3/PDGFRB and clinicopathological characteristics of the patients. Additionally, possible signaling pathways co-regulated by S1PR3 and PDGFRB were predicted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Least absolute shrinkage and selection operator (LASSO) regression was used to identify hub genes that co-regulated S1PR3 and PDGFRB expression. A prognostic model based on hub genes was constructed for patients with COPD. Finally, the relationship between the hub genes and tumor immune cell infiltration was investigated.

Results

The expression levels of SPHK1 and PDGFRB were significantly upregulated in COAD patient tissues (P < 0.001 and P < 0.001, respectively). Moreover, Kaplan-Meier analysis showed that patients with COAD with high expression levels of SPHK1 and S1PR3 had shorter overall survival (OS) than those with low expression levels (P = 0.013 and P = 0.005, respectively). Spearman's correlation analysis verified a strong positive correlation (P < 0.001, r = 0.790) between the expression of S1PR3 and PDGFRB. In addition, we found that high SPHK1 and PDGGRB expression levels were associated with perineural invasion (P < 0.001 and P = 0.011, respectively). High expression of S1PR3 and PDGGRB was prominently associated with N stage (P = 0.002 and P = 0.021, respectively). High levels of SPHK1, S1PR3, and PDGFRB were associated with lymph node invasion. (P = 0.018, P = 0.004, and P = 0.001, respectively). GO and KEGG results revealed that S1PR3 and PDGFRB may participate in COAD cell extracellular matrix organization and cellular signal transduction. Five hub genes, SFRP2, GPRC5B, RSPO3, FGF14, and TCF7L1, were identified using LASSO regression. Survival analysis showed that the OS in the high-risk group was remarkably shorter than that in the low-risk group. The results indicated that tumor immune cells were significantly increased in the high-risk group compared to those in the low-risk group.

Conclusions

S1PR3 and PDGFRB may be important markers for predicting lymphatic metastasis and poor prognosis in patients with COAD. The underlying mechanisms may involve immune cell infiltration.

The Potential Significance of the EMILIN3 Gene in Augmenting the Aggressiveness of Low-Grade Gliomas is Noteworthy

Purpose

Low-grade gliomas (LGG) are common brain tumors with high mortality rates. Cancer cell invasion is a significant factor in tumor metastasis. Novel biomarkers are urgently needed to predict LGG prognosis effectively.

Methods

The data for LGG were obtained from the Bioinformatics database. A consensus clustering analysis was performed to identify molecular subtypes linked with invasion in LGG. Differential expression analysis was performed to identify differentially expressed genes (DEGs) between the identified clusters. Enrichment analyses were then conducted to explore the function for DEGs. Prognostic signatures were placed, and their predictive power was assessed. Furthermore, the invasion-related prognostic signature was validated using the CGGA dataset. Subsequently, clinical specimens were procured in order to validate the expression levels of the distinct genes examined in this research, and to further explore the impact of these genes on the glioma cell line LN229 and HS-683.

Results

Two invasion-related molecular subtypes of LGG were identified, and we sifted 163 DEGs between them. The enrichment analyses indicated that DEGs are mainly related to pattern specification process. Subsequently, 10 signature genes (IGF2BP2, SRY, CHI3L1, IGF2BP3, MEOX2, ABCC3, HOXC4, OTP, METTL7B, and EMILIN3) were sifted out to construct a risk model. Besides, the survival (OS) in the high-risk group was lower. The performance of the risk model was verified. Furthermore, a highly reliable nomogram was generated. Cellular experiments revealed the ability to promote cell viability, value-addedness, migratory ability, invasive ability, and colony-forming ability of the glioma cell line LN229 and HS-683. The qRT-PCR analysis of clinical glioma samples showed that these 10 genes were expressed at higher levels in high-grade gliomas than in low-grade gliomas, suggesting that these genes are associated with poor prognosis of gliomas.

Conclusion

Our study sifted out ten invasion-related biomarkers of LGG, providing a reference for treatments and prognostic prediction in LGG.

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

Identification and validation of regulatory T cell-associated gene signatures to predict colon adenocarcinoma prognosis

Colon adenocarcinoma (COAD) is a common cause of cancer-related death. Due to the difficulty in early diagnosis and drug resistance, conventional treatments are difficult to be effective. Some studies have found that the functional recovery of T cells in the tumor microenvironment, especially regulatory T cells (Tregs), plays an important role in the progression of cancer. This study used the TCGA data set, clinical information and RNA-seq data of COAD patients to construct a Tregs-related risk score (TRS) through methods such as WGCNA, single-factor Cox, multi-factor Cox and random survival forest (RSF). Moreover, we also used the TCGA test set and internal validation set to verify the predictive ability of TRS, and used functional enrichment analysis and somatic mutation analysis to mine genes related to TRS, such as like thrombin/trypsin receptor 2 (F2RL2), inhibin subunit beta B (INHBB) and melanoma antigen family A12 (MAGEA12). Moreover, this study confirmed the expression of these prognostic genes using scRNA-seq data. We also performed qPCR analysis of various genes in normal and cancerous colon cancer cell lines to verify that these genes indeed play a role in CODA patients. We also constructed a mouse CODA model to study and evaluate the impact of key genes such as MAGEA12 on tumor growth in mice. This study explores the important role of Treg cells in the prognosis of COAD and discovers some potential biomarkers for the occurrence and development of COAD, which provides some new ideas for the treatment of COAD.

Comparison of left- and right-sided colorectal cancer to explore prognostic signatures related to pyroptosis

Background

Colorectal cancer (CRC) is one of the most common malignancies, and pyroptosis exerts an immunoregulatory role in CRC. Although the location of the primary tumor is a prognostic factor for patients with CRC, the mechanisms of pyroptosis in left- and right-sided CRC remain unclear.

Methods

Expression and clinical data were collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Differences in clinical characteristics, immune cell infiltration, and somatic mutations between left- and right-sided CRC were then compared. After screening for differentially expressed genes, Pearson correlation analysis was performed to select pyroptosis-related genes, followed by a gene set enrichment analysis. Univariate and multivariate Cox regression analyses were used to construct and validate the prognostic model and nomogram for predicting prognosis. Collected left- and right-sided CRC samples were subjected to reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to validate the expression of key pyroptosis-related genes.

Results

Left- and right-sided CRC exhibited significant differences in clinical features and immune cell infiltration. Five prognostic signatures were identified from among 134 pyroptosis-related differentially expressed genes to construct a risk score-based prognostic model, and adverse outcomes for high-risk patients were further verified using an external cohort. A nomogram was also generated based on three independent prognostic factors to predict survival probabilities, while calibration curves confirmed the consistency between the predicted and actual survival. Experiment data confirmed the significant differential expression of five genes between left- and right-sided CRC.

Conclusion

The five identified pyroptosis-related gene signatures may be potential biomarkers for predicting prognosis in left- and right-sided CRC and may help improve the clinical outcomes of patients with CRC.

Identification of a novel lymphangiogenesis signature associated with immune cell infiltration in colorectal cancer based on bioinformatics analysis

BACKGROUND

Lymphangiogenesis plays an important role in tumor progression and is significantly associated with tumor immune infiltration. However, the role and mechanisms of lymphangiogenesis in colorectal cancer (CRC) are still unknown. Thus, the objective is to identify the lymphangiogenesis-related genes associated with immune infiltration and investigation of their prognosis value.

METHODS

mRNA expression profiles and corresponding clinical information of CRC samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The lymphangiogenesis-related genes (LymRGs) were collected from the Molecular Signatures database (MSigDB). Lymphangiogenesis score (LymScore) and immune cell infiltrating levels were quantified using ssGSEA. LymScore) and immune cell infiltrating levels-related hub genes were identified using weighted gene co-expression network analysis (WGCNA). Univariate Cox and LASSO regression analyses were performed to identify the prognostic gene signature and construct a risk model. Furthermore, a predictive nomogram was constructed based on the independent risk factor generated from a multivariate Cox model.

RESULTS

A total of 1076 LymScore and immune cell infiltrating levels-related hub genes from three key modules were identified by WGCNA. Lymscore is positively associated with natural killer cells as well as regulator T cells infiltrating. These modular genes were enriched in extracellular matrix and structure, collagen fibril organization, cell-substrate adhesion, etc. NUMBL, TSPAN11, PHF21A, PDGFRA, ZNF385A, and RIMKLB were eventually identified as the prognostic gene signature in CRC. And patients were divided into high-risk and low-risk groups based on the median risk score, the patients in the high-risk group indicated poor survival and were predisposed to metastasis and advanced stages. NUMBL and PHF21A were upregulated but PDGFRA was downregulated in tumor samples compared with normal samples in the Human Protein Atlas (HPA) database.

CONCLUSION

Our finding highlights the critical role of lymphangiogenesis in CRC progression and metastasis and provides a novel gene signature for CRC and novel therapeutic strategies for anti-lymphangiogenic therapies in CRC.

Construction and validation of a prognostic nine-gene signature associated with radiosensitivity in head and neck squamous cell carcinoma

Background

Radiotherapy is an effective treatment for head and neck squamous cell carcinoma (HNSCC), however how to predict the prognosis is not clear.

Methods

Here we collected 262 radiosensitivity-associated genes, screened and constructed a prognostic nine-gene risk model through univariate COX, lasso regression, stepwise regression and multivariate COX analysis for transcriptome and clinical information of HNSCC patients obtained from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases.

Results

The reliability and robustness of the risk model were verified by receiver operating characteristic (ROC) curves, risk maps, and Kaplan-Meier (KM) curves analysis. Differences in immune cell infiltration and immune-related pathway enrichment between high-risk and low-risk subgroups were determined by multiple immune infiltration analyses. Meanwhile, the mutation map and the responses to immunotherapy were also differentiated by the prognostic nine-gene signature associated with radiosensitivity. These nine genes expression in HNSCC was verified in the Human Protein Atlas (HPA) database. After that, these nine genes expression was verified to be related to radiation resistance through in-vitro cell experiments.

Conclusions

All results showed that the nine-gene signature associated with radiosensitivity is a potential prognostic indicator for HNSCC patients after radiotherapy and provides potential gene targets for enhancing the efficacy of radiotherapy.

Genetic Variants as Predictors of the Success of Colorectal Cancer Treatments

BACKGROUND

Some genetic polymorphisms (SNPs) have been proposed as predictors for different colorectal cancer (CRC) outcomes. This work aims to assess their performance in our cohort and find new SNPs associated with them.

METHODS

A total of 833 CRC cases were analyzed for seven outcomes, including the use of chemotherapy, and stratified by tumor location and stage. The performance of 63 SNPs was assessed using a generalized linear model and area under the receiver operating characteristic curve, and local SNPs were detected using logistic regressions.

RESULTS

In total 26 of the SNPs showed an AUC > 0.6 and a significant association (p < 0.05) with one or more outcomes. However, clinical variables outperformed some of them, and the combination of genetic and clinical data showed better performance. In addition, 49 suggestive (p < 5 × 10-6) SNPs associated with one or more CRC outcomes were detected, and those SNPs were located at or near genes involved in biological mechanisms associated with CRC.

CONCLUSIONS

Some SNPs with clinical data can be used in our population as predictors of some CRC outcomes, and the local SNPs detected in our study could be feasible markers that need further validation as predictors.

Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock

Biological age, distinct from an individual's chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals' chronological age. Our retinal aging clocking, 'eyeAge', predicted chronological age more accurately than other aging clocks (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UK Biobank, respectively). Additionally, eyeAge was independent of blood marker-based measures of biological age, maintaining an all-cause mortality hazard ratio of 1.026 even when adjusted for phenotypic age. The individual-specific nature of eyeAge was reinforced via multiple GWAS hits in the UK Biobank cohort. The top GWAS locus was further validated via knockdown of the fly homolog, Alk, which slowed age-related decline in vision in flies. This study demonstrates the potential utility of a retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, opening avenues for quick and actionable evaluation of gero-protective therapeutics.

Identification Invasion-Related Long Non-Coding RNAs in Lung Adenocarcinoma and Analysis of Competitive Endogenous RNA Regulatory Networks

Background

Cell invasion plays a vital role in cancer development and progression. Aberrant expression of long non-coding RNAs (lncRNAs) is also critical in carcinogenesis. However, the prognostic value of invasion-related lncRNAs in lung adenocarcinoma (LUAD) remains unknown.

Methods

Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and microRNAs (DEmiRNAs) were between LUAD and control samples. Pearson correlation analyses were performed to screen for invasion-related DElncRNAs (DEIRLs). Univariate and multivariate Cox regression algorithms were applied to identify key genes and construct the risk score model, which was evaluated using receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA) was used to explore the underlying pathways of the risk model. Moreover, an invasion-related competitive endogenous RNA (ceRNA) regulatory network was constructed. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to detect the expression of prognostic lncRNAs in the LUAD and control samples.

Results

A total of 45 DElncRNAs were identified as DEIRLs. RP3-525N10.2, LINC00857, EP300-AS1, PDZRN3-AS1, and RP5-1102E8.3 were potential prognostic lncRNAs, the expression of which was verified by RT-qPCR in LUAD samples. Both the risk score model and nomogram used the prognostic lncRNAs. ROC curves showed the risk score model had moderate accuracy and the nomogram had high accuracy in predicting patient prognosis. GSEA results indicated that the risk score model was associated with many biological processes and pathways relevant to cell proliferation. A ceRNA regulatory network was constructed in which PDZRN3-miR-96-5p-CPEB1, EP300-AS1-miR-93-5p-CORO2B, and RP3-525N10.2-miR-130a-5p-GHR may be key invasion-related regulatory pathways in LUAD.

Conclusion

Our study identified five novel invasion-related prognostic lncRNAs (RP3-525N10.2, LINC00857, EP300-AS1, PDZRN3-AS1, and RP5-1102E8.3) and established an accurate model for predicting the prognosis of patients with LUAD. These findings enrich our understanding of the relationships between cell invasion, lncRNAs, and LUAD and may provide novel treatment directions.

Comprehensive Characterization of RNA-Binding Proteins in Colon Adenocarcinoma Identifies a Novel Prognostic Signature for Predicting Clinical Outcomes and Immunotherapy Responses Based on Machine Learning

BACKGROUND

RNA-binding proteins (RBPs) are crucial factors that function in the posttranscriptional modification process and are significant in cancer.

OBJECTIVE

This research aimed for a multigene signature to predict the prognosis and immunotherapy response of patients with colon adenocarcinoma (COAD) based on the expression profile of RNA-binding proteins (RBPs).

METHODS

COAD samples retrieved from the TCGA and GEO datasets were utilized for a training dataset and a validation dataset. Totally, 14 shared RBP genes with prognostic significance were identified. Non-negative matrix factorization clusters defined by these RBPs could stratify COAD patients into two molecular subtypes. Cox regression analysis and identification of 8-gene signature categorized COAD patients into high- and low-risk populations with significantly different prognosis and immunotherapy responses.

RESULTS

Our prediction signature was superior to another five well-established prediction models. A nomogram was generated to quantificationally predict the overall survival (OS) rate, validated by calibration curves. Our findings also indicated that high-risk populations possessed an enhanced immune evasion capacity and low-risk populations might benefit immunotherapy, especially for the joint combination of PD-1 and CTLA4 immunosuppressants. DHX15 and LARS2 were detected with significantly different expressions in both datasets, which were further confirmed by qRTPCR and immunohistochemical staining.

CONCLUSION

Our observations supported an eight-RBP-related signature that could be applied for survival prediction and immunotherapy response of patients with COAD.

A Novel Pyroptosis-Related Gene Signature for Predicting the Prognosis and the Associated Immune Infiltration in Colon Adenocarcinoma

Background

Pyroptosis has been demonstrated to be an inflammatory form of programmed cell death recently. However, the expression of pyroptosis-related genes (PRGs) in colon adenocarcinoma (COAD) and their correlations with prognosis remain unclear.

Methods

Data of COAD patients were obtained from The Cancer Genome Atlas (TCGA) database to screen differentially expressed genes (DEGs). Univariate Cox regression analysis and the LASSO Cox regression analysis were applied to construct a gene signature. All COAD patients in TCGA cohort were separated into low-risk subgroup or high-risk subgroup via the risk score. Kaplan–Meier survival analysis and receiver operator characteristic (ROC) curves were adopted to assess its prognostic efficiency. COAD data from the GSE17537 datasets was used for validation. A prognostic nomogram was established to predict individual survival. The correlation between PRGs and immune cell infiltration in COAD was verified based on TIMER database. CIBERSORT analysis was utilized on risk subgroup as defined by model. The protein and mRNA expression level of PRGs were verified by HPA database and qPCR.

Results

A total of 51 differentially expressed PRGs were identified in TCGA cohort. Through univariate Cox regression analysis and LASSO Cox regression analysis, a prognostic model containing 7 PRGs was constructed. Kaplan–Meier survival analysis indicated that patients in the low-risk subgroup exhibited better prognosis compared to those in the high-risk subgroup. Additionally, the area under the curve (AUC) of ROC is 0.60, 0.63, and 0.73 for 1-, 3-, and 5-year survival in TCGA cohort and 0.63, 0.65, and 0.64 in validation set. TIMER database showed a strong correlation between 7 PRGs and tumor microenvironment in COAD. Moreover, CIBERSORT showed significant differences in the infiltration of plasma cells, M0 macrophages, resting dendritic cells, and eosinophils between low-risk subgroup and high-risk subgroup. HPA database showed that protein expression level of SDHB, GZMA, BTK, EEF2K, and NR1H2 was higher in normal tissues. And the transcriptional level of CASP5, BTK, SDHB, GZMA, and RIPK3 was high in normal tissues.

Conclusions

Our study identified a novel PRGs signature that could be used to predict the prognosis of COAD patients, which might provide a new therapeutic target for the treatment of COAD patients.

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma

Background

A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA).

Methods

From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan-Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built.

Results

Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability.

Conclusions

The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

Multi-Omics Analysis for Transcriptional Regulation of Immune-Related Targets Using Epigenetic Data: A New Research Direction

Background

Currently, a comprehensive method for exploration of transcriptional regulation has not been well established. We explored a novel pipeline to analyze transcriptional regulation using co-analysis of RNA sequencing (RNA-seq), assay for transposase-accessible chromatin using sequencing (ATAC-seq), and chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq).

Methods

The G protein-coupled receptors (GPCRs) possibly associated with macrophages were further filtered using a reduced-Cox regression model. ATAC-seq profiles were used to map the chromatin accessibility of the GPRC5B promoter region. Pearson analysis was performed to identify the transcription factor (TF) whose expression was correlated with open chromatin regions of GPRC5B promoter. ChIP-seq profiles were obtained to confirm the physical binding of GATA4 and its predicted binding regions. For verification, quantitative polymerase chain reaction (qPCR) and multidimensional database validations were performed.

Results

The reduced-Cox regression model revealed the prognostic value of GPRC5B. A novel pipeline for TF exploration was proposed. With our novel pipeline, we first identified chr16:19884686-19885185 as a reproducible open chromatin region in the GPRC5B promoter. Thereafter, we confirmed the correlation between GATA4 expression and the accessibility of this region, confirmed its physical binding, and proved in vitro how its overexpression could regulate GPRC5B. GPRC5B was significantly downregulated in colon adenocarcinoma (COAD) as seen in 28 patient samples. The correlation between GPRC5B and macrophages in COAD was validated using multiple databases.

Conclusion

GPRC5B, correlated with macrophages, was a key GPCR affecting COAD prognosis. Further, with our novel pipeline, TF GATA4 was identified as a direct upstream of GPRC5B. This study proposed a novel pipeline for TF exploration and provided a theoretical basis for COAD therapy.

Identification of Ferroptosis-Related Genes Signature Predicting the Efficiency of Invasion and Metastasis Ability in Colon Adenocarcinoma

Background: Colon adenocarcinoma (COAD) is one of the most prevalent cancers worldwide and has become a leading cause of cancer death. Although many potential biomarkers of COAD have been screened with the bioinformatics method, it is necessary to explore novel markers for the diagnosis and appropriate individual treatments for COAD patients due to the high heterogeneity of this disease. Epithelial-to-mesenchymal transition (EMT)-mediated tumor metastasis suggests poor prognosis of cancers. Ferroptosis is involved in tumor development. EMT signaling can increase the cellular sensitivity to ferroptosis in tumors. The aim of our study is finding novel prognostic biomarkers to determine COAD patients for predicting efficiency of metastasis status and targeting precise ferroptosis-related therapy.

Methods: A novel gene signature related to metastasis and ferroptosis was identified combing with risk model and WGCNA analysis with R software. The biological functions and predictive ability of the signature in COAD were explored through bioinformatics analysis.

Results: We established a four-gene prognostic signature (MMP7, YAP1, PCOLCE, and HOXC11) based on EMT and ferroptosis related genes and validated the reliability and effectiveness of this model in COAD. This four-gene prognostic signature was closely connected with metastasis and ferroptosis sensitivity of COAD. Moreover, WGCNA analysis further confirmed the correlation between PCOLCE, HOXC11, and liver and lymphatic invasion of COAD.

Conclusion: The four genes may become potential prognostic biomarkers to identify COAD patients with metastasis. Moreover, this four-gene signature may be able to determine the COAD suitable with ferroptosis induction therapy. Finally, PCOLCE2 and HOXC11 were selected individually because of their novelties and precise prediction ability. Overall, this signature provided novel possibilities for better prognostic evaluation of COAD patients and may be of great guiding significance for individualized treatment and clinical decision.

Identification of a 3-gene signature based on differentially expressed invasion genes related to cancer molecular subtypes to predict the prognosis of osteosarcoma patients

Invasion is a critical pathway leading to tumor metastasis. This study constructed an invasion-related polygenic signature to predict osteosarcoma prognosis. We initially determined two molecular subtypes of osteosarcoma, Cluster1 (C1) and Cluster2 (C2).. A 3 invasive-gene signature was established by univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis of the differentially expressed genes (DEGs) between the two subtypes, and was validated in internal and two external data sets (GSE21257 and GSE39058). Patients were divided into high- and low-risk groups by their signature, and the prognosis of osteosarcoma patients in the high-risk group was poor. Based on the time-independent receiver operating characteristic (ROC) curve, the area under the curve (AUC) for 1-year and 2-year OS were higher than 0.75 in internal and external cohorts. This signature also showed a high accuracy and independence in predicting osteosarcoma prognosis and a higher AUC in predicting 1-year osteosarcoma survival than other four existing models. In a word, a 3 invasive gene-based signature was developed, showing a high performance in predicting osteosarcoma prognosis. This signature could facilitate clinical prognostic analysis of osteosarcoma.

Identification and Validation of a Novel Inflammatory Response-Related Gene Signature for the Prognosis of Colon Cancer

Purpose

The inflammatory response plays a crucial role in the occurrence and development of colon cancer. In this study, we aimed to explore a novel prognostic model for patients with colon cancer (COAD) based on inflammatory response-related genes.

Methods

Inflammatory response-related genes were obtained from Molecular Signatures database. Univariate and multivariate Cox regression analyses were used for model construction based on TCGA dataset. GSE39582 dataset and qRT-PCR dataset were used for validation. Gene set variation analysis and gene set enrichment analysis were performed to explore the potential regulatory pathways. The immune cell infiltration level was analyzed via CIBERSORT. Immunohistochemistry analysis and experiments were used to explore the function of genes in model.

Results

In this study, a novel prognostic signature was identified using stepwise Cox proportional hazards regression analysis based on TCGA dataset. The results were subsequently validated in 562 patients from GSE39582 and a qRT-PCR data set from 70 tumor samples. Functional analysis indicated that the tumor microenvironment and immune cell infiltrate were different between high- and low-risk groups. Additionally, IHC results showed that the protein levels of prognostic genes were significantly different between COAD tissues and adjacent non-tumorous tissues, and prognostic genes could regulate the malignant phenotype of COAD cells.

Conclusion

Overall, the inflammation-related gene signature can be used for prognostic prediction in patients with COAD.

Glycosyltransferase B4GALNT2 as a Predictor of Good Prognosis in Colon Cancer: Lessons from Databases

BACKGROUND

glycosyltransferase B4GALNT2 and its cognate carbohydrate antigen Sd^a are highly expressed in normal colon but strongly downregulated in colorectal carcinoma (CRC). We previously showed that CRC patients expressing higher B4GALNT2 mRNA levels displayed longer survival. Forced B4GALNT2 expression reduced the malignancy and stemness of colon cancer cells.

METHODS

Kaplan-Meier survival curves were determined in "The Cancer Genome Atlas" (TCGA) COAD cohort for several glycosyltransferases, oncogenes, and tumor suppressor genes. Whole expression data of coding genes as well as miRNA and methylation data for B4GALNT2 were downloaded from TCGA.

RESULTS

the prognostic potential of B4GALNT2 was the best among the glycosyltransferases tested and better than that of many oncogenes and tumor suppressor genes; high B4GALNT2 expression was associated with a lower malignancy gene expression profile; differential methylation of an intronic B4GALNT2 gene position and miR-204-5p expression play major roles in B4GALNT2 regulation.

CONCLUSIONS

high B4GALNT2 expression is a strong predictor of good prognosis in CRC as a part of a wider molecular signature that includes ZG16, ITLN1, BEST2, and GUCA2B. Differential DNA methylation and miRNA expression contribute to regulating B4GALNT2 expression during colorectal carcinogenesis.