Identification of a five‐gene signature with prognostic value in colorectal cancer

An integrated framework for prognosis prediction and drug response modeling in colorectal liver metastasis drug discovery

BACKGROUND

Colorectal cancer (CRC) is the third most prevalent cancer globally, and liver metastasis (CRLM) is the primary cause of death. Hence, it is essential to discover novel prognostic biomarkers and therapeutic drugs for CRLM.

METHODS

This study developed two liver metastasis-associated prognostic signatures based on differentially expressed genes (DEGs) in CRLM. Additionally, we employed an interpretable deep learning model utilizing drug sensitivity databases to identify potential therapeutic drugs for high-risk CRLM patients. Subsequently, in vitro and in vivo experiments were performed to verify the efficacy of these compounds.

RESULTS

These two prognostic models exhibited superior performance compared to previously reported ones. Obatoclax, a BCL-2 inhibitor, showed significant differential responses between high and low risk groups classified by prognostic models, and demonstrated remarkable effectiveness in both Transwell assay and CT26 colorectal liver metastasis mouse model.

CONCLUSIONS

This study highlights the significance of developing specialized prognostication approaches and investigating effective therapeutic drugs for patients with CRLM. The application of a deep learning drug response model provides a new drug discovery strategy for translational medicine in precision oncology.

Pyroptosis-Derived Long Noncoding RNA Profiles Reveal a Novel Signature for Evaluating the Prognosis of Patients With Lung Adenocarcinoma

PURPOSE

Long noncoding RNAs (lncRNAs) were recently implicated in modifying pyroptosis. Nonetheless, pyroptosis-related lncRNAs and their possible clinical relevance persist largely uninvestigated in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

A sum of 921 samples were collected from three independent data sets. We obtained pyroptosis-related genes from both the Molecular Signatures Database and relevant literature sources and used four machine learning techniques, comprising stepwise Cox, ridge regression, least absolute shrinkage and selection operator, and random forest. Multiple bioinformatics approaches were used to further investigate the underlying mechanisms.

RESULTS

In total, 39 differentially expressed pyroptosis genes were identified by comparing normal and tumor samples. Correlation analysis revealed 933 pyroptosis-related lncRNAs. Furthermore, univariate Cox regression determined 11 lncRNAs that exhibited stable associations with prognosis in the three cohorts, which were used to construct the pyroptosis-derived lncRNA signature. After analyzing the optimal results from four machine learning algorithms, we ultimately selected random forest to develop the pyroptosis-derived lncRNA signature. This signature was proven to be an independent prognostic factor and exhibited robust performance in three cohorts.

CONCLUSION

We provided novel insight and established a pyroptosis-derived lncRNA signature for patients with LUAD, exhibiting strong predictive capabilities in both the training and validation sets.

HIF-1α signaling: Essential roles in tumorigenesis and implications in targeted therapies

The hypoxic microenvironment is an essential characteristic of most malignant tumors. Notably, hypoxia-inducible factor-1 alpha (HIF-1α) is a key regulatory factor of cellular adaptation to hypoxia, and many critical pathways are correlated with the biological activity of organisms via HIF-1α. In the intra-tumoral hypoxic environment, HIF-1α is highly expressed and contributes to the malignant progression of tumors, which in turn results in a poor prognosis in patients. Recently, it has been indicated that HIF-1α involves in various critical processes of life events and tumor development via regulating the expression of HIF-1α target genes, such as cell proliferation and apoptosis, angiogenesis, glucose metabolism, immune response, therapeutic resistance, etc. Apart from solid tumors, accumulating evidence has revealed that HIF-1α is also closely associated with the development and progression of hematological malignancies, such as leukemia, lymphoma, and multiple myeloma. Targeted inhibition of HIF-1α can facilitate an increased sensitivity of patients with malignancies to relevant therapeutic agents. In the review, we elaborated on the basic structure and biological functions of HIF-1α and summarized their current role in various malignancies. It is expected that they will have future potential for targeted therapy.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

SCG2: A Prognostic Marker That Pinpoints Chemotherapy and Immunotherapy in Colorectal Cancer

Background

Fluorouracil (FU)-based chemotherapy regimens are indispensable in the comprehensive treatment of colorectal cancer (CRC). However, the heterogeneity of treated individuals and the severe adverse effects of chemotherapy results in limited overall benefit.

Methods

Firstly, Weighted gene co-expression network analysis (WGCNA) identified modules tightly associated with chemotherapy response. Then, the in-house cohort and prognostic cohorts from TCGA and GEO were subjected to Cox proportional hazards model and survival analysis to ascertain the predictable function of SCG2 on the prognosis of CRC patients. Finally, we performed In vitro experiments, functional analysis, somatic mutation, and copy number variation research to explore the biological characteristics of SCG2.

Results

We identified red and green as the modules most associated with chemotherapy response, in which SCG2 was considered a risky factor with higher expression predicting poorer prognosis. SCG2 expression in the APC non-mutation group was remarkably higher than in the mutation group. The mutation frequencies of amplified genes differed significantly between different SCG2 expression subgroups. Besides, CRC cell lines with SCG2 knockdown have reduced invasive, proliferative, and proliferative capacity. We discovered that the SCG2 high expression subgroup was the immune hot type and considered more suitable for immunotherapy.

Conclusion

This study demonstrates the clinical significance and biological characteristics of SCG2, which could serve as a promising biomarker to identify patients who may benefit from chemotherapy and immunotherapy.

A Three-Genes Signature Predicting Colorectal Cancer Relapse Reveals LEMD1 Promoting CRC Cells Migration by RhoA/ROCK1 Signaling Pathway

Objective

Colorectal cancer (CRC) patients that experience early relapse consistently exhibit poor survival. However, no effective approach has been developed for the diagnosis and prognosis prediction of postoperative relapsed CRC.

Methods

Multiple datasets from the GEO database and TCGA database were utilized for bioinformatics analysis. WGCNA analyses and RRA analysis were performed to identify key genes. The COX/Lasso regression model was used to construct the recurrence model. Subsequent in vitro experiments further validated the potential role of the hub genes in CRC.

Results

A comprehensive analysis was performed on multiple CRC datasets and a CRC recurrence model was constructed containing LEMD1, SERPINE1, and SIAE. After further validation in two independent databases, we selected LEMD1 for in vitro experiments and found that LEMD1 could regulate CRC cell proliferation, migration, invasion, and promote EMT transition. The Rho-GTPase pulldown experiments further indicated that LEMD1 could affect RhoA activity and regulate cytoskeletal dynamics. Finally, we demonstrated that LEMD1 promoted CRC cell migration through the RhoA/ROCK1 signaling pathway.

Conclusions

In this study, a CRC relapse model consisting of LEMD1, SERPINE1, and SIAE was constructed by comprehensive analysis of multiple CRC datasets. LEMD1 could promote CRC cell migration through the RhoA/ROCK signaling pathway.

Identification of Angiogenesis-Related Prognostic Biomarkers Associated With Immune Cell Infiltration in Breast Cancer

Background: This study aimed to explore the prognostic value of angiogenesis-related genes (ARGs) and their association with immune cell infiltration (ICI) in breast cancer (BC).

Methods: Transcriptome data of BC were obtained from the TCGA and GEO databases. Differentially expressed ARGs were identified by the limma package. The identification of key genes and construction of the risk score model were performed by univariate and multivariate Cox regression algorithms. The prognostic value of the risk score was assessed by ROC curves and nomogram. GO, KEGG pathway, and GSEA were used to investigate the biological functions of differentially expressed genes (DEGs), and CIBERSORT, ssGSEA, and xCell algorithms were performed to estimate the ICI in high-risk and low-risk groups. The correlations between prognostic biomarkers and differentially distributed immune cells were assessed. Moreover, a ceRNA regulatory network based on prognostic biomarkers was constructed and visualized by Cytoscape software.

Results: A total of 18 differentially expressed ARGs were identified between tumor and adjacent normal tissue samples. TNFSF12, SCG2, COL4A3, and TNNI3 were identified as key prognostic genes by univariate and multivariate Cox regression analyses. The risk score model was further constructed based on the four-gene signature and validated in GSE7390 and GSE88770 datasets. ROC curves and nomogram indicated that the risk score had good accuracy for determining BC patient survival. Biological function analysis showed that DEGs in high- and low-risk groups had a high enrichment in immune-related biological processes and signaling pathways. Moreover, significantly different ICIs were found between high- and low-risk groups, such as memory B cells, CD8⁺ T cells, resting memory CD4⁺ T cells, follicular helper T cells, regulatory T cells, monocytes, M2 macrophages, and neutrophils, and each prognostic biomarker was significantly correlated with one or more immune cell types.

Conclusion: The current study identified novel prognostic ARGs and developed a prognostic model for predicting survival in patients with BC. Furthermore, this study indicated that ICI may act as a bond between angiogenesis and BC. These findings enhance our understanding of angiogenesis in BC and provide novel guidance on developing therapeutic targets for BC patients.

A glycolysis-related two-gene risk model that can effectively predict the prognosis of patients with rectal cancer

Background

Aerobic glycolysis is an emerging hallmark of cancer. Although some studies have constructed glycolysis-related prognostic models of colon adenocarcinoma (COAD) based on The Cancer Genome Atlas (TCGA) database, whether the COAD glycolysis-related prognostic model is appropriate for distinguishing the prognosis of rectal adenocarcinoma (READ) patients remains unknown. Exploring critical and specific glycolytic genes related to READ prognosis may help us discover new potential therapeutic targets for READ patients.

Results

Three gene sets, HALLMARK_GLYCOLYSIS, REACTOME_GLYCOLYSIS and REACTOME_REGULATION_OF_GLYCOLYSIS_BY_FRUCTOSE_2_6_BISPHOSPHATE_METABOLISM, were both significantly enriched in both COAD and READ through glycolysis-related gene set enrichment analysis (GSEA). We found that six genes (ANKZF1, STC2, SUCLG2P2, P4HA1, GPC1 and PCK1) were independent prognostic genes in COAD, while TSTA3 and PKP2 were independent prognostic genes in READ. Glycolysis-related prognostic model of COAD and READ was, respectively, constructed and assessed in COAD and READ. We found that the glycolysis-related prognostic model of COAD was not appropriate for READ, while glycolysis-related prognostic model of READ was more appropriate for READ than for COAD. PCA and t-SNE analysis confirmed that READ patients in two groups (high and low risk score groups) were distributed in discrete directions based on the glycolysis-related prognostic model of READ. We found that this model was an independent prognostic indicator through multivariate Cox analysis, and it still showed robust effectiveness in different age, gender, M stage, and TNM stage. A nomogram combining the risk model of READ with clinicopathological characteristics was established to provide oncologists with a practical tool to evaluate the rectal cancer outcomes. GO enrichment and KEGG analyses confirmed that differentially expressed genes (DEGs) were enriched in several glycolysis-related molecular functions or pathways based on glycolysis-related prognostic model of READ.

Conclusions

We found that a glycolysis-related prognostic model of COAD was not appropriate for READ, and we established a novel glycolysis-related two-gene risk model to effectively predict the prognosis of rectal cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00377-0.

Exploration and validation of a combined immune and metabolism gene signature for prognosis prediction of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is still one of the most frequently diagnosed malignancy around the world. The complex etiology and high heterogeneity of CRC necessitates the identification of new reliable signature to identify different tumor prognosis, which may help more precise understanding of the molecular properties of CRC and identify the appropriate treatment for CRC patients. In this study, we aimed to identify a combined immune and metabolism gene signature for prognosis prediction of CRC from large volume of CRC transcriptional data.

METHODS

Gene expression profiling and clinical data of HCC samples was retrieved from the from public datasets. IRGs and MRGs were identified from differential expression analysis. Univariate and multivariate Cox regression analysis were applied to establish the prognostic metabolism-immune status-related signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were generated for diagnostic efficacy estimation. Real-time polymerase chain reaction (RT-PCR), Western blot and immunohistochemistry (IHC) was conducted to verified the expression of key genes in CRC cells and tissues.

RESULTS

A gene signature comprising four genes (including two IRGs and two MRGs) were identified and verified, with superior predictive performance in discriminating the overall survival (OS) of high-risk and low-risk compared to existing signatures. A prognostic nomogram based on the four-gene signature exhibited a best predictive performance, which enabled the prognosis prediction of CRC patients. The hub gene ESM1 related to CRC were selected via the machine learning and prognostic analysis. RT-PCR, Western blot and IHC indicated that ESM1 was high expressed in tumor than normal with superior predictive performance of CRC survival.

CONCLUSIONS

A novel combined MRGs and IRGs-related prognostic signature that could stratify CRC patients into low-and high- risk groups of unfavorable outcomes for survival, was identified and verified. This might help, to some extent, to individualized treatment and prognosis assessment of CRC patients. Similarly, the mining of key genes provides a new perspective to explore the molecular mechanisms and targeted therapies of CRC.

Screening and Identification of Hub Genes in the Corticosteroid Resistance Network in Human Airway Epithelial Cells via Microarray Analysis

Background and Objective: Corticosteroid resistance is a major barrier to chronic obstructive pulmonary disease (COPD), but the exact mechanism of corticosteroid resistance in COPD has been less well studied. Methods: The microarray dataset GSE11906, which includes genomic and clinical data on COPD, was downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified using R software. Gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes (KEGG) were utilized to enrich and analyze the gene cohort related to the response to steroid hormones, respectively. The Connectivity Map (CMap) database was used to screen corticosteroid resistance-related drugs that might exert a potential therapeutic effect. STRING was used to construct a protein-protein interaction (PPI) network of the gene cohort, and the CytoHubba plug-in of Cytoscape was used to screen the hub genes in the PPI network. The expression levels of hub genes in cigarette smoke extract (CSE)-stimulated bronchial epithelial cells were assayed by quantitative real-time PCR and western blotting. Results: Twenty-one genes were found to be correlated with the response to steroid hormones. In the CMap database, 32 small-molecule compounds that might exert a therapeutic effect on corticosteroid resistance in COPD were identified. Nine hub genes were extracted from the PPI network. The expression levels of the BMP4, FOS, FN1, EGFR, and SPP1 proteins were consistent with the microarray data obtained from molecular biology experiments. Scopoletin significantly restrained the increases in the levels of AKR1C3, ALDH3A1, FN1 and reversed the decreases of phosphorylated GR and HDAC2 caused by CSE exposure. Conclusion: The BMP4, FOS, FN1, EGFR, and SPP1 genes are closely correlated with CSE-induced glucocorticoid resistance in airway epithelial cells. Scopoletin may be a potential drug for the treatment of glucocorticoid resistance caused by CSE.

Secretogranin II impairs tumor growth and angiogenesis by promoting degradation of hypoxia-inducible factor-1α in colorectal cancer

Distant metastasis is a major cause of death in patients with colorectal cancer (CRC) but the management of advanced and metastatic CRC still remains problematic due to the distinct molecular alterations during tumor progression. Tumor angiogenesis is a key step in tumor growth, invasion and metastasis. However, the signaling pathways involved in angiogenesis are poorly understood. The results of the present study showed that secretogranin II (SCG2) was significantly downregulated in malignant CRC tissues, and higher expression of SCG2 was correlated with longer disease‐free survival and overall survival of CRC patients. The results of an animal study showed that ectopic expression of SCG2 significantly inhibited CRC tumor growth by disrupting angiogenesis. Furthermore, the inhibition of expression of vascular endothelial growth factor (VEGF) by SCG2 and rescue of VEGF effectively blocked SCG2‐induced inhibition of angiogenesis. Investigations into the underlying mechanism suggested that SCG2 promoted degradation of hypoxia‐inducible factor (HIF)‐1α by interacting with the von Hippel–Lindau tumor suppressor in CRC cells. Blocking of degradation of HIF‐1α effectively attenuated the SCG2‐mediated decrease in expression of VEGF in CRC cells. Collectively, these results demonstrated that treatment with SCG2 effectively inhibited CRC tumor growth by disrupting the activities of HIF‐1α/VEGF, thereby clarifying the anti‐tumor and anti‐angiogenesis roles of SCG2 in CRC, while providing a novel therapeutic target and a potential prognostic marker of disease progression.

Identification of Vitamin D-related gene signature to predict colorectal cancer prognosis

Colorectal cancer (CRC) is one of the most common malignant carcinomas worldwide with poor prognosis, imposing an increasingly heavy burden on patients. Previous experiments and epidemiological studies have shown that vitamin D and vitamin D-related genes play a vital role in CRC. Therefore, we aimed to construct a vitamin D-related gene signature to predict prognosis in CRC. The CRC data from The Cancer Genome Atlas (TCGA) was performed as the training set. A total of 173 vitamin D-related genes in the TCGA CRC dataset were screened, and 17 genes associated with CRC prognosis were identified from them. Then, a vitamin D-related gene signature consisting of those 17 genes was established by univariate and multivariate Cox analyses. Moreover, four external datasets (GSE17536, GSE103479, GSE39582, and GSE17537) were used as testing set to validate the stability of this signature. The high-risk group presented a significantly poorer overall survival than low-risk group in both of training set and testing sets. Besides, the areas under the curve (AUCs) for signature on OS in training set at 1, 3, and 5 years were 0.710, 0.708, 0.710 respectively. The AUCs of the ROC curve in GSE17536 for 1, 3, and 5 years were 0.649, 0.654, and 0.694. These results indicated the vitamin D-related gene signature model could effectively predict the survival status of CRC patients. This vitamin D-related gene signature was also correlated with TNM stage in CRC clinical parameters, and the higher risk score from this model was companied with higher clinical stage. Furthermore, the high accuracy of this prognostic signature was validated and confirmed by nomogram model. In conclusion, we have proposed a novel vitamin D-related gene model to predict the prognosis of CRC, which will help provide new therapeutic targets and act as potential prognostic biomarkers for CRC.

Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

A highly expressed mRNA signature for predicting survival in patients with stage I/II non-small-cell lung cancer after operation

There is an urgent need to identify novel biomarkers that predict the prognosis of patients with NSCLC. In this study,we aim to find out mRNA signature closely related to the prognosis of NSCLC by new algorithm of bioinformatics. Identification of highly expressed mRNA in stage I/II patients with NSCLC was performed with the “Limma” package of R software. Survival analysis of patients with different mRNA expression levels was subsequently calculated by Cox regression analysis, and a multi-RNA signature was obtained by using the training set. Kaplan–Meier estimator, log-rank test and receiver operating characteristic (ROC) curves were used to analyse the predictive ability of the multi-RNA signature. RT-PCR used to verify the expression of the multi-RNA signature, and Westernblot used to verify the expression of proteins related to the multi-RNA signature. We identified fifteen survival-related mRNAs in the training set and classified the patients as high risk or low risk. NSCLC patients with low risk scores had longer disease-free survival than patients with high risk scores. The fifteen-mRNA signature was an independent prognostic factor, as shown by the ROC curve. ROC curve also showed that the combined model of the fifteen-mRNA signature and tumour stage had higher precision than stage alone. The expression of fifteen mRNAs and related proteins were higher in stage II NSCLC than in stage I NSCLC. Multi-gene expression profiles provide a moderate prognostic tool for NSCLC patients with stage I/II disease.

The clinical relevance of gene expression based prognostic signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers, with more than one million new cases every year. In the last few decades, several advancements in therapeutic and preventative levels have reduced the mortality rate, but new biomarkers are required for improved prognosis. The alterations at the genetic and epigenetic level have been recognized as major players in tumorigenesis. The products of gene expression in the form of mRNA, microRNA, and long-noncoding RNA, have started to emerge as important regulatory molecules, playing an important role in cancer. Gene-expression based prognostic risk scores, which quantify and compare their expression, have emerged as promising biomarkers with enormous clinical value. These composite multi-gene models in which more than one gene is used to predict prognosis have been shown to be significantly effective in identifying patients with multiple clinico-pathological risks like overall mortality, response to chemotherapy, risk of metastasis, etc. The advent of microarray and advanced sequencing technologies have led to the generation of large datasets like TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), which have fueled the search for new biomarkers. Continuous evaluation of these candidate biomarkers in clinical settings is promising to improve the management of CRC. These composite gene signatures provide potential in identifying high-risk patients, which might help clinicians to better manage these patients and design appropriate personalized therapeutic interventions. In this review, we emphasize on composite prognostic scores from diverse resources with clinical utility in CRC.

Genomics and prognosis analysis of epithelial-mesenchymal transition in colorectal cancer patients

Background

The epithelial-mesenchymal transition (EMT) plays a pivotal role in various physiological processes, such as embryonic development, tissue morphogenesis, and wound healing. EMT also plays an important role in cancer invasion, metastasis, and chemoresistance. Additionally, EMT is partially responsible for chemoresistance in colorectal cancer (CRC). The aim of this research is to develop an EMT-based prognostic signature in CRC.

Methods

RNA-seq and microarray data, together with clinical information, were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. A total of 244 differentially expressed EMT-related genes (ERGs) were obtained by comparing the expression between normal and tumor tissues. An EMT-related signature of 11 genes was identified as crucially related to the overall survival (OS) of patients through univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and Cox regression analysis. Finally, we established a clinical nomogram to predict the survival possibility of CRC patients by integrating clinical characteristics and the EMT-related gene signature.

Results

Two hundred and forty-four differentially expressed ERGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that EMT-related signaling pathway genes were highly related to CRC. Kaplan-Meier analysis revealed that the 11-EMT signature could significantly distinguish high- and low-risk patients in both TCGA and GEO CRC cohorts. In addition, the calibration curves verified fine concordance between the nomogram prediction model and actual observation.

Conclusion

We developed a novel EMT-related gene signature for the prognosis prediction of CRC patients, which could improve the individualized outcome prediction in CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07615-5.

Analysis of DNA methylation-driven genes for predicting the prognosis of patients with colorectal cancer

Aberrant promoter methylation and ensuing abnormal gene expression are important epigenetic mechanisms that contribute to colorectal oncogenesis. Yet, the prognostic significance of such methylation-driven genes in colorectal cancer (CRC) remains obscure. Herein, a total of 181 genes were identified as the methylation-driven molecular features of CRC by integrated analysis of the expression profiles and the matched DNA methylation data from The Cancer Genome Atlas (TCGA) database. Among them, a five-gene signature (POU4F1, NOVA1, MAGEA1, SLCO4C1, and IZUMO2) was developed as a risk assessment model for predicting the clinical outcomes in CRC. The Kaplan-Meier analysis and Harrell's C index demonstrated that the risk assessment model significantly distinguished the patients in high or low-risk groups (p-value < 0.0001 log-rank test, HR: 2.034, 95% CI: 1.419-2.916, C index: 0.655). The sensitivity and specificity were validated by the receiver operating characteristic (ROC) analysis. Furthermore, different pharmaceutical treatment responses were observed between the high-risk and low-risk groups. Indeed, the methylation-driven gene signature could act as an independent prognostic evaluation biomarker for assessing the OS of CRC patients and guiding the pharmaceutical treatment. Compared with known biomarkers, the methylation-driven gene signature could reveal cross-omics molecular features for improving clinical stratification and prognosis.

An interactive network of alternative splicing events with prognostic value in geriatric lung adenocarcinoma via the regulation of splicing factors

Alternative splicing (AS), an essential process for the maturation of mRNAs, is involved in tumorigenesis and tumor progression, including angiogenesis, apoptosis, and metastasis. AS changes can be frequently observed in different tumors, especially in geriatric lung adenocarcinoma (GLAD). Previous studies have reported an association between AS events and tumorigenesis but have lacked a systematic analysis of its underlying mechanisms. In the present study, we obtained splicing event information from SpliceSeq and clinical information regarding GLAD from The Cancer Genome Atlas. Survival-associated AS events were selected to construct eight prognostic index (PI) models. We also constructed a correlation network between splicing factors (SFs) and survival-related AS events to identify a potential molecular mechanism involved in regulating AS-related events in GLAD. Our study findings confirm that AS has a strong prognostic value for GLAD and sheds light on the clinical significance of targeting SFs in the treatment of GLAD.

Identification of candidate biomarkers and therapeutic drugs of colorectal cancer by integrated bioinformatics analysis

Most colorectal cancer (CRC) patients are diagnosed with advanced stages and low prognosis. We aimed to identify potential diagnostic and prognostic biomarkers, as well as active small molecules of CRC. Microarray data (GSE9348, GSE35279, and GSE106582) were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by the GEO2R platform. Common DEGs were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Cytoscape software was used to construct protein-protein interaction networks and identify hub genes. Hub genes were evaluated by Kaplan-Meier survival analysis in the GEPIA database and validated in two independent microarray data (GSE74602 and GSE83889). Common DEGs were used to select active small molecules by the connectivity map database. A total of 166 DEGs were identified as common DEGs. GO analysis demonstrated that common DEGs were significantly enriched in the apoptotic process, cell proliferation, and cell adhesion. KEGG analysis indicated that the most enriched pathways were the PI3K-Akt signaling pathway and extracellular matrix-receptor interaction. COL1A2, THBS2, TIMP1, and CXCL8 significantly upregulated in colorectal tumor. High expressions of COL1A2, THBS2, and TIMP1 were associated with poor survival, while high expressions of CXCL8 were associated with better survival. We selected 11 small molecules for CRC therapy. In conclusion, we found key dysregulated genes associated with CRC and potential small molecules to reverse them. COL1A2, THBS2, TIMP1, and CXCL8 may act as diagnostic and prognostic biomarkers of CRC.

Development of a 15-gene signature for predicting prognosis in advanced colorectal cancer

Advanced colorectal cancer (CRC) is a significant cause of cancer mortality, with a poor prognosis. Here, we identified a novel prognostic signature for predicting survival of advanced CRC. Advanced CRC data were used (training set: n = 267 and validation set: n = 264). The survival analyses were investigated. The functional analysis of the prognostic signature was examined. In this study, our 15-gene signature was established and was an independent prognostic factor of advanced CRC. Stratification analyses also showed that this signature was still powerful for survival prediction in each stratum of age, gender, stage, and metastasis status. In mechanism, our signature involved in DNA replication, DNA damage, and cell cycle. Therefore, our findings suggested that this 15-gene signature has prognostic and predictive value in advanced CRC, which could be further used in personalized therapy for advanced CRC.

Development of an Immune Infiltration-Related Eight-Gene Prognostic Signature in Colorectal Cancer Microenvironment

Objective

Stromal cells and immune cells have important clinical significance in the microenvironment of colorectal cancer (CRC). This study is aimed at developing a CRC gene signature on the basis of stromal and immune scores.

Methods

A cohort of CRC patients (n = 433) were adopted from The Cancer Genome Atlas (TCGA) database. Stromal/immune scores were calculated by the ESTIMATE algorithm. Correlation between prognosis/clinical characteristics and stromal/immune scores was assessed. Differentially expressed stromal and immune genes were identified. Their potential functions were annotated by functional enrichment analysis. Cox regression analysis was used to develop an eight-gene risk score model. Its predictive efficacies for 3 years, 5 years, overall survival (OS), and progression-free survival interval (PFI) were evaluated using time-dependent receiver operating characteristic (ROC) curves. The correlation between the risk score and the infiltering levels of six immune cells was analyzed using TIMER. The risk score was validated using an independent dataset.

Results

Immune score was in a significant association with prognosis and clinical characteristics of CRC. 736 upregulated and two downregulated stromal and immune genes were identified, which were mainly enriched into immune-related biological processes and pathways. An-eight gene prognostic risk score model was conducted, consisting of CCL22, CD36, CPA3, CPT1C, KCNE4, NFATC1, RASGRP2, and SLC2A3. High risk score indicated a poor prognosis of patients. The area under the ROC curves (AUC) s of the model for 3 years, 5 years, OS, and PFI were 0.71, 0.70, 0.73, and 0.66, respectively. Thus, the model possessed well performance for prediction of patients' prognosis, which was confirmed by an external dataset. Moreover, the risk score was significantly correlated with immune cell infiltration.

Conclusion

Our study conducted an immune-related prognostic risk score model, which could provide novel targets for immunotherapy of CRC.

Integrative Gene Expression Profiling Analysis to Investigate Potential Prognostic Biomarkers for Colorectal Cancer

BACKGROUND Despite noteworthy advancements in the multidisciplinary treatment of colorectal cancer (CRC) and deeper understanding in the molecular mechanisms of CRC, many of CRC patients with histologically identical tumors present different treatment response and prognosis. Thus, more evidence on novel predictive and prognostic biomarkers for CRC remains urgently needed. This study aims to identify potential prognostic biomarkers for CRC with integrative gene expression profiling analysis. MATERIAL AND METHODS Differential expression analysis of paired CRC and adjacent normal tissue samples in 6 microarray datasets was independently performed, and the 6 datasets were integrated by the robust rank aggregation method to detect consistent differentially expressed genes (DEGs). Aberrant expression patterns of these genes were further validated in RNA sequencing data. Then, gene set enrichment analysis (GSEA) was performed to investigate significantly dysregulated biological functions in CRC. Finally, univariate, LASSO and multivariate Cox regression models were built to identify key prognostic genes in CRC patients. RESULTS A total of 990 DEGs (495 downregulated and 495 upregulated genes) were acquired after integratedly analyzing the 6 microarray datasets, and 4131 DEGs (2050 downregulated and 2081 upregulated genes) were obtained from the RNA sequencing dataset. Subsequently, these DEGs were intersected and 885 consistent DEGs were finally identified, including 458 downregulated and 427 upregulated genes. Two risky prognostic genes (TIMP1 and LZTS3) and 5 protective prognostic genes (AXIN2, CXCL1, ITLN1, CPT2 and CLDN23) were identified, which were significantly associated with the prognosis of CRC. CONCLUSIONS The 7 genes that we identified would provide more evidence for further applying novel diagnostic and prognostic biomarkers in clinical practice to facilitate personalized treatment of CRC.

Identification of a 5-Gene Signature Predicting Progression and Prognosis of Clear Cell Renal Cell Carcinoma

Background

Although the mortality rates of clear cell renal cell carcinoma (ccRCC) have decreased in recent years, the clinical outcome remains highly dependent on the individual patient. Therefore, identifying novel biomarkers for ccRCC patients is crucial.

Material/Methods

In this study, we obtained RNA sequencing data and clinical information from the TCGA database. Subsequently, we performed integrated bioinformatic analysis that includes differently expressed genes analysis, gene ontology and KEGG pathway analysis, protein-protein interaction analysis, and survival analysis. Moreover, univariate and multivariate Cox proportional hazards regression models were constructed.

Results

As a result, we identified a total of 263 dysregulated genes that may participate in the metastasis of ccRCC, and established a predictive signature relying on the expression of OTX1, MATN4, PI3, ERVV-2, and NFE4, which could serve as significant progressive and prognostic biomarkers for ccRCC.

Conclusions

We identified differentially expressed genes that may be involved in the metastasis of ccRCC. Moreover, a predictive signature based on the expression of OTX1, MATN4, PI3, ERVV-2, and NFE4 could be an independent prognostic factor for ccRCC.