Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

Integrated single-cell and bulk RNA sequencing analysis identifies a prognostic signature related to ferroptosis dependence in colorectal cancer

Ferroptosis is an iron-dependent form of cell death induced by lipid oxidation with an essential role in diseases, including cancer. Since prognostic value of ferroptosis-dependent related genes (FDRGs) in colorectal cancer (CRC) remains unclear, we explored the significance of FDRGs in CRC through comprehensive single-cell analysis. We downloaded the GSE161277 dataset for single-cell analyses and calculated the ferroptosis-dependent gene score (FerrScore) for each cell type. According to each cell type-specific median FerrScore, we categorized the cells into low- and high-ferroptosis groups. By analyzing differentially-expressed genes across the two groups, we identified FDRGs. We further screened these prognosis-related genes used to develop a prognostic signature and calculated its correlation with immune infiltration. We also compared immune checkpoint gene efficacy among different risk groups, and qRT-PCR was performed in colorectal normal and cancer cell lines to explore whether the signature genes could be used as clinical prognostic indicators. In total, 523 FDRGs were identified. A prognostic signature including five signature genes was constructed, and patients were divided into two risk groups. The high-risk group had poor survival rates and displayed high levels of immune infiltration. Our newly developed ferroptosis-based prognostic signature possessed a high predictive ability for CRC.

Identification of key immune cell-related genes involved in tumorigenesis and prognosis of cervical squamous cell carcinoma

The infiltration of immune cells can significantly affect the prognosis and immune therapy of patients with cervical squamous cell carcinoma (CSCC). This study aimed to explore key immune cell-related genes in the tumorigenesis and prognosis of CSCC. The module significantly related to immunity was screened by weighted gene co-expression network analysis (WGCNA) and ESTIMATE analysis, followed by correlation analysis with clinical traits. Key candidate genes were intersected with the protein-protein interaction (PPI) network genes for immune-related genes. The relationship between immune cell infiltration and key genes was analyzed. Tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) predicted the response to immunotherapy in CSCC patients. Clinically, quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry were manipulated for analyzing the changes in mRNA and protein expression of key genes in cancer. Western blot was conducted to assess the correlation between key genes and immune infiltration. The brown module was notably associated with the immune microenvironment of CSCC, from which three immune-related key genes (TYROBP, CCL5, and HLA-DRA) were obtained. High expression of these genes was significantly positively associated with the infiltration abundance of T cells, B cells, and other immune cells. High expression levels of three key genes were confirmed in para-cancer tissue and correlated with the abundance of immune cells. The high-expression group of key genes was more sensitive to immunotherapy. We provide a theoretical basis for searching for potential targets for effective treatment and diagnosis of CSCC and provide new ideas for developing novel immunotherapy strategies.

Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis

Background

Lung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively.

Materials and methods

LUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay.

Results

A total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues.

Conclusion

Immune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma

Background: Colon adenocarcinoma (COAD) is a heterogeneous tumor and senescence is crucial in the occurrence of cancer. This study aimed to identify senescence-based subtypes and construct a prognostic signature to predict the prognosis and guide immunotherapy or chemotherapy decisions for COAD patients. Methods: Based on the single-cell RNA sequencing (scRNA-seq) data of 13 samples from the Gene Expression Omnibus (GEO) database, we assessed cellular senescence characteristics. Transcriptome data, copy number variations (CNVs) and single nucleotide variations (SNVs) data were obtained from The Cancer Genome Atlas (TCGA) database. GSE39582 and GSE17537 were used for validation. Senescence subtypes were identified using unsupervised consensus clustering analysis, and a prognostic signature was developed using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO). Response of risk groups to chemotherapy was predicted using the half-maximal inhibitory concentration (IC50) values. We further analyzed the relationship between risk gene expression and methylation level. The prediction performance was assessed by nomogram. Results: Senescence-related pathways were highly enriched in malignant cells and bulk RNA-seq verified cellular senescence. Three senescence subtypes were identified, in which patients in clust3 had poorest prognosis and higher T stage, accompanied with higher tumor mutation burden (TMB) and mutations, activated inflammatory response, more immune cell infiltration, and higher immune escape tendency. A senescence-based signature using 11 genes (MFNG, GPRC5B, TNNT1, CCL22, NOXA1, PABPC1L, PCOLCE2, MID2, CPA3, HSPA1A, and CALB1) was established, and accurately predicted a lower prognosis in high risk patients. Its robustness was validated by external cohort. Low risk patients were more sensitive to small molecule drugs including Erlotinib, Sunitinib, MG-132, CGP-082996, AZ628, Sorafenib, VX-680, and Z-LLNle-CHO. Risk score was an independent prognostic factor and nomogram confirmed its reliability. Four risk genes (CALB1, CPA3, NOXA1, and TNNT1) had significant positive correlation with their methylation level, while six genes (CCL22, GPRC5B, HSPA1A, MFNG, PABPC1L, and PCOLCE2) were negatively correlated with their methylation level. Conclusion: This study provides novel understanding of heterogeneity in COAD from the perspective of senescence, and develops signatures for prognosis prediction in COAD.

A Ferroptosis-Related LncRNA Signature Associated with Prognosis, Tumor Immune Environment, and Genome Instability in Hepatocellular Carcinoma

Background

Ferroptosis is an iron-dependent form of cell death. In this study, we identified ferroptosis-related long noncoding RNAs (FRlncRNAs) to investigate their association with hepatocellular carcinoma (HCC) in prognosis, tumor immune environment, and genome instability.

Methods

Transcriptome profile data of HCC were retrieved from a public database. FRlncRNAs were identified by co-expression analysis. Patients were randomly divided into training and test cohorts. Univariate Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were performed to construct a risk model. Patients were divided into high- and low-risk groups based on the risk model. AUC and C index were used to assess the risk model. Survival analysis, immune status, and genome instability were compared between the two groups.

Results

Sixteen FRlncRNAs were identified and used to construct an FRlncRNA signature for the risk model. The Kaplan-Meier analysis revealed that patients in the high-risk group had poorer overall survival than patients in the low-risk group. The area under curve of the risk model was 0.879, 0.809, and 0.757 in the training cohort and 0.635, 0.688, and 0.739 in the test cohort at 1, 2, and 3 years, respectively. The risk model was an independent prognostic predictor and showed excellent prediction of prognosis compared with clinicopathological features. For the differentially expressed ferroptosis-related genes, many enriched metabolic pathways were identified in the functional enrichment analysis. Immune cells such as CD8+ T cells, macrophages M1, natural killer cells, and B cells, which may be associated with antitumor immune responses, differed between the high- and low-risk groups. Genome instability based on the risk model was also explored. A total of 61 genes were differently mutated between the two risk groups, and among them, TP53, HECW2, TRIM66, MCTP2, and KIAA1551 had the most significant mutation frequency differences.

Conclusion

The FRlncRNA signature is closely related with overall survival, tumor immune environment, and genome instability in HCC.

Molecular Biomarker of Drug Resistance Developed From Patient-Derived Organoids Predicts Survival of Colorectal Cancer Patients

The drug 5-fluorouracil (5-Fu) is the critical composition of colorectal cancer (CRC) treatments. Prognostic and predictive molecular biomarkers for CRC patients (CRCpts) treated with 5-Fu-based chemotherapy can provide assistance for tailoring treatment approach. Here, we established a molecular biomarker of 5-Fu resistance derived from colorectal cancer organoids (CRCOs) for predicting the survival of CRCpts. Forty-one CRCO cultures were generated from 50 CRC tumor tissues after surgery (82%). The following experiments revealed a great diversity in drug sensitivity for 10 μM 5-Fu treatment tested by using organoid size change. Fourteen cases (34.1%) were 5-Fu sensitive and the other 27 (65.9%) were resistant. Then, differentially expressed genes (DEGs) associated with 5-Fu resistance were outputted by transcriptome sequencing. In particular, DEGs were generated in two comparison groups: 1) 5-Fu sensitive and resistant untreated CRCOs; 2) CRCOs before 5-Fu treatment and surviving CRCOs after 5-Fu treatment. Some molecules and most of the pathways that have been reported to be involved in 5-Fu resistance were identified in the current research. By using DEGs correlated with 5-Fu resistance and survival of CRCpts, the gene signature and drug-resistant score model (DRSM) containing five molecules were established in The Cancer Genome Atlas (TCGA)-CRC cohort by least absolute shrinkage and selection operator (LASSO) regression analysis and 5-fold cross-validation. Multivariate analysis revealed that drug-resistant score (DRS) was an independent prognostic factor for overall survival (OS) in CRCpts in TCGA-CRC cohort (P < 0.001). Further validation results from four Gene Expression Omnibus (GEO) cohorts elucidated that the DRSM based on five genes related to 5-Fu chemosensitivity and developed from patient-derived organoids can predict survival of CRCpts. Meanwhile, our model could predict the survival of CRCpts in different subgroups. Besides, the difference of molecular pathways, tumor mutational burden (TMB), immune response-related pathways, immune score, stromal score, and immune cell proportion were dissected between DRS-high and DRS-low patients in TCGA-CRC cohort.

The protein 4.1R downregulates VEGFA in M2 macrophages to inhibit colon cancer metastasis

M2 macrophages are crucial components of the tumour microenvironment and have been shown to be closely related to tumour progression. Co-culture with 4.1R^-/- M2 macrophages enhances the malignancy of colon cancer (CC), but the mechanism remains unclear. Here, we report that protein 4.1R knockout reduced the phagocytosis of M2 macrophages (M-CSF/IL-4-treated bone marrow cells) and promoted MC38 colon cancer cell proliferation, migration, invasion, tumour formation and epithelial-mesenchymal transition (EMT), which are regulated by M2 macrophages. Further mechanistic dissection revealed that the 4.1R knockout upregulated vascular endothelial growth factor A (VEGFA) secreted by M2 macrophages and promoted colon cancer progression by activating the PI3K/AKT signalling pathway. In summary, our present study identified that 4.1R downregulates VEGFA secretion in M2 macrophages and delays the malignant potential of colon cancer by inhibiting the PI3K/AKT signalling pathway.

Identification and validation of an immune-associated RNA-binding proteins signature to predict clinical outcomes and therapeutic responses in colon cancer patients

Background

The immune infiltration of patients with colon cancer (CC) is closely associated with RNA-binding proteins (RBPs). However, immune-associated RBPs (IARBPs) in CC remain unexplored.

Methods

The data were downloaded from The Cancer Genome Atlas (TCGA) and the patients were divided into four immune subgroups by single sample gene set enrichment analysis (ssGSEA), in which weighted gene correlation network analysis (WGCNA) identified modules of co-expressed genes correlated with immune infiltration. Univariate (UCR) and multivariate Cox regression (MCR) analyses were applied to screen survival-associated IARBPs. Then, a prognostic signature was performed on TCGA dataset. Risk model was constructed based on the TCGA dataset. Based on the median risk score, CC patients were subdivided into low- and high-risk groups. Furthermore, the accuracy and prognostic value of this signature were validated by using Kaplan-Meier (K-M) curve, receiver operating characteristic (ROC). We further validated the findings in Gene Expression Omnibus (GEO) database. Finally, we evaluated the association between gene expression level and drug sensitivity.

Results

Based on the infiltration of immune cells, the TCGA patients were divided into four subgroups. In total, we identified 25 IARBPs, after differential expression and WGCNA analysis. Subsequently, two IARBP signatures (FBXO17 and PPARGC1A) were identified to be significantly associated with the overall survival (OS) of CC patients. K-M survival analysis revealed that the low-risk group correlated with prolonged OS. The prognostic signature was an independent prognostic factor and reflects the immune status of CC patients. Finally, FBXO17 was related with drug sensitivity of bleomycin, gemcitabine, and lenvatinib. PPARGC1A was related to drug sensitivity of dabrafenib, vemurafenib, and trametinib.

Conclusion

A novel two immune-associated RBPs that was established that may be useful in predicting survival and individualized treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02411-2.

Circ_0026416 downregulation blocks the development of colorectal cancer through depleting MYO6 expression by enriching miR-545-3p

Background

Emerging evidence reveals that the initiation and development of human cancers, including colorectal cancer (CRC), are associated with the deregulation of circular RNAs (circRNAs). Our study intended to disclose the role of circ_0026416 in the malignant behaviors of CRC.

Methods

The detection for circ_0026416 expression, miR-545-3p expression, and myosin VI (MYO6) mRNA expression was performed using quantitative real-time PCR (qPCR). CCK-8 assay, colony formation assay, transwell assay, and flow cytometry assay were applied for functional analysis to monitor cell proliferation, migration, invasion, and apoptosis. The protein levels of MYO6 and epithelial mesenchymal-transition (EMT) markers were detected by western blot. Mouse models were used to determine the role of circ_0026416 in vivo. The potential relationship between miR-545-3p and circ_0026416 or MYO6 was verified by dual-luciferase reporter assay and RIP assay.

Results

The expression of circ_0026416 was increased in CRC tumor tissues and cell lines. Circ_0026416 downregulation inhibited CRC cell proliferation, colony formation, migration, invasion, and EMT but induced cell apoptosis in vitro, and circ_0026416 knockdown also blocked tumor growth in vivo. MiR-545-3p was a target of circ_0026416, and rescue experiments indicated that circ_0026416 knockdown blocked CRC development by enriching miR-545-3p. In addition, miR-545-3p targeted MYO6 and inhibited MYO6 expression. MiR-545-3p enrichment suppressed CRC cell malignant behaviors by sequestering MYO6. Importantly, circ_0026416 knockdown depleted MYO6 expression by enriching miR-545-3p.

Conclusion

Circ_0026416 downregulation blocked the development of CRC through depleting MYO6 expression by enriching miR-545-3p.

Highlights

Circ_0026416 downregulation inhibits CRC development in vitro and in vivo.

Circ_0026416 regulates the expression of MYO6 by targeting miR-545-3p.

Circ_0026416 governs the miR-545-3p/MYO6 axis to regulate CRC progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02407-y.

LINC01224 promotes colorectal cancer progression through targeting miR-485-5p/MYO6 axis

Background

Long noncoding RNAs (lncRNAs) are related to colorectal cancer (CRC) development. However, the role and mechanism of lncRNA LINC01224 in CRC development are largely unknown.

Methods

LINC01224, Yin Yang 1 (YY1), microRNA (miR)-485-5p, and myosins of class VI (MYO6) levels were examined using quantitative reverse transcription polymerase chain reaction and western blotting. Functional analyses were processed through CCK-8, colony formation, flow cytometry, transwell, and xenograft analyses. Dual-luciferase reporter, chromatin immunoprecipitation (ChIP), RNA immunoprecipitation, and pull-down assays were conducted to analyze the binding interaction.

Results

LINC01224 abundance was elevated in CRC tissue samples and cell lines. Elevated LINC01224 might indicate the lower 5-year overall survival in 52 CRC patients. LINC01224 was upregulated via the transcription factor YY1. LINC01224 knockdown restrained CRC cell proliferation, migration, and invasion and increased apoptosis. MiR-485-5p was sponged by LINC01224, and miR-485-5p downregulation relieved the influence of LINC01224 interference on CRC progression. MYO6 was targeted via miR-485-5p and regulated via LINC01224/miR-485-5p axis. MiR-485-5p overexpression suppressed CRC cell proliferation, migration, and invasion and facilitated apoptosis. MYO6 upregulation mitigated the role of miR-485-5p. LINC01224 knockdown decreased xenograft tumor growth.

Conclusion

YY1-induced LINC01224 regulates CRC development via modulating miR-485-5p/MYO6 axis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02389-x.

Determination of a six-gene prognostic model for cervical cancer based on WGCNA combined with LASSO and Cox-PH analysis

AIM

This study aimed to establish a risk model of hub genes to evaluate the prognosis of patients with cervical cancer.

METHODS

Based on TCGA and GTEx databases, the differentially expressed genes (DEGs) were screened and then analyzed using GO and KEGG analyses. The weighted gene co-expression network (WGCNA) was then used to perform modular analysis of DEGs. Univariate Cox regression analysis combined with LASSO and Cox-pH was used to select the prognostic genes. Then, multivariate Cox regression analysis was used to screen the hub genes. The risk model was established based on hub genes and evaluated by risk curve, survival state, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve.

RESULTS

We screened 1265 DEGs between cervical cancer and normal samples, of which 620 were downregulated and 645 were upregulated. GO and KEGG analyses revealed that most of the upregulated genes were related to the metastasis of cancer cells, while the downregulated genes mostly acted on the cell cycle. Then, WGCNA mined six modules (red, blue, green, brown, yellow, and gray), and the brown module with the most DEGs and related to multiple cancers was selected for the follow-up study. Eight genes were identified by univariate Cox regression analysis combined with the LASSO Cox-pH model. Then, six hub genes (SLC25A5, ENO1, ANLN, RIBC2, PTTG1, and MCM5) were screened by multivariate Cox regression analysis, and SLC25A5, ANLN, RIBC2, and PTTG1 could be used as independent prognostic factors. Finally, we determined that the risk model established by the six hub genes was effective and stable.

CONCLUSIONS

This study supplies the prognostic value of the risk model and the new promising targets for the cervical cancer treatment, and their biological functions need to be further explored.

Importance of Circ0009910 in colorectal cancer pathogenesis as a possible regulator of miR-145 and PEAK1

Introduction

Colorectal cancer (CRC) is one of the most frequent neoplasms in the world. Based on the emerging role of noncoding RNAs, particularly circular RNAs in pathogenesis of cancers, we designed this study to inspect the expression levels of a circ0009910-mediated regulatory pathway in colorectal cancer.

Methods

After bioinformatics analyses and construction of putative circ0009910/ miR-145-5p/PEAK1 pathway, the expression levels of these components were evaluated in 50 CRC tissues and adjacent specimens by quantitative real-time PCR. Moreover, we appraised the correlation coefficients between these transcripts and calculated the correlation between circ0009910 expression levels with clinicopathological features of patients.

Results

Circ0009910 and PEAK1 were significantly upregulated, while miR-145-5p was decreased in CRC samples compared with adjacent tissues (p < 0.05). Moreover, statistically significant correlations were observed between expression levels of circ0009910, miR-145-5p, and PEAK1. We also reported considerable correlations between circ0009910 expression and clinicopathological parameters including sex and perineural invasion. Finally, ROC curve analysis showed circ0009910 level as a discriminative biomarker for CRC.

Conclusion

For the first time, we could introduce circ0009910 as an important biomarker in CRC. Collectively, this investigation helped us to identify a newly diagnosed pathway in CRC that can be a potential axis for designing effective drugs for treatment of CRC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02378-0.