Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients

Identification of immunotherapy-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognostic signature in gastric carcinoma

BACKGROUND

Recent advances in immunotherapy have elicited a considerable amount of attention as viable therapeutic options for several cancer types, the present study aimed to explore the immunotherapy-related genes (IRGs) and develop a prognostic risk signature in gastric carcinoma (GC) based on these genes.

METHODS

IRGs were identified by comparing immunotherapy responders and non-responders in GC. Then, GC patients were divided into distinct subtypes by unsupervised clustering method based on IRGs, and the differences in immune characteristics and prognostic stratification between these subtypes were analyzed. An immunotherapy-related risk score (IRRS) signature was developed and validated for risk classification and prognosis prediction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Besides, the predictive ability of the IRRS in immunotherapy response was also determined.

RESULTS

A total of 63 IRGs were identified, and 371 GC patients were stratified into two molecular subgroups with significantly different prognosis and immune characteristics. Then, an IRRS signature comprised of three IRGs (CENP8, NRP1, and SERPINE1) was constructed to predict the prognosis of GC patients in TCGA cohort. Importantly, external validation in multiple GEO cohorts further confirmed the universal applicability of the IRRS in distinct populations. Furthermore, we found that the IRRS was significantly correlated with patient's responsiveness to immunotherapy, GC patients with low IRRS are more likely to benefit from existing immunotherapy.

CONCLUSIONS

The risk score could serve as a robust prognostic biomarker, provide therapeutic benefits for immunotherapy and may be helpful for clinical decision making in GC patients.

Hypoxia-related THBD+ macrophages as a prognostic factor in glioma: Construction of a powerful risk model

Glioma is a prevalent malignant tumour characterized by hypoxia as a pivotal factor in its progression. This study aims to investigate the impact of the most severely hypoxic cell subpopulation in glioma. Our findings reveal that the THBD+ macrophage subpopulation is closely associated with hypoxia in glioma, exhibiting significantly higher infiltration in tumours compared to non-tumour tissues. Moreover, a high proportion of THBD+ cells correlates with poor prognosis in glioblastoma (GBM) patients. Notably, THBD+ macrophages exhibit hypoxic characteristics and epithelial-mesenchymal transition features. Silencing THBD expression leads to a notable reduction in the proliferation and metastasis of glioma cells. Furthermore, we developed a THBD+ macrophage-related risk signature (THBDMRS) through machine learning techniques. THBDMRS emerges as an independent prognostic factor for GBM patients with a substantial prognostic impact. By comparing THBDMRS with 119 established prognostic features, we demonstrate the superior prognostic performance of THBDMRS. Additionally, THBDMRS is associated with glioma metastasis and extracellular matrix remodelling. In conclusion, hypoxia-related THBD+ macrophages play a pivotal role in glioma pathogenesis, and THBDMRS emerges as a potent and promising prognostic tool for GBM, contributing to enhanced patient survival outcomes.

A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer

PURPOSE

This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis.

METHODS

SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses.

RESULTS

Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets.

CONCLUSION

GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.

Downstream STING pathways IRF3 and NF-κB differentially regulate CCL22 in response to cytosolic dsDNA

Double-stranded DNA (dsDNA) in the cytoplasm of eukaryotic cells is abnormal and typically indicates the presence of pathogens or mislocalized self-DNA. Multiple sensors detect cytosolic dsDNA and trigger robust immune responses via activation of type I interferons. Several cancer immunotherapy treatments also activate cytosolic nucleic acid sensing pathways, including oncolytic viruses, nucleic acid-based cancer vaccines, and pharmacological agonists. We report here that cytosolic dsDNA introduced into malignant cells can robustly upregulate expression of CCL22, a chemokine responsible for the recruitment of regulatory T cells (Tregs). Tregs in the tumor microenvironment are thought to repress anti-tumor immune responses and contribute to tumor immune evasion. Surprisingly, we found that CCL22 upregulation by dsDNA was mediated primarily by interferon regulatory factor 3 (IRF3), a key transcription factor that activates type I interferons. This finding was unexpected given previous reports that type I interferon alpha (IFN-α) inhibits CCL22 and that IRF3 is associated with strong anti-tumor immune responses, not Treg recruitment. We also found that CCL22 upregulation by dsDNA occurred concurrently with type I interferon beta (IFN-β) upregulation. IRF3 is one of two transcription factors downstream of the STimulator of INterferon Genes (STING), a hub adaptor protein through which multiple dsDNA sensors transmit their signals. The other transcription factor downstream of STING, NF-κB, has been reported to regulate CCL22 expression in other contexts, and NF-κB has also been associated with multiple pro-tumor functions, including Treg recruitment. However, we found that NF-κB in the context of activation by cytosolic dsDNA contributed minimally to CCL22 upregulation compared with IRF3. Lastly, we observed that two strains of the same cell line differed profoundly in their capacity to upregulate CCL22 and IFN-β in response to dsDNA, despite apparent STING activation in both cell lines. This finding suggests that during tumor evolution, cells can acquire, or lose, the ability to upregulate CCL22. This study adds to our understanding of factors that may modulate immune activation in response to cytosolic DNA and has implications for immunotherapy strategies that activate DNA sensing pathways in cancer cells.

The roles of FLOT1 in human diseases (Review)

FLOT1, a scaffold protein of lipid rafts, is involved in several biological processes, including lipid raft protein‑-dependent or clathrin‑independent endocytosis, and the formation of hippocampal synapses, amongst others. Increasing evidence has shown that FLOT1 can function as both a cancer promoter and cancer suppressor dependent on the type of cancer. FLOT1 can affect the occurrence and development of several types of cancer by affecting epithelial‑mesenchymal transition, proliferation of cancer cells, and relevant signaling pathways, and is regulated by long intergenic non‑coding RNAs or microRNAs. In the nervous system, overexpression or abnormally low expression of FLOT1 may lead to the occurrence of neurological diseases, such as Alzheimer's disease, Parkinson's disease, major depressive disorder and other diseases. Additionally, it is also associated with dilated cardiomyopathy, pathogenic microbial infection, diabetes‑related diseases, and gynecological diseases, amongst other diseases. In the present review, the structure and localization of FLOT1, as well as the physiological processes it is involved in are reviewed, and then the upstream and downstream regulation of FLOT1 in human disease, particularly in different types of cancer and neurological diseases are discussed, with a focus on potentially targeting FLOT1 for the clinical treatment of several diseases.

Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data

Background

Colon cancer (CC) ranks as one of the leading causes of cancer-related mortality globally. Single-cell transcriptome sequencing (scRNA-seq) offers precise gene expression data for distinct cell types. This study aimed to utilize scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data from CC samples to develop a novel prognostic model.

Methods

scRNA-seq data was downloaded from the GSE161277 database. R packages including "Seurat", "Harmony", and "singleR" were employed to categorize eight major cell types within normal and tumor tissues. By comparing tumor and normal samples, differentially expressed genes (DEGs) across these major cell types were identified. Gene Ontology (GO) enrichment analyses of DEGs for each cell type were conducted using "Metascape". DEGs-based signature construction involved Cox regression and least absolute shrinkage operator (LASSO) analyses, performed on The Cancer Genome Atlas (TCGA) training cohort. Validation occurred in the GSE39582 and GSE33382 datasets. The expression pattern of prognostic genes was verified using spatial transcriptome sequencing (ST-seq) data. Ultimately, an established prognostic nomogram based on the gene signature and age was established and calibrated. Sensitivity to chemotherapeutic drugs was predicted with the "oncoPredict" R package.

Results

Using scRNA-Seq data, we examined 33,213 cells, categorizing them into eight cell types within normal and tumor samples. GO enrichment analysis revealed various cancer-related pathways across DEGs in these cell types. Among the 55 DEGs identified via univariate Cox regression, four independent prognostic genes emerged: PTPN6, CXCL13, SPINK4, and NPDC1. Expression validation through ST-seq confirmed PTPN6 and CXCL13 predominance in immune cells, while SPINK4 and NPDC1 were relatively epithelial cell-specific. Creating a four-gene prognostic signature, Kaplan-Meier survival analyses emphasized higher risk scores correlating with unfavorable prognoses, confirmed across training and validation cohorts. The risk score emerged as an independent prognostic factor, supported by a reliable nomogram. Intriguingly, drug sensitivity analysis unveiled contrasting anti-cancer drug responses in the two risk groups, suggesting significant clinical implications.

Conclusion

We developed a novel prognostic four-gene risk model, and these genes may act as potential therapeutic targets for CC.

A novel investigation into an E2F transcription factor-related prognostic model with seven signatures for colon cancer patients

The pathogenesis of colon cancer, a common gastrointestinal tumour, involves complicated factors, especially a series of cell cycle-related genes. E2F transcription factors during the cell cycle play an essential role in the occurrence of colon cancer. It is meaningful to establish an efficient prognostic model of colon cancer targeting cellular E2F-associated genes. This has not been reported previously. The authors first aimed to explore the links of E2F genes with the clinical outcomes of colon cancer patients by integrating data from the TCGA-COAD (n = 521), GSE17536 (n = 177) and GSE39582 (n = 585) cohorts. The Cox regression and Lasso modelling approach to identify a novel colon cancer prognostic model involving several hub genes (CDKN2A, GSPT1, PNN, POLD3, PPP1R8, PTTG1 and RFC1) were utilised. Moreover, an E2F-related nomogram that efficiently predicted the survival rates of colon cancer patients was created. Additionally, the authors first identified two E2F tumour clusters, which showed distinct prognostic features. Interestingly, the potential links of E2F-based classification and 'protein secretion' issues of multiorgans and tumour infiltration of 'T-cell regulatory (Tregs)' and 'CD56dim natural killer cell' were detected. The authors' findings are of potential clinical significance for the prognosis assessment and mechanistic exploration of colon cancer.

Prognostic model development for classification of colorectal adenocarcinoma by using machine learning model based on feature selection technique boruta

Colorectal cancer (CRC) is the third most prevalent cancer type and accounts for nearly one million deaths worldwide. The CRC mRNA gene expression datasets from TCGA and GEO (GSE144259, GSE50760, and GSE87096) were analyzed to find the significant differentially expressed genes (DEGs). These significant genes were further processed for feature selection through boruta and the confirmed features of importance (genes) were subsequently used for ML-based prognostic classification model development. These genes were analyzed for survival and correlation analysis between final genes and infiltrated immunocytes. A total of 770 CRC samples were included having 78 normal and 692 tumor tissue samples. 170 significant DEGs were identified after DESeq2 analysis along with the topconfects R package. The 33 confirmed features of importance-based RF prognostic classification model have given accuracy, precision, recall, and f1-score of 100% with 0% standard deviation. The overall survival analysis had finalized GLP2R and VSTM2A genes that were significantly downregulated in tumor samples and had a strong correlation with immunocyte infiltration. The involvement of these genes in CRC prognosis was further confirmed on the basis of their biological function and literature analysis. The current findings indicate that GLP2R and VSTM2A may play a significant role in CRC progression and immune response suppression.

Extracellular matrix-based gene signature for predicting prognosis in colon cancer and immune microenvironment

Background

The extracellular matrix (ECM) plays a vital role in progression, expansion, and prognosis of malignancies. In this study, we aimed to explore a novel ECM-based prognostic model for patients with colon cancer (CC).

Methods

ECM-related genes were obtained from Molecular Signatures database. Differential expression analysis was performed using the CC dataset from The Cancer Genome Atlas (TCGA) database. Four ECM-related genes related to overall survival were identified using the Cox regression and LASSO analysis. Then an ECM-related signature was developed and verified in three independent CC cohorts (GSE33882, GSE39582 and GSE29621) from the Gene Expression Omnibus (GEO). A prognostic nomogram was developed incorporating the ECM-related gene signature with clinical risk factors. CIBERSORT was used to explore the immune cell infiltration level. Human Protein Atlas (HPA) database was utilized to validate the expression levels of identified prognostic ECM genes.

Results

Four ECM-related genes (CXCL13, CXCL14, SFRP5 and THBS4) were identified to develop an ECM-based gene signature and demarcated CC patients into the high- and low-risk groups. In training and validation datasets, patients in the low-risk group had better overall survival outcomes than those in the high-risk group (log-rank P<0.001). In addition, ECM-related signature was significantly associated with consensus molecular subtype 4 (CMS4) as well as other known clinical risk factors such as a higher Tumor, Nodal Involvement, Metastasis (TNM) stage. Moreover, the risk score derived from the ECM-based gene signature could be utilized as an independent prognostic factor for CC patients. A nomogram including the ECM-related gene signature, age and stage was developed to serve clinical practice. CIBERSORT analysis showed immune cell infiltration was different between high- and low-risk groups. The immunohistochemical results derived from HPA indicated differential expression of prognosis-related ECM genes in CC and normal tissues.

Conclusions

In the present study, a novel risk model based on ECM-signature could effectively reflect individual risk classification and provide potential therapeutic targets for CC patients. Moreover, the prognostic nomogram may help predict individualized survival.

A novel chromatin regulator signature predicts the prognosis, clinical features and immunotherapy of colon cancer

Aim: To elucidate the potential function and prognostic value of chromatin regulators (CRs) in colon cancer. Materials & methods: A comprehensive analysis of CR RNA expression data from public databases was conducted. Results: The authors successfully established and validated a 17 CR-based signature using public databases. Ten CRs of the signature were eventually verified at the protein level using the Human Protein Atlas database. Functional enrichment showed that CRs were significantly enriched in cancer-related pathways. This signature was remarkably relevant to immune cell infiltration, immune checkpoints, tumor immune dysfunction and exclusion (TIDE) score and drug sensitivity. Conclusion: The authors identified a novel, reliable prognostic signature for colon cancer. The study provided new insights into the function of CRs and has important clinical implications for immunotherapy for colon cancer.

Identification and validation of an inflammation-related lncRNAs signature for improving outcomes of patients in colorectal cancer

Background: Colorectal cancer is the fourth most deadly cancer worldwide. Although current treatment regimens have prolonged the survival of patients, the prognosis is still unsatisfactory. Inflammation and lncRNAs are closely related to tumor occurrence and development in CRC. Therefore, it is necessary to establish a new prognostic signature based on inflammation-related lncRNAs to improve the prognosis of patients with CRC. Methods: LASSO-penalized Cox analysis was performed to construct a prognostic signature. Kaplan-Meier curves were used for survival analysis and ROC curves were used to measure the performance of the signature. Functional enrichment analysis was conducted to reveal the biological significance of the signature. The R package "maftool" and GISTIC2.0 algorithm were performed for analysis and visualization of genomic variations. The R package "pRRophetic", CMap analysis and submap analysis were performed to predict response to chemotherapy and immunotherapy. Results: An effective and independent prognostic signature, IRLncSig, was constructed based on sixteen inflammation-related lncRNAs. The IRLncSig was proved to be an independent prognostic indicator in CRC and was superior to clinical variables and the other four published signatures. The nomograms were constructed based on inflammation-related lncRNAs and detected by calibration curves. All samples were classified into two groups according to the median value, and we found frequent mutations of the TP53 gene in the high-risk group. We also found some significantly amplificated regions in the high-risk group, 8q24.3, 20q12, 8q22.3, and 20q13.2, which may regulate the inflammatory activity of cancer cells in CRC. Finally, we identified chemotherapeutic agents for high-risk patients and found that these patients were more likely to respond to immunotherapy, especially anti-CTLA4 therapy. Conclusion: In short, we constructed a new signature based on sixteen inflammation-related lncRNAs to improve the outcomes of patients in CRC. Our findings have proved that the IRLncSig can be used as an effective and independent marker for predicting the survival of patients with CRC.

Mechanistic and functional extrapolation of SET and MYND domain-containing protein 2 to pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal neoplasms worldwide and represents the vast majority of pancreatic cancer cases. Understanding the molecular pathogenesis and the underlying mechanisms involved in the initiation, maintenance, and progression of PDAC is an urgent need, which may lead to the development of novel therapeutic strategies against this deadly cancer. Here, we review the role of SET and MYND domain-containing protein 2 (SMYD2) in initiating and maintaining PDAC development through methylating multiple tumor suppressors and oncogenic proteins. Given the broad substrate specificity of SMYD2 and its involvement in diverse oncogenic signaling pathways in many other cancers, the mechanistic extrapolation of SMYD2 from these cancers to PDAC may allow for developing new hypotheses about the mechanisms driving PDAC tumor growth and metastasis, supporting a proposition that targeting SMYD2 could be a powerful strategy for the prevention and treatment of PDAC.

LIMK1: A promising prognostic and immune infiltration indicator in colorectal cancer

Studies have shown that LIM domain kinase 1 (LIMK1) is upregulated in a variety of tumors and may be a potential detection target. The present study analyzed the expression difference of LIMK1 and its relationship with tumor clinicopathological characteristics and tumor microenvironment in colorectal cancer (CRC). The transcriptomic data of LIMK1 with CRC were downloaded from The Cancer Genome Atlas (TCGA) database and GEO databases for analyzing the expression of LIMK1 mRNA and the correlation with the prognosis of patients. The protein expression of LIMK1 was obtained from the Human Protein Atlas. The receiver operating characteristic (ROC) curve and Kaplan-Meier was used to evaluate the expression characteristics and prognostic differences of LIMK1 in CRC. STRING was used to analyze co-expression genes of LIMK1. The tumor immune estimation resource was applied to the correlation between LIMK1 expression and immune infiltrates. The present study verified LIMK1 expression at the level of clinical samples collected from the Tianjin Medical University General Hospital and cell lines using reverse transcription-quantitative PCR. The mRNA and protein expression of LIMK1 were both upregulated in tumor tissues compared with adjacent tissues in CRC. The expression levels of LIMK1 were positively associated with clinical-pathological features of CRC including lymphatic invasion (P=4.00×10⁻²) and high pathologic stages (P=4.20×10⁻²). The AUC value of LIMK1 in CRC was 0.937 (95% CI: 0.918-0.957) through ROC analysis. Under the best cut-off value (4.009), the sensitivity and specificity were 98 and 81.9%. LIMK1 expression was mainly related to CD4⁺ T cells, macrophages and dendritic cells in the immune microenvironment of CRC. In conclusion, the high expression of LIMK1 in CRC was closely related to the clinical features and prognosis of patients. Therefore, LIMK1 was a promising prognostic indicator and a potential target for immunotherapy in CRC.

Construction of a Novel Immune-Related mRNA Signature to Predict the Prognosis and Immune Characteristics of Human Colorectal Cancer

Background: Anti-cancer immunotherapeutic approaches have gained significant efficacy in multiple cancer types. However, not all patients with colorectal cancer (CRC) could benefit from immunotherapy due to tumor heterogeneity. The purpose of this study was to construct an immune-related signature for predicting the immune characteristics and prognosis of CRC.

Methods: RNA-sequencing data and corresponding clinical information of patients with CRC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal (ImmPort). Then, we utilized univariate, lasso regression, and multivariate cox regression to identify prognostic IRGs and develop the immune-related signature. Subsequently, a nomogram was established based on the signature and other prognostic factors, and its predictive capacity was assessed by receiver operating characteristic (ROC) and decision curve analysis (DCA). Finally, associations between the signature and the immune characteristics of CRC were assessed.

Results: In total, 472 samples downloaded from TCGA were divided into the training cohort (236 samples) and internal validation cohort (236 samples), and the GEO cohort was downloaded as an external validation cohort (122 samples). A total of 476 differently expressed IRGs were identified, 17 of which were significantly correlated to the prognosis of CRC patients. Finally, 10 IRGs were filtered out to construct the risk score signature, and patients were divided into low- and high-risk groups according to the median of risk scores in the training cohort. The high-risk score was significantly correlated with unfavorable survival outcomes and aggressive clinicopathological characteristics in CRC patients, and the results were further confirmed in the internal validation cohort, entire TCGA cohort, and external validation cohort. Immune infiltration analysis revealed that patients in the low-risk group infiltrated with high tumor-infiltrating immune cell (TIIC) abundances compared to the high-risk group. Moreover, we also found that the immune checkpoint biomarkers were significantly overexpressed in the low-risk group.

Conclusion: The prognostic signature established by IRGs showed a promising clinical value for predicting the prognosis and immune characteristics of human CRC, which contribute to individualized treatment decisions.

A novel immune-related gene signature predicting survival in sarcoma patients

Sarcomas are a heterogeneous group of rare mesenchymal tumors. The migration of immune cells into these tumors and the prognostic impact of tumor-specific factors determining their interaction with these tumors remain poorly understood. The current risk stratification system is insufficient to provide a precise survival prediction and treatment response. Thus, valid prognostic models are needed to guide treatment. This study analyzed the gene expression and outcome of 980 sarcoma patients from seven public datasets. The abundance of immune cells and the response to immunotherapy was calculated. Immune-related genes (IRGs) were screened through a weighted gene co-expression network analysis (WGCNA). A least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a powerful IRG signature predicting prognosis. The identified IRG signature incorporated 14 genes and identified high-risk patients in sarcoma cohorts. The 14-IRG signature was identified as an independent risk factor for overall and disease-free survival. Moreover, the IRG signature acted as a potential indicator for immunotherapy. The nomogram based on the risk score was built to provide a more accurate survival prediction. The decision tree with IRG risk score discriminated risk subgroups powerfully. This proposed IRG signature is a robust biomarker to predict outcomes and treatment responses in sarcoma 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.

Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer

Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients.

Identification of long non-coding RNA (lncRNA) signatures could be used to improve cancer clinical outcome. Here the authors developed a machine learning-based integrative procedure to construct a consensus immune-related lncRNA signature to predict prognosis, recurrence and treatment benefits in colorectal cancer.

MAPK Activated Protein Kinase 3 Is a Prognostic-Related Biomarker and Associated With Immune Infiltrates in Glioma

Glioma is the most common primary brain tumor that causes significant morbidity and mortality. MAPK activated protein kinase 3 (MAPKAPK3/MK3) is a serine/threonine protein kinase regulating various cellular responses and gene expression. However, the role of MK3 in tumor progress, prognosis, and immunity for glioma remains unclear. Here, we determined the expression and prognostic values of MK3. We further analyzed the correlation of MK3 expression with immune infiltrations by using the biochemical methods and bioinformatic approaches with available databases. We find that MK3 is aberrantly upregulated in glioma. In addition, the higher MK3 expression is closely linked to the poor clinicopathologic features of glioma patients. Importantly, MK3 expression is negatively correlated with the prognosis of patients with glioma. Mechanistically, we demonstrated that the correlated genes of MK3 were mainly enriched in pathways that regulate tumor immune responses. The MK3 level was significantly associated with tumor-infiltrating immune cells and positively correlated with the majority of tumor immunoinhibitors, chemokines, and chemokine receptors in glioma. Thus, these findings suggest the novel prognostic roles of MK3 and define MK3 as a promising target for glioma immunotherapy.

Construction and validation of a metabolic risk model predicting prognosis of colon cancer

Metabolic genes have played a significant role in tumor development and prognosis. In this study, we constructed a metabolic risk model to predict the prognosis of colon cancer based on The Cancer Genome Atlas (TCGA) and validated the model by Gene Expression Omnibus (GEO). We extracted 753 metabolic genes and identified 139 differentially expressed genes (DEGs) from TCGA database. Then we conducted univariate cox regression analysis and Least Absolute Shrinkage and Selection Operator Cox regression analysis to identify prognosis-related genes and construct the metabolic risk model. An eleven-gene prognostic model was constructed after 1000 resamples. The gene signature has been proved to have an excellent ability to predict prognosis by Kaplan-Meier analysis, time-dependent receiver operating characteristic, risk score, univariate and multivariate cox regression analysis based on TCGA. Then we validated the model by Kaplan-Meier analysis and risk score based on GEO database. Finally, we performed a weighted gene co-expression network analysis and protein-protein interaction network on DEGs, and Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology enrichment analyses were conducted. The results of functional analyses showed that most significantly enriched pathways focused on metabolism, especially glucose and lipid metabolism pathways.