Weighted Gene Coexpression Network Analysis of Features That Control Cancer Stem Cells Reveals Prognostic Biomarkers in Lung Adenocarcinoma

Targeting BMP2 for therapeutic strategies against hepatocellular carcinoma

OBJECTIVES

This study aimed to investigate the role of BMP2 in hepatocellular carcinoma (HCC) growth and metastasis using a dual approach combining single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq.

METHODS

scRNA-seq data from the GEO database and bulk RNA-seq data from the TCGA database were analyzed. Differentially expressed marker genes of endothelial cells were identified and analyzed using enrichment analysis, PPI analysis, correlation analysis, and GSEA. In vitro, experiments were conducted using the Huh-7 HCC cell line, and in vivo, models of HCC growth and metastasis were established by knocking down BMP2.

RESULTS

The scRNA-seq analysis identified BMP2 as a key marker gene in endothelial cells of HCC samples. Elevated BMP2 expression correlated with poor prognosis in HCC. In vitro experiments showed that silencing BMP2 inhibited the proliferation, migration, and invasion of liver cancer cells. In vivo studies confirmed increased BMP2 expression in HCC tissues, promoting angiogenesis and HCC growth.

CONCLUSION

This study highlights the role of BMP2 in tumor angiogenesis and HCC progression. Targeting BMP2 could be a promising therapeutic strategy against HCC.

The Tumor Stemness Indice mRNAsi can Act as Molecular Typing Tool for Lung Adenocarcinoma

Due to the high heterogeneity, lung adenocarcinoma (LUAD) cannot be distinguished into precise molecular subtypes, thereby resulting in poor therapeutic effect and low 5-year survival rate clinically. Although the tumor stemness score (mRNAsi) has been shown to accurately characterize the similarity index of cancer stem cells (CSCs), whether mRNAsi can serve as an effective molecular typing tool for LUAD isn't reported to date. In this study, we first demonstrate that mRNAsi is significantly correlated with the prognosis and disease degree of LUAD patients, i.e., the higher the mRNAsi, the worse the prognosis and the higher the disease degree. Second, we identify 449 mRNAsi-related genes based on both weighted gene co-expression network analysis (WGCNA) and univariate regression analysis. Third, our results display that 449 mRNAsi-related genes can accurately distinguish the LUAD patients into two molecular subtypes: ms-H subtype (with high mRNAsi) and ms-L subtype (with low mRNAsi), particularly the ms-H subtype has a worse prognosis. Remarkably, significant differences in clinical characteristics, immune microenvironment, and somatic mutation exist between the two molecular subtypes, which might lead to the poorer prognosis of the ms-H subtype patients than that of the ms-L subtype ones. Finally, we establish a prognostic model containing 8 mRNAsi-related genes, which can effectively predict the survival rate of LUAD patients. Taken together, our work provides the first molecular subtype related to mRNAsi in LUAD, and reveals that these two molecular subtypes, the prognostic model and marker genes may have important clinical value for effectively monitoring and treating LUAD patients.

Identification of molecular subtypes and prognostic model to reveal immune infiltration and predict prognosis based on immunogenic cell death-related genes in lung adenocarcinoma

Immunogenic cell death (ICD) has been increasingly indicated to be related to caners. However, ICD's role in Lung adenocarcinoma (LUAD) is still not well investigated. Clinical data along with associated mRNA expression profiles from LUAD cases were collected in TCGA and GEO databases. 13 ICD-related genes were identified. Relations of ICD-related genes expression with prognosis of patients, tumor immune microenvironment (TIME) was analyzed. Then, candidate genes were identified and the prognostic signature were constructed. Afterwards, one nomogram incorporating those chosen clinical data together with risk scores were built. Finally, the effect of HSP90AA1, one gene of the prognostic signature, on LUAD cell were analyzed. Two clusters were identified, which were designated as the ICD-high or -low subtype according to ICD-related genes levels. ICD-high subgroup showed good prognosis, high immune cell infiltration degrees, and enhanced immune response signaling activity compared with ICD-low subtype. Moreover, we established and verified the risk signature based on ICD-related genes. High risk group predicted poor prognosis of LUAD independently and presented negative association with immune score and immune status. Furthermore, nomogram contributed to the accurate prediction of LUAD prognostic outcome. Finally, HSP90AA1 levels were remarkably elevated within tumor cells in comparison with healthy pulmonary epithelial cells. HSP90α, HSP90AA1 protein product, promoted growth, migration, and invasion of LUAD cells. Molecular subtypes and prognostic model were identified by incorporating ICD-related genes, and it was related to TIME and might be adopted for the accurate prediction of LUAD prognosis.

Identification of disulfidptosis-related subtypes and development of a prognosis model based on stacking framework in renal clear cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor with an unsatisfactory prognosis. This study aims to identify the expression patterns of disulfidptosis-related genes (DRGs), develop a prognostic model, and predict immunological profiles.

METHODS

First, we identified differentially expressed DRGs in TCGA-KIRC cohort and analyzed their mutational profiles, methylation levels, and interaction networks. Subsequently, we identified disulfidptosis-associated molecular subtypes and investigated their prognostic and immunological characteristics. Simultaneously, a disulfidptosis-related prognostic signature (DRPS) was developed using a two-stage stacking framework consisting of 5 machine learning models. The effect of DRPS on immune cell infiltration levels was explored using seven different algorithms, and the status and function of T cells for distinct risk-score groups were evaluated based on T cell exhaustion and dysfunction scores. Additionally, the study also examined differences in clinical characteristics and therapy efficacy between high- and low-risk groups.

RESULTS

We found two disulfidptosis-associated clusters, one of which had a poor prognosis and was linked to high immune cell infiltration but impaired T cell function. DRPS showed excellent predictive performance in all four cohorts and could accurately identified disulfidptosis-related molecular subtypes. The DRPS-based risk score was positively associated with poor prognosis, malignant pathological features, high immune cell infiltration levels, and T cell exhaustion or dysfunction, and better respond to immunotherapy and targeted therapy. Additionally, we have identified a close association between ISG20 and disulfidptosis as well as tumor immunity.

CONCLUSION

Our study identified distinct disulfidptosis-related subtypes in ccRCC patients, and constructed the highly accurate and robust DRPS based on an ensemble learning framework, which has critical reference value in clinical decision-making and individualized treatment. And this work also revealed ISG20 exhibits promising potential as a therapeutic target for ccRCC.

Ubiquitin-specific peptidase 5 facilitates cancer stem cell-like properties in lung cancer by deubiquitinating β-catenin

BACKGROUND

Lung cancer has the highest mortality rate in the world, and mounting evidence suggests that cancer stem cells (CSCs) are associated with poor prognosis, recurrence, and metastasis of lung cancer. It is urgent to identify new biomarkers and therapeutic targets for targeting lung CSCs.

METHODS

We computed the single-sample gene set enrichment analysis (ssGSEA) of 1554 Reactome gene sets to identify the mRNA expression-based stemness index (mRNAsi)-associated pathways using the genome-wide RNA sequencing data of 509 patients from The Cancer Genome Atlas (TCGA) cohort of lung adenocarcinoma (LUAD). Phenotypic effects of ubiquitin-specific peptidase 5 (USP5) on the CSC-like properties and metastasis were examined by in vitro sphere formation assay, migration assay, invasion assay, and in vivo xenografted animal models. Cycloheximide chase assay, co-immunoprecipitation assay, and deubiquitination assay were performed to confirm the effect of USP5 on the deubiquitination of β-catenin.

RESULTS

We demonstrated that USP5 expression were positively correlated with the stemness-associated signatures and poor outcomes in lung cancer specimens. Silencing of endogenous USP5 reduced CSC-like characteristics, epithelial-mesenchymal transition (EMT), and metastasis in vitro and in vivo. Furthermore, USP5 interacted with β-catenin, which resulted in deubiquitination, stabilization of β-catenin, and activation of Wnt/β-catenin pathway. Accordingly, expression of USP5 was positively correlated with the enrichment score of the Wnt/TCF pathway signature in human lung cancer. Silencing of β-catenin expression suppressed USP5-enhancing sphere formation. Targeting USP5 with the small molecule WP1130 promoted the degradation of β-catenin, and showed great inhibitory effects on sphere formation, migration, and invasion. Finally, we identified a poor-prognosis subset of tumors characterized by high levels of USP5, Wnt signaling score, and Stemness score in both TCGA-LUAD and Rousseaux_2013 datasets.

CONCLUSIONS

These findings reveal a clinical evidence for USP5-enhanced Wnt/β-catenin signaling in promoting lung cancer stemness and metastasis, implying that targeting USP5 could provide beneficial effects to improve lung cancer therapeutics.

Comprehensive analysis of necroptosis-related lncRNA signature with potential implications in tumor heterogeneity and prediction of prognosis in clear cell renal cell carcinoma

BACKGROUND

Necroptosis has been reported to play a critical role in occurrence and progression of cancer. The dysregulation of long non-coding RNAs (lncRNAs) is associated with the progression and metastasis of clear cell renal cell carcinoma (CCRCC). However, research on necroptosis-related lncRNAs in the tumor heterogeneity and prognosis of CCRCC is not completely unclear. This study aimed to analysis the tumor heterogeneity among CCRCC subgroups and construct a CCRCC prognostic signature based on necroptosis-related lncRNAs.

METHODS

Weighted gene co-expression network analysis (WGCNA) was performed to identify necroptosis-related lncRNAs. A preliminary classification of molecular subgroups was performed by non-negative matrix factorization (NMF) consensus clustering analysis. Comprehensive analyses, including fraction genome altered (FGA), tumor mutational burden (TMB), DNA methylation alterations, copy number variations (CNVs), and single nucleotide polymorphisms (SNPs), were performed to explore the potential factors for tumor heterogeneity among the three subgroups. Subsequently, we constructed a predictive signature by multivariate Cox regression. Nomogram, calibration curves, decision curve analysis (DCA), and time-dependent receiver-operating characteristics (ROC) were used to validate and evaluate the signature. Finally, immune correlation analyses, including immune-related signaling pathways, immune cell infiltration status and immune checkpoint gene expression level, were also performed.

RESULTS

Seven necroptosis-related lncRNAs were screened out by WGCNA, and three subgroups were classified by NMF consensus clustering analysis. There were significant differences in survival prognosis, clinicopathological characteristics, enrichments of immune-related signaling pathway, degree of immune cell infiltration, and expression of immune checkpoint genes in the various subgroups. Most importantly, we found that 26 differentially expressed genes (DEGs) among the 3 subgroups were not affected by DNA methylation alterations, CNVs and SNPs. On the contrary, these DEGs were associated with the seven necroptosis-related lncRNAs. Subsequently, the identified RP11-133F8.2 and RP11-283G6.4 by multivariate Cox regression analysis were involved in the risk model, which could serve as an independent prognostic factor for CCRCC. Finally, qRT-PCR confirmed the differential expression of the two lncRNAs.

CONCLUSIONS

These findings contributed to understanding the function of necroptosis-related lncRNAs in CCRCC and provided new insights of prognostic evaluation and optimal therapeutic strategy for CCRCC.

Identification of key genes in colorectal cancer diagnosis by co-expression analysis weighted gene co-expression network analysis

BACKGROUND

The purpose of this study was using bioinformatics tools to identify biomarkers and molecular factors involved in the diagnosis of colorectal cancer, which are effective for the diagnosis and treatment of the disease.

METHODS

We determined differentially expressed genes (DEGs) related to colorectal cancer (CRC) using the data series retrieved from GEO database. Then the weighted gene co-expression network analysis (WGCNA) was conducted to explore co-expression modules related to CRC diagnosis. Next, the relationship between the integrated modules with clinical features such as the stage of CRC was evaluated. Other downstream analyses were performed on selected module genes.

RESULTS

In this study, after performing the WGCNA method, a module named blue module which was more significantly associated with the CRC stage was selected for further evaluation. Afterward, the Protein-protein interaction network through sting software for 154 genes of the blue module was constructed and eight hub genes were identified through the evaluation of constructed network with Cytoscape. Among these eight hub genes, upregulation of MMP9, SERPINH1, COL1A2, COL5A2, COL1A1, SPARC, and COL5A1 in CRC was validated in other microarray and TCGA data. Based on the results of the mRNA-miRNA interaction network, SERPINH1 was found as a target gene of miR-940. Finally, results of the DGIDB database indicated that Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, and Bevacizumab, could be used as a therapeutic agent for targeting the MMP9. Furthermore, Ocriplasmin and Collagenase clostridium histolyticum could target COL1A1, COL1A2, COL5A1, and COL5A2.

CONCLUSION

Taken together, the results of the current study indicated that seven hub genes including COL1A2, COL5A1, COL5A2, SERPINH1, MMP9, SPARC, and COL1A1 which were upregulated in CRC could be used as a diagnostic and progression biomarker of CRC. On the other hand, miR-940 which targets SERPINH1 could be used as a potential biomarker of CRC. More ever, Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, Bevacizumab, Ocriplasmin , and Collagenase clostridium histolyticum were introduced as therapeutic agents for CRC which their therapeutic potential should be evaluated experimentally.

Bioinformatics construction and experimental validation of a cuproptosis-related lncRNA prognostic model in lung adenocarcinoma for immunotherapy response prediction

Cuproptosis is a newly form of cell death. Cuproptosis related lncRNA in lung adenocarcinoma (LUAD) has also not been fully elucidated. In the present study, we aimed to construct a prognostic signature based on cuproptosis-related lncRNA in LUAD and investigate its association with immunotherapy response. The RNA-sequencing data, clinical information and simple nucleotide variation of LUAD patients were obtained from TCGA database. The LASSO Cox regression was used to construct a prognostic signature. The CIBERSORT, ESTIMATE and ssGSEA algorithms were applied to assess the association between risk score and TME. TIDE score was applied to reflect the efficiency of immunotherapy response. The influence of overexpression of lncRNA TMPO-AS1 on A549 cell was also assessed by in vitro experiments. The lncRNA prognostic signature included AL606834.1, AL138778.1, AP000302.1, AC007384.1, AL161431.1, TMPO-AS1 and KIAA1671-AS1. Low-risk group exhibited much higher immune score, stromal score and ESTIMATE score, but lower tumor purity compared with high-risk groups. Also, low-risk group was associated with a much higher score of immune cells and immune related function sets, indicating an immune activation state. Low-risk patients had relative higher TIDE score and lower TMB. External validation using IMvigor210 immunotherapy cohort demonstrated that low-risk group had a better prognosis and might more easily benefit from immunotherapy. Overexpression of lncRNA TMPO-AS1 promoted the proliferation, migration and invasion of A549 cell line. The novel cuproptosis-related lncRNA signature could predict the prognosis of LUAD patients, and helped clinicians stratify patients appropriate for immunotherapy and determine individual therapeutic strategies.

Structure of co-expression networks of Bifidobacterium species in response to human milk oligosaccharides

Biological systems respond to environmental perturbations and a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. Experimental information from transcriptomic studies has allowed the identification of gene networks that contribute to our understanding of microbial adaptations. In this study, we analyzed the gene co-expression networks of three Bifidobacterium species in response to different types of human milk oligosaccharides (HMO) using weighted gene co-expression analysis (WGCNA). RNA-seq data obtained from Geo Datasets were obtained for Bifidobacterium longum subsp. Infantis, Bifidobacterium bifidum and Bifidobacterium longum subsp. Longum. Between 10 and 20 co-expressing modules were obtained for each dataset. HMO-associated genes appeared in the modules with more genes for B. infantis and B. bifidum, in contrast with B. longum. Hub genes were identified in each module, and in general they participated in conserved essential processes. Certain modules were differentially enriched with LacI-like transcription factors, and others with certain metabolic pathways such as the biosynthesis of secondary metabolites. The three Bifidobacterium transcriptomes showed distinct regulation patterns for HMO utilization. HMO-associated genes in B. infantis co-expressed in two modules according to their participation in galactose or N-Acetylglucosamine utilization. Instead, B. bifidum showed a less structured co-expression of genes participating in HMO utilization. Finally, this category of genes in B. longum clustered in a small module, indicating a lack of co-expression with main cell processes and suggesting a recent acquisition. This study highlights distinct co-expression architectures in these bifidobacterial genomes during HMO consumption, and contributes to understanding gene regulation and co-expression in these species of the gut microbiome.

Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach

Although systemic chemotherapy remains the standard of care for TNBC, even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC PDXs to identify biomarkers of response. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.

Weighted Gene Co-Expression Network Analysis of Oxymatrine in Psoriasis Treatment

Purpose

Psoriasis is a common, chronic, inflammatory, recurrent, immune-mediated skin disease. Oxymatrine is effective for treating moderate and severe psoriasis. Here, transcriptional changes in skin lesions before and after oxymatrine treatment of patients with psoriasis were identified using full-length transcriptome analysis and then compared with those of normal skin tissues.

Patients and Methods

Co-expression modules were constructed by combining the psoriasis area and severity index (PASI) score with weighted gene co-expression network analysis to explore the action mechanism of oxymatrine in improving clinical PASI. The expression of selected genes was verified using immunohistochemistry, quantitative real-time PCR, and Western blotting.

Results

Kyoto Encyclopedia of Gene and Genome pathway analysis revealed that oxymatrine treatment reversed the abnormal pathways, with an improvement in lesions and a reduction in PASI scores. Gene Ontology (GO) analysis revealed that oxymatrine treatment led to altered GO terms being regulated with a decrease in the PASI score in patients. Therefore, oxymatrine treatment may improve the skin barrier, differentiation of keratinocytes, and alleviate abnormality of organelles such as desmosomes. Protein-protein interaction network interaction analysis revealed that the top five hub genes among many interrelated genes were CNFN, S100A8, SPRR2A, SPRR2D, and SPRR2E, associated with the epidermal differentiation complex (EDC). EDC regulates keratinocyte differentiation. This result indicates that oxymatrine treatment can restore keratinocyte differentiation by regulating the expression of EDC-related genes.

Conclusion

Oxymatrine can improve erythema, scales, and other clinical symptoms of patients with psoriasis by regulating EDC-related genes and multiple pathways, thereby promoting the repair of epithelial tissue and maintaining the dynamic balance of skin keratosis.

Activation of MYO1G by lncRNA MNX1-AS1 Drives the Progression in Lung Cancer

Lung cancer represents the most prevalent cancer worldwide and causes the death of many patients. Cancer stem cells (CSCs), a subpopulation of cancer cells, have the capacities of self-renewal, unlimited proliferation, and multiple differentiation potential. The purpose of this study was to explore the potential role of long noncoding RNA (lncRNA) MNX1-AS1 on maintaining the stemness of CSC in lung cancer. CSCs were firstly sorted by flow cytometry. After the determination of the target of the present study using Gene Expression Omnibus dataset, MNX1-AS1was found to be highly expressed in lung cancer tissues and cells. Deletion of MNX1-AS1 inhibited proliferation, migration, invasion and sphere-forming abilities of CSC. Furthermore, subcellular fractionation, fluorescence in situ hybridization, RNA immunoprecipitation, and dual-luciferase experiments demonstrated that MNX1-AS1 recruited the transcription factor POU domain class 2 transcription factor 2 (POU2F2) to the nucleus and activated the myosin IG (MYO1G) expression. MYO1G overexpression partially reversed the si-MNX1-AS1-decreased stemness of CSCs. Finally, MNX1-AS1 suppression significantly repressed the growth of xenografts in vivo. Our study highlights the importance of the MNX1-AS1/POU2F2/MYO1G axis in stem cell-like properties of lung cancer cells.

Exploring novel independent prognostic biomarkers for hepatocellular carcinoma based on TCGA and GEO databases

BACKGROUND

Hepatocellular carcinoma (HCC) has become the fifth most common cancer globally, with the second-highest mortality rate and poor survival outcomes. In our research, we aimed to use The Cancer Genome Atlas and gene expression omnibus databases to identify potential genetic biomarkers to predict and improve the survival rate of HCC patients.

METHODS

In GSE60502, GSE76427, and GSE84402, we performed differential expression analysis to obtain differentially expressed genes (DEGs). In the The Cancer Genome Atlas database, the FPKM expression profile was subjected to weighted gene co-expression analysis to obtain modules closely related to HCC. We received common genes by intersecting the genes in the module with the differential genes. Then, we fused the common genes' expression profiles, survival time, and survival status for univariate, Least Absolute Shrinkage and Selection Operator, and multivariate COX regression analysis to obtain prognostic genes. Predictive genes were performed in K-M survival analysis and combined with clinical data for independent predictive analysis.

RESULTS

After differential expression analysis, GSE60502 obtained 1107 DEGs, GSE76427 obtained 424 DEGs, and GSE84402 obtained 1668 DEGs. Through weighted gene co-expression analysis analysis, we can see that the blue and brown modules were closely associated with HCC. After single and multivariate COX regression analysis, we found that suppressor of cytokine signaling 2 (SOCS2) and SERPINF2 were independent prognostic genes for HCC. After survival analysis, HCC patients with high expression of SOCS2 and SERPINF2 had a longer survival time. These 2 genes in normal liver tissues were higher than in HCC at the transcriptional level.

CONCLUSION

SOCS2 and SERPINF2 were new independent prognostic genes of HCC. So, they may provide new treatment methods and measures for diagnosing HCC.

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer.

Immune- and Stemness-Related Genes Revealed by Comprehensive Analysis and Validation for Cancer Immunity and Prognosis and Its Nomogram in Lung Adenocarcinoma

Objective

Lung adenocarcinoma (LUAD) is a familiar lung cancer with a very poor prognosis. This study investigated the immune- and stemness-related genes to develop model related with cancer immunity and prognosis in LUAD.

Method

The Cancer Genome Atlas (TCGA) was utilized for obtaining original transcriptome data and clinical information. Differential expression, prognostic value, and correlation with clinic parameter of mRNA stemness index (mRNAsi) were conducted in LUAD. Significant mRNAsi-related module and hub genes were screened using weighted gene coexpression network analysis (WGCNA). Meanwhile, immune-related differential genes (IRGs) were screened in LUAD. Stem cell index and immune-related differential genes (SC-IRGs) were screened and further developed to construct prognosis-related model and nomogram. Comprehensive analysis of hub genes and subgroups, involving enrichment in the subgroup [gene set enrichment analysis (GSEA)], gene mutation, genetic correlation, gene expression, immune, tumor mutation burden (TMB), and drug sensitivity, used bioinformatics and reverse transcription polymerase chain reaction (RT-PCR) for verification.

Results

Through difference analysis, mRNAsi of LUAD group was markedly higher than that of normal group. Clinical parameters (age, gender, and T staging) were ascertained to be highly relevant to mRNAsi. MEturquoise and MEblue were found to be the most significant modules (including positive and negative correlations) related to mRNAsi via WGCNA. The functions and pathways of the two mRNAsi-related modules were mainly enriched in tumorigenesis, development, and metastasis. Combining stem cell index–related differential genes and immune-related differential genes, 30 prognosis-related SC-IRGs were screened via Cox regression analysis. Then, 16 prognosis-related SC-IRGs were screened to construct a LASSO regression model at last. In addition, the model was successfully validated by using TCGA-LUAD and GSE68465, whereas c-index and the calibration curves were utilized to demonstrate the clinical value of our nomogram. Following the validation of the model, GSEA, immune cell correlation, TMB, clinical relevance, etc., have found significant difference in high- and low-risk groups, and 16-gene expression of the SC-IRG model also was tested by RT-PCR. ADRB2, ANGPTL4, BDNF, CBLC, CX3CR1, and IL3RA were found markedly different expression between the tumor and normal group.

Conclusion

The SC-IRG model and the prognostic nomogram could accurately predict LUAD survival. Our study used mRNAsi combined with immunity that may lay a foundation for the future research studies in LUAD.

Constructing a Prognostic Gene Signature for Lung Adenocarcinoma Based on Weighted Gene Co-Expression Network Analysis and Single-Cell Analysis

Purpose

Lung adenocarcinoma (LUAD) has a high degree of intratumor heterogeneity. Advanced single-cell RNA sequencing (scRNA-seq) technologies have offered tools to analyze intratumor heterogeneity, which improves the accuracy of identifying biomarkers based on single-cell expression data, and thus helps in predicting prognosis of cancer patients and assisting decision-makings for cancer treatment.

Patients and Methods

ScRNA-seq data containing two LUAD and two para-cancerous tissue samples were included to identify different cell clusters in tumor tissues. To identify the most relevant modules and important cell subpopulations (clusters) in LUAD tissues, weighted gene co-expression network analysis (WGCNA) was performed. Subsequently, LUAD molecular subtypes were constructed by unsupervised consensus clustering based on genes in key modules. Using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic model of LUAD was established.

Results

A total of 14 cell clusters belonging to 10 cell types in LUAD were identified. The turquoise module was the most relevant to LUAD among all the modules; cluster 10 (C10, lung epithelial cells) was found to be the most strongly associated with the turquoise module. LUAD samples were divided into two groups of distinct molecular subtypes. Based on the 165 shared genes between the turquoise module and C10, 511 DEGs between the two molecular subtypes were obtained, and five of them were selected to construct the gene signature, which was validated to be an independent prognostic marker of LUAD.

Conclusion

Fourteen cell clusters co-existed in LUAD, which contributed to its intratumor heterogeneity. Two molecular subtypes of LUAD were identified and a five-gene signature was developed and validated to be significantly associated with prognostic and clinical characteristics of LUAD patients.

Integrative Analysis Revealed Stemness Features and a Novel Stemness-Related Classification in Colorectal Cancer Patients

Cancer stem cells play crucial roles in colorectal cancer (CRC) tumorigenesis and treatment response. This study aimed to determine the value of the mRNA stemness index (mRNAsi) in CRC and introduce a stemness-related classification to predict the outcome of patients. mRNAsi scores and RNA sequence data of CRC patients were analyzed. We found that high mRNAsi scores were related to early-stage CRC and a better patient prognosis. Two stemness-based subtypes (subtype I and II) were identified. Patients in subtype I presented a significantly better prognosis than those in subtype II. Patients in these two subtype groups presented significantly different tumor immunity scores and immune cell infiltration patterns. Genomic variations revealed that patients in subtype I had a lower tumor mutation burden than those in subtype II. A three-gene stemness subtype predictor was established, showing good diagnostic value in discriminating patients in different subtypes. A prognostic signature based on five stemness-related genes was established and validated in two independent cohorts and clinical samples, showing a better predictive performance than other clinical parameters. We concluded that mRNAsi scores were associated with the clinical outcome in CRC patients. The stemness-related classification was a promising prognostic predictor for CRC patients.

Chromobox Homologue 7 Acts as a Tumor Suppressor in Both Lung Adenocarcinoma and Lung Squamous Cell Carcinoma via Inhibiting ERK/MAPK Signaling Pathway

Chromobox homologue 7 (CBX7) is a member of the polycomb group family that plays a pivotal role in regulating cellular processes in human cancers. This study aims to explore the function and underlying molecular mechanisms of CBX7 in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). The expression of CBX7 in LUAD and LUSC tissues was analyzed by UALCAN and GEPIA based on the TCGA database. Cell viability and apoptosis were measured by CCK-8 and flow cytometry assays, respectively. Cell migration and invasion were detected by transwell assay. The functions of downregulated genes in LUAD were enriched via GO and KEGG pathway analyses. The mRNA expression of CBX7, ERK1/2, and p38 was determined by qRT-PCR, and the protein levels of CBX7, ERK1/2, p-ERK1/2, p38, and p-p38 were measured by Western blotting. Tumor xenograft model was established to validate the antitumor effect of CBX7. The expression of CBX7 and Ki-67 in tumor tissues was detected by immunohistochemistry. CBX7 was downregulated in the tissues and cells of both LUAD and LUSC. Low CBX7 expression was associated with a poor overall survival rate in LUAD patients. CBX7 overexpression inhibited the viability, migration, and invasion and promoted the apoptosis of LUAD and LUSC cells. In addition, the downregulated genes in LUAD were enriched in MAPK cascade (GO) and MAPK signaling pathway (KEGG). ERK/MAPK pathway was then determined as a downstream target of CBX7, which was inhibited by CBX7 overexpression in LUAD and LUSC cells. The overexpression of CBX7 inhibited the malignant progression of LUAD and LUSC cells probably via suppressing the ERK/MAPK signaling pathway in vitro and in vivo.

Genomic Analysis Reveals the Prognostic and Immunotherapeutic Response Characteristics of Ferroptosis in Lung Squamous Cell Carcinoma

INTRODUCTION

Recent studies have reported that ferroptosis is an iron-dependent cell death process and is a potential therapeutic target in various tumours. The purpose of this study was to establish a new algorithm based on the ferroptosis score to ascertain the prognosis and response to immunotherapy of patients with lung squamous cell carcinoma (LUSC).

METHODS

The RNA-seq data of patients with LUSC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases and merged after removing the inter batch differences. Based on the expression of the ferroptosis-related genes, unsupervised consistent cluster analysis was performed to obtain various ferroptosis-related subgroups. These subgroups were analysed to obtain differentially expressed genes (DEGs). Subsequently, multiple gene clusters were obtained by unsupervised consistent cluster analysis based on the expression of the DEGs. The Boruta algorithm was used to calculate the ferroptosis score.

RESULTS

There were significant differences in prognosis amongst the various ferroptosis-related and gene clusters. In addition, the gene set variation analysis revealed that the different ferroptosis-related clusters and gene clusters demonstrated differences in biological pathways. The ferroptosis scores positively correlated with the tumour mutation burden, and patients with lower scores had a better prognosis. In addition, the ferroptosis score was accurate in predicting the effectiveness of immunotherapy.

CONCLUSION

There were significant differences in the prognosis and immunotherapy response of patients with LUSC with different ferroptosis scores. Therefore, a comprehensive clinical evaluation of the ferroptosis score of each patient with LUSC is clinically significant.

Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the most lethal malignancies and is currently lacking in effective biomarkers to assist in diagnosis and therapy. The aim of this study is to investigate hub genes and develop a risk signature for predicting prognosis of LUAD patients.

METHODS

RNA-sequencing data and relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes associated with mRNA expression-based stemness indices (mRNAsi) in TCGA. We utilized LASSO Cox regression to assemble our predictive model. To validate our predictive model, me applied it to an external cohort.

RESULTS

mRNAsi index was significantly associated with the tissue type of LUAD, and high mRNAsi scores may have a protective influence on survival outcomes seen in LUAD patients. WGCNA indicated that the turquoise module was significantly correlated with the mRNAsi. We identified a 9-gene signature (CENPW, MCM2, STIL, RACGAP1, ASPM, KIF14, ANLN, CDCA8, and PLK1) from the turquoise module that could effectively identify a high-risk subset of these patients. Using the Kaplan-Meier survival curve, as well as the time-dependent receiver operating characteristic (tdROC) analysis, we determined that this gene signature had a strong predictive ability (AUC = 0.716). By combining the 9-gene signature with clinicopathological features, we were able to design a predictive nomogram. Finally, we additionally validated the 9-gene signature using two external cohorts from GEO and the model proved to be of high value.

CONCLUSION

Our study shows that the 9-gene mRNAsi-related signature can predict the prognosis of LUAD patient and contribute to decisions in the treatment and prevention of LUAD patients.

Identification of Common and Distinct Pathways in Inflammatory Bowel Disease and Colorectal Cancer: A Hypothesis Based on Weighted Gene Co-Expression Network Analysis

Patients with inflammatory bowel disease (IBD), including ulcerative colitis and Crohn’s disease, are at higher risk to develop colorectal cancer (CRC). However, the underlying mechanisms of this predisposition remain elusive. We performed in-depth comparative computational analyses to gain new insights, including weighted gene co-expression network analysis (WGCNA) and gene ontology and pathway enrichment analyses, using gene expression datasets from IBD and CRC patients. When individually comparing IBD and CRC to normal control samples, we identified clusters of highly correlated genes, differentially expressed genes, and module-trait associations specific for each disease. When comparing IBD to CRC, we identified common hub genes and commonly enriched pathways. Most notably, IBD and CRC share significantly increased expression of five genes (MMP10, LCN2, REG1A, REG3A, and DUOX2), enriched inflammatory and neutrophil activation pathways and, most notably, highly significant enrichment of IL-4 and IL-13 signaling. Thus, our work expands our knowledge about the intricate relationship between IBD and CRC development and provides new rationales for developing novel therapeutic strategies.

Analyzing and validating the prognostic value and immune microenvironment of clear cell renal cell carcinoma

Tumor immune microenvironment (TIME) plays an important role in tumor diagnosis, prevention, treatment and prognosis. However, the correlation and potential mechanism between clear cell renal cell carcinoma (ccRCC) and its TIME are not clear. Therefore, we aimed to identify potential prognostic biomarkers related to TIME of ccRCC. Unsupervised consensus clustering analysis was performed to divide patients into different immune subgroups according to their single-sample gene set enrichment analysis (ssGSEA) scores. Then, we validated the differences in immune cell infiltration, prognosis, clinical characteristics and expression levels of HLA and immune checkpoint genes between different immune subgroups. Weighted gene coexpression network analysis (WGCNA) was used to identify the significant modules and hub genes that were related to the immune subgroups. A nomogram was established to predict the overall survival (OS) outcomes after independent prognostic factors were identified by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses. Five clusters (immune subgroups) were identified. There was no significant difference in age, sex or N stage. And there were significant differences in race, T stage, M stage, grade, prognosis and tumor microenvironment. WGCNA revealed that the red module has an important relationship with TIME, and obtained 14 hub genes. In addition, the nomogram containing LAG3 and GZMK accurately predicted OS outcomes of ccRCC patients. LAG3 and GZMK have a certain correlation with the prognosis of ccRCC patients, and play an important role in the TIME. These two hub genes deserve further study as biomarkers of the TIME.

Gene Co-Expression in Breast Cancer: A Matter of Distance

Gene regulatory and signaling phenomena are known to be relevant players underlying the establishment of cellular phenotypes. It is also known that such regulatory programs are disrupted in cancer, leading to the onset and development of malignant phenotypes. Gene co-expression matrices have allowed us to compare and analyze complex phenotypes such as breast cancer (BrCa) and their control counterparts. Global co-expression patterns have revealed, for instance, that the highest gene-gene co-expression interactions often occur between genes from the same chromosome (cis-), meanwhile inter-chromosome (trans-) interactions are scarce and have lower correlation values. Furthermore, strength of cis- correlations have been shown to decay with the chromosome distance of gene couples. Despite this loss of long-distance co-expression has been clearly identified, it has been observed only in a small fraction of the whole co-expression landscape, namely the most significant interactions. For that reason, an approach that takes into account the whole interaction set results appealing. In this work, we developed a hybrid method to analyze whole-chromosome Pearson correlation matrices for the four BrCa subtypes (Luminal A, Luminal B, HER2+ and Basal), as well as adjacent normal breast tissue derived matrices. We implemented a systematic method for clustering gene couples, by using eigenvalue spectral decomposition and the k–medoids algorithm, allowing us to determine a number of clusters without removing any interaction. With this method we compared, for each chromosome in the five phenotypes: a) Whether or not the gene-gene co-expression decays with the distance in the breast cancer subtypes b) the chromosome location of cis- clusters of gene couples, and c) whether or not the loss of long-distance co-expression is observed in the whole range of interactions. We found that in the correlation matrix for the control phenotype, positive and negative Pearson correlations deviate from a random null model independently of the distance between couples. Conversely, for all BrCa subtypes, in all chromosomes, positive correlations decay with distance, and negative correlations do not differ from the null model. We also found that BrCa clusters are distance-dependent, meanwhile for the control phenotype, chromosome location does not determine the clustering. To our knowledge, this is the first time that a dependence on distance is reported for gene clusters in breast cancer. Since this method uses the whole cis- interaction geneset, combination with other -omics approaches may provide further evidence to understand in a more integrative fashion, the mechanisms that disrupt gene regulation in cancer.

A novel ferroptosis-related lncRNA signature for prognosis prediction in gastric cancer

Background

Gastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC.

Methods

Ferroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model.

Results

We constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410–2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR.

Conclusions

This risk model was shown to be a novel method for predicting prognosis for GC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08975-2.

Precise pathological classification of non-small cell lung adenocarcinoma and squamous carcinoma based on an integrated platform of targeted metabolome and lipidome

BACKGROUND

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most common subtypes of NSCLC. Despite genetic differences between LUAD and LUSC have been clarified in depth, the metabolic differences of these two subtypes are still unclear.

METHODS

Totally, 128 plasma samples of NSCLC patients were collected before initial treatments, followed by determination of LC-ESI-Q TRAP-MS/MS. Differentially expressed metabolites were screened based on a strict standard.

RESULTS

Based on the integrated platform of targeted metabolome and lipidome, a total of 1141 endogenous metabolites (including 809 lipids) were finally detected in the plasma of NSCLC patients, including 16 increased and 3 decreased endogenous compounds in LUAD group when compared with LUSC group. Thereafter, a logistic regression model integrating four differential metabolites [2-(Methylthio) ethanol, Cortisol, D-Glyceric Acid, and N-Acetylhistamine] was established and could accurately differentiate LUAD and LUSC with an area under the ROC curve of 0.946 (95% CI 0.886-1.000). The cut-off value showed a satisfactory efficacy with 92.0% sensitivity and 92.9% specificity. KEGG functional enrichment analysis showed these differentially expressed metabolites could be further enriched in riboflavin metabolism, steroid hormone biosynthesis, prostate cancer, etc. The endogenous metabolites identified in this study have the potential to be used as novel biomarkers to distinguish LUAD from LUSC.

CONCLUSIONS

Our research might provide more evidence for exploring the pathogenesis and differentiation of NSCLC. This research could promote a deeper understanding and precise treatment of lung cancer.

Overexpression of RNF126 is associated with poor prognosis and contributes to the progression of lung adenocarcinoma

Aim: To document the expression and function of RNF126 in lung adenocarcinoma (LAD). Materials & methods: A total of 102 LAD patients were enrolled in this retrospective study. The mRNA and protein levels of RNF126 were tested via RT-qPCR and immunohistochemical staining, respectively. Univariate and multivariate analyses were conducted to exploring the clinical significance of RNF126 in the prognosis. Knockdown and overexpression strategies were used to validate the tumor-related roles of RNF126 both in vitro and in vivo. Results: RNF126 was highly expressed and was an independent predictor of a poor prognosis for LAD patients. We also revealed that RNF126 knockdown suppressed proliferation of LAD cells and xenografts. Conclusion: RNF126 is a potential survival predictor and therapeutic target for LAD.

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

Identification of colon tumor marker NKD1 via integrated bioinformatics analysis and experimental validation

Background

Colorectal cancer is an important death‐related disease in the worldwide. However, specific colon cancer tumor markers currently used for diagnosis and treatment are few. The purpose of this study is to screen the potential colon cancer markers by bioinformatics and verify the results with experiments.

Methods

Gene expression data were downloaded from two different databases: TCGA database and GEO datasets, which were then analyzed by two different methods (difference analysis and WGCNA method). Venn and PPI analysis obtained the potential core genes, which were then performed the GO enrichment and KEGG pathway analysis. Expressions levels of NKD1 in colon carcinoma tissues were further confirmed by immunohistochemical staining and western blot assays. Moreover, the function was measured by MTT, clone formation, and tumor transplantation experiments. Importantly, co‐immunoprecipitation, immunofluorescence, and protein stability assays were further performed to explore the underlying mechanism of NKD1 promoting cell proliferation.

Results

Nine potential core genes highly expressed in colon cancer samples were screened out by bioinformatics analysis. NKD1, one of the hub genes, highly expressed in the colon carcinoma tissues could enhance the proliferation of colon cancer cells. Mechanism research demonstrated that NKD1 was essential for the combination between Wnt signalosome (DVL) and β‐catenin, and that NKD1 knockout remarkably decreased the β‐catenin expression. Immunofluorescence assays further implied that NKD1 knockout significantly inhibited β‐catenin nuclear accumulation. Importantly, the stability of β‐catenin proteins was maintained by NKD1 in the colon cancer cells.

Conclusion

We believe that NKD1 well expressed in the colorectal carcinoma tissues can enhance the proliferation of colon cancer cells. Furthermore, the functions that NKD1 may have in colon cancer cells should be different from that NKD1 has played in the zebrafish. Thus, NKD1 could be a specific colorectal cancer marker.

Evaluation of tumor microenvironmental immune regulation and prognostic in lung adenocarcinoma from the perspective of purinergic receptor P2Y13

Tumor-infiltrating immune cells (TICs) can serve as an important indicator to evaluate the prognosis and therapeutic response in lung adenocarcinoma (LUAD). The identification of mutated genes that can affect the abundance of TICs and prognosis has practical implications. In the presented study, tumor microenvironment (TME) scoring was performed by the ESTIMATE scoring system on 598 RNA transcripts selected from the TCGA database to determine the proportions of immune cells and stromal cells. The infiltration difference of TICs in LUAD samples was obtained by CIBERSORT. The 'immuneeconv' R software package, which integrates six latest algorithms, including TIMER, xCell, MCP-counter, CIBERSORT, EPIC and quanTIseq were used to verify the correlation between purinergic receptor P2Y13 (P2RY13) and immune cells. Based on RNA sequencing analysis of the Lewis lung cancer-bearing model in C57BL/6 mice and immunohistochemistry (IHC) of human LUAD tissues, the expression of P2RY13 and associated pathways were verified. It was shown that differentially expressed genes (DEGs) obtained by interactive analysis based on Immunescore and Stromalscore were significantly enriched in immune-related pathways. The expression of P2RY13 was significantly associated with prognosis and clinicopathological characteristics of LUAD patients. More importantly, this gene played an important role in maintaining the immune dominant environment and changing the regulation of TICs. P2RY13 expression was positively correlated with the infiltration of dendritic cells (DCs) in various of tumor tissues as validated by the PanglaoDB scRNA-seq database. Therefore, P2RY13 is expected to be a potential biomarker for predicting TME and the prognosis of LUAD after verification.

Applying immune-related lncRNA pairs to construct a prognostic signature and predict the immune landscape of stomach adenocarcinoma

Background: Long noncoding RNAs (lncRNAs) are associated with the survival of cancer patients. We constructed an immune-related lncRNA (irlncRNA) pair signature for stomach adenocarcinoma (STAD).Research design and methods: irlncRNAs were identified via coexpression analysis with immune-related genes. Differentially expressed irlncRNAs (DEirlncRNAs) were paired. Least absolute shrinkage and selection operator (LASSO) and multivariate Cox proportional hazards regression methods were used to construct the signature. We calculated the area under the receiver operating characteristic (ROC) curve and determined the best cutoff value according to the Akaike information criterion (AIC). Patients were divided into high - and low-risk groups, and differences in immune cell infiltration, tumor mutation burden (TMB) and drug treatment effects between the groups were explored according to the risk score.Results: An 8-irlncRNA-pair signature was constructed and proven to be a strong prognosis predictor in STAD patients through external verification. Moreover, the risk score was identified as an independent prognostic factor. There were significant differences in immune cell infiltration and the response to several drug treatments between patients with high and low risk scores, and the risk score was negatively correlated with TMB.Conclusions: The signature consisting of 8 irlncRNA pairs showed good prognostic predictive value.

Single-cell immune checkpoint landscape of PBMCs stimulated with Candida albicans

Immune checkpoints play various important roles in tumour immunity, which usually contribute to T cells’ exhaustion, leading to immunosuppression in the tumour microenvironment. However, the roles of immune checkpoints in infectious diseases, especially fungal infection, remain elusive. Here, we reanalyzed a recent published single-cell RNA-sequencing (scRNA-seq) data of peripheral blood mononuclear cells (PBMCs) stimulated with Candida albicans (C. albicans), to explore the expression patterns of immune checkpoints after C. albicans bloodstream infection. We characterized the heterogeneous pathway activities among different immune cell subpopulations after C. albicans infection. The CTLA-4 pathway was up-regulated in stimulated CD4⁺ and CD8⁺ T cells, while the PD-1 pathway showed high activity in stimulated plasmacytoid dendritic cell (pDC) and monocytes. Importantly, we found that immunosuppressive checkpoints HAVCR2 and LAG3 were only expressed in stimulated NK and CD8⁺ T cells, respectively. Their viabilities were validated by flow cytometry. We also identified three overexpressed genes (ISG20, LY6E, ISG15) across all stimulated cells. Also, two monocyte-specific overexpressed genes (SNX10, IDO1) were screened out in this study. Together, these results supplemented the landscape of immune checkpoints in fungal infection, which may serve as potential therapeutic targets for C. albicans infection. Moreover, the genes with the most relevant for C. albicans infection were identified in this study.

LRP1B mutation: a novel independent prognostic factor and a predictive tumor mutation burden in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies globally and the second leading cause of cancer-related death. Low-density lipoprotein (LDL) receptor-related protein 1B (LRP1B) is one of the commonly mutated genes in HCC, but its role in HCC remains unclear. In this study, we analyzed the role of LRP1B mutation in HCC. The bioinformatics results show that LRP1B had a frequency of mutation in HCC patients, and LRP1B mutation was associated with a higher tumor mutation burden (TMB), and survival analysis proved that the prognosis of HCC patients with LRP1B mutation was poor. Univariate and multivariate COX regression analysis indicated that LRP1B mutation was an independent risk factor in evaluating HCC patients' prognosis. Correlation analysis showed that LRP1B mutation status was associated with the infiltration of 2 types of immune cells and higher expression of immune checkpoint gene human endogenous retrovirus-H long terminal repeat-associating protein 2 (HHLA2) in HCC patients. In summary, the results show that LRP1B mutation is associated with the higher TMB and poor prognosis of patients with HCC, and it was an independent risk factor for clinical outcomes of HCC patients. LRP1B gene mutations can serve as predictors in HCC patients with higher TMB and higher expression of HHLA2. The results of this study will be beneficial to future studies on targeted therapy and immunotherapy for HCC.

As above, so below: Whole transcriptome profiling demonstrates strong molecular similarities between avian dorsal and ventral pallial subdivisions

Over the last two decades, beginning with the Avian Brain Nomenclature Forum in 2000, major revisions have been made to our understanding of the organization and nomenclature of the avian brain. However, there are still unresolved questions on avian pallial organization, particularly whether the cells above the vestigial ventricle represent distinct populations to those below it or similar populations. To test these two hypotheses, we profiled the transcriptomes of the major avian pallial subdivisions dorsal and ventral to the vestigial ventricle boundary using RNA sequencing and a new zebra finch genome assembly containing about 22,000 annotated, complete genes. We found that the transcriptomes of neural populations above and below the ventricle were remarkably similar. Each subdivision in dorsal pallium (Wulst) had a corresponding molecular counterpart in the ventral pallium (dorsal ventricular ridge). In turn, each corresponding subdivision exhibited shared gene co‐expression modules that contained gene sets enriched in functional specializations, such as anatomical structure development, synaptic transmission, signaling, and neurogenesis. These findings are more in line with the continuum hypothesis of avian brain subdivision organization above and below the vestigial ventricle space, with the pallium as a whole consisting of four major cell populations (intercalated pallium, mesopallium, hyper‐nidopallium, and arcopallium) instead of seven (hyperpallium apicale, interstitial hyperpallium apicale, intercalated hyperpallium, hyperpallium densocellare, mesopallium, nidopallium, and arcopallium). We suggest adopting a more streamlined hierarchical naming system that reflects the robust similarities in gene expression, neural connectivity motifs, and function. These findings have important implications for our understanding of overall vertebrate brain evolution.

Abnormal Expression and Prognostic Significance of Bone Morphogenetic Proteins and Their Receptors in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is one of the most life-threatening malignancies. The crucial role of bone morphogenetic protein (BMP)/BMP receptors reveals the significance of exploring BMP protein-related prognostic predictors in LUAD.

Methods

The mRNA expression of BMPs/BMP receptors was investigated in LUAD and normal lung tissues. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed, and the prognostic values were assessed by Kaplan-Meier Plotter. Univariate and multivariate Cox regression analyses were executed to ascertain the correlation between overall survival (OS) and the mRNA expression of BMPs/BMP receptors. The receiver operating characteristic (ROC) curves were implemented to evaluate the predictive power of the prognostic model. Then, the prognostic model was validated in the GEO cohort. Furthermore, a nomogram comprising the prognostic model was established.

Results

The mRNA expression of BMP2/5/6/R2, ACVRL1, and TGFBR2/3 was lower in LUAD tissues than in normal lung tissues. High expression of BMP2/4/5/R1A/R2, ACVR1/2A/L1, and TGFBR1/3 was associated with better OS, while BMP7 and ACVR1C/2B were associated with poorer OS. Three genes (BMP5, BMP7, and ACVR2A) were screened by univariate and multivariate Cox regression analyses to develop the prognostic model in TCGA. Significantly better survival was observed in LUAD patients with a low-risk score than those with a high-risk score. The ROC curves confirmed the good performance of the prognostic model, then, the prognostic model was validated in the GSE31210 dataset. A nomogram was constructed (AUCs>0.7). And hub genes were further evaluated, including gene set enrichment analysis and immune cell infiltration.

Conclusions

BMP5, BMP7, and ACVR2A are potential therapeutic targets in LUAD. The three-gene prognostic model and the nomogram are reliable tools for predicting the OS of LUAD patients.

Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations

Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.

Prognostic and clinicopathological value of BUB1B expression in patients with lung adenocarcinoma: a meta-analysis

BACKGROUND

Abnormal BUB1B expression has been proven to be related to the poor prognosis of various tumors. This meta-analysis aimed to identify the prognostic role of BUB1B in patients with lung adenocarcinoma (LUAD).

RESEARCH DESIGN AND METHODS

Relevant studies from the PubMed, Embase, Web of Science, and Cochrane Library databases and two public databases that stored sequencing data were retrieved. The standardized mean difference (SMD) and 95% confidence intervals (CIs) for the association between the BUB1B expression level and clinical characteristics were calculated. Pooled hazard ratios (HRs) and 95% CIs were calculated to estimate the association between BUB1B expression and survival outcomes.

RESULTS

A total of 16 studies involving 2771 LUAD patients with BUB1B expression were included in this meta-analysis. Patients with older age showed low BUB1B expression. High BUB1B expression was associated with male sex, a smoking history, and an advanced TNM stage. High BUB1B expression was predictive of poor overall survival (OS) and progression-free survival (PFS). In addition, no publication bias was found.

CONCLUSIONS

This meta-analysis demonstrates that BUB1B is a significant biomarker for a poor prognosis and poor clinicopathological outcomes in patients with LUAD.

Systematic expression analysis of the CELSR family reveals the importance of CELSR3 in human lung adenocarcinoma

Cadherin EGF LAG seven‐pass G‐type receptors (CELSRs) are involved in the progression of various types of cancer. CELSR3, a crucial signalling molecule in the WNT/PCP pathway, is believed to be associated with tumorigenesis and metastasis. However, its role in lung adenocarcinoma (LUAD) remains unclear. In this paper, we analysed the expression of CELSR family members using the Oncomine, GEPIA and UALCAN databases. We used a Kaplan‐Meier plotter to assess the effect of CELSRs on tumour prognosis. Next, gene ontology (GO), KEGG pathway, miRNA target, kinase target and transcription factor‐target enrichment were analysed by GSEA. Simultaneously, we conducted functional assays including cell viability, colony formation and transwell assays, to determine the oncogenic role of CELSR3 in LUAD. Finally, we used the TIMER and TISIDB databases to analyse the correlation between CELSR3 and immune infiltration and the potential chemokine receptor axis causing immune cell expression. High expression of CELSR3 is in LUAD predicts poor prognosis and early progression of the tumour. KEGG and GO enrichment analysis revealed the functional relationship between CELSR3 and cell adhesion, the cell cycle, and DNA replication. Down‐regulation of CELSR3 suppressed cell proliferation to a significant extent, in addition to inhibiting invasion and migration in LUAD cells. Finally, CELSR3 expression was significantly correlated with the infiltration level of CD8+T cells through the CCL17/CCR4 axis in LUAD. These results indicate that CELSR3 can serve as a prognostic biomarker for determining prognosis and immune infiltration in LUAD.

Novel VHL substrate targets SFMBT1 and ZHX2 may be important prognostic predictors in patients with ccRCC

Renal cell carcinoma is one of the most malignant cancers, with limited prognostic prediction system. The present study aimed to determine the prognostic value of novel von Hippel-Lindau (VHL) substrate targets in predicting the outcome of clear cell renal cell carcinoma (ccRCC). A total of 97 patients with ccRCC were enrolled in the present study, and the tissue microarray that was constructed using 97 ccRCC samples was used for immunohistochemical analysis. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors. Reverse transcription-quantitative PCR analysis demonstrated that the mRNA expression levels of scm-like with four malignant brain tumor domains (SFMBT1) and zinc fingers and homeoboxes 2 (ZHX2) were upregulated in cancer tissues compared with adjacent normal tissues. Among the 97 patients with ccRCC, SFMBT1 expression was upregulated in 61.9% (60/97), while ZHX2 expression was upregulated in 52.6% (51/97). Overall survival (OS) and disease-free survival (DFS) analyses indicated that SFMBT1 or ZHX2 alone were of limited predictive value; however, the combined expression of these two targets (high SFMBT1 and high ZHX2 expression, SHZH group) was significantly associated with OS (P=0.0350) and DFS (P=0.0434). In addition, multivariate analysis identified SHZH as an independent prognostic factor in patients with ccRCC. Taken together, these results suggest that SFMBT1 and ZHX2 act as novel substrate targets of VHL and, to the best of our knowledge, the present study was the first to provide insight on the co-expression of these two targets in representing a promising biomarker to predict the outcome of patients with ccRCC.

A Risk Signature with Nine Stemness Index-Associated Genes for Predicting Survival of Patients with Uterine Corpus Endometrial Carcinoma

Purpose

To identify mRNA expression-based stemness index- (mRNAsi-) related genes and build an mRNAsi-related risk signature for endometrial cancer.

Methods

We collected mRNAsi data of endometrial cancer samples from The Cancer Genome Atlas (TCGA) and analyzed their relationship with the main clinicopathological characteristics and prognosis of endometrial cancer patients. We screened the top 50% of the genes in TCGA for weighted gene correlation network analysis (WGCNA) to explore mRNAsi-related gene sets. Among these mRNAsi-related genes, we further screened for those related to the prognosis of endometrial cancer patients via univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Using stepwise multivariate Cox regression analysis, a stemness index-related risk signature was constructed. Finally, we identified potential prognostic biomarkers for endometrial cancer by combining the GEO database and immunohistochemical staining.

Results

The mRNAsi of endometrial cancer samples was significantly higher than that of normal samples and was related to the International Federation of Gynecology and Obstetrics (FIGO) stage, pathological grade, postoperative tumor status, and overall survival of endometrial cancer patients. We identified 21 mRNAsi-related gene modules, and 1,324 genes were obtained from the most relevant module. TCGA samples were divided into training and validation cohorts, and the training cohort was used to construct a nine-mRNAsi-related gene signature (B3GAT2, CD3EAP, DMC1, FRMPD3, LINC01224, LINC02068, LY6H, NR6A1, and TLE2). High-risk and low-risk patients had significant prognostic differences, and the risk signature could accurately predict their 1-, 3-, and 5-year survival. The nomogram composed of risk score and multiple clinicopathological features could accurately predict 1-, 3-, and 5-year survival. Finally, CD3EAP was found to be a novel prognostic biomarker for endometrial cancer.

Conclusion

Endometrial cancer cell stemness is related to patient prognosis. The nine-gene risk signature is an independent prognostic factor and can accurately predict endometrial cancer patient prognosis.

Constructing and validating a diagnostic nomogram for multiple sclerosis via bioinformatic analysis

The purpose of this study was to identify biomarkers and construct a diagnostic prediction model for multiple sclerosis (MS). Microarray datasets in the Gene Expression Omnibus (GEO) were downloaded. Weighted gene coexpression analysis (WGCNA) was used to search for hub modules and biomarkers related to MS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to roughly define their biological functions and pathways. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis were used to identify the diagnostic biomarkers and construct a nomogram. The calibration curve and receiver operating characteristic (ROC) curve were used to judge the diagnostic predictive ability. In addition, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to calculate the proportion of 22 kinds of immune cells. GSE41850 was used as the training set, and GSE17048 was used as the test set. WGCNA revealed one hub module containing 165 hub genes. Most of their biological functions and pathways are related to cell metabolism and immune cell activation. The diagnostic nomogram contained ARPC5, ROD1, UBQLN2, ZNF281, ABCA1 and FAS. The ROC curve and the calibration curve of the training set and test set confirmed that the nomogram had great prediction ability. In addition, monocytes and M0 macrophages were significantly different between MS patients and healthy people. The expression of ARPC5, ZNF281 and ABCA1 is correlated with M0 macrophages. The nomogram provides new insights and contributes to the accurate diagnosis of MS.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02675-1.

Prognostic signature of lung adenocarcinoma based on stem cell-related genes

Lung adenocarcinoma (LUAD) is characterized by high infiltration and rapid growth. The function of the stem cell population is to control and maintain cell regeneration. Therefore, it is necessary to study the prognostic value of stem cell-related genes in LUAD. Signature genes were screened out from 166 stem cell-related genes according to the least absolute shrinkage operator (LASSO) and subsequently multivariate Cox regression analysis, and then established risk model. Immune infiltration and nomogram model were used to evaluate the clinical efficacy of signature. A signature consisting of 10 genes was used to dichotomize the LUAD patients into two groups (cutoff, 1.314), and then validated in GSE20319 and GSE42127. There was a significant correlation between signature and clinical characteristics. Patients with high-risk had a shorter overall survival. Furthermore, significant differences were found in multiple immune cells between the high-risk group and low-risk group. A high correlation was also reflected between signature and immune infiltration. What’s more, the signature could effectively predict the efficacy of chemotherapy in patients with LUAD, and a nomogram based on signature might accurately predict the prognosis of patients with LUAD. The signature-based of stem cell-related genes might be contributed to predicting prognosis of patients with LUAD.

MYBL2-Driven Transcriptional Programs Link Replication Stress and Error-prone DNA Repair With Genomic Instability in Lung Adenocarcinoma

It has long been recognized that defects in cell cycle checkpoint and DNA repair pathways give rise to genomic instability, tumor heterogeneity, and metastasis. Despite this knowledge, the transcription factor-mediated gene expression programs that enable survival and proliferation in the face of enormous replication stress and DNA damage have remained elusive. Using robust omics data from two independent studies, we provide evidence that a large cohort of lung adenocarcinomas exhibit significant genome instability and overexpress the DNA damage responsive transcription factor MYB proto-oncogene like 2 (MYBL2). Across two studies, elevated MYBL2 expression was a robust marker of poor overall survival and disease-free survival outcomes, regardless of disease stage. Clinically, elevated MYBL2 expression identified patients with aggressive early onset disease, increased lymph node involvement, and increased incidence of distant metastases. Analysis of genomic sequencing data demonstrated that MYBL2 High lung adenocarcinomas had elevated somatic mutation burden, widespread chromosomal alterations, and alterations in single-strand DNA break repair pathways. In this study, we provide evidence that impaired single-strand break repair, combined with a loss of cell cycle regulators TP53 and RB1, give rise to MYBL2-mediated transcriptional programs. Omics data supports a model wherein tumors with significant genomic instability upregulate MYBL2 to drive genes that control replication stress responses, promote error-prone DNA repair, and antagonize faithful homologous recombination repair. Our study supports the use of checkpoint kinase 1 (CHK1) pharmacological inhibitors, in targeted MYBL2 High patient cohorts, as a future therapy to improve lung adenocarcinoma patient outcomes.

Gene Panel of Persister Cells as a Prognostic Indicator for Tumor Repopulation After Radiation

Tumor repopulation during cycles of radiotherapy limits the radio-response in ensuing cycles and causes failure of treatment. It is thus of vital importance to unveil the mechanisms underlying tumor repopulating cells. Increasing evidence suggests that a subpopulation of drug-tolerant persister cancer cells (DTPs) could survive the cytotoxic treatment and resume to propagate. Whether these persister cells contribute to development of radio-resistance remains elusive. Based on the genetic profiling of DTPs by integrating datasets from Gene Expression Omnibus database, this study aimed to provide novel insights into tumor-repopulation mediated radio-resistance and identify predictive biomarkers for radio-response in clinic. A prognostic risk index, grounded on four persister genes (LYNX1, SYNPO, GADD45B, and PDLIM1), was constructed in non-small-cell lung cancer patients from The Cancer Genome Atlas Program (TCGA) using stepwise Cox regression analysis. Weighted gene co-expression network analysis further confirmed the interaction among persister-gene based risk score, radio-response and overall survival time. In addition, the predictive role of risk index was validated in vitro and in other types of TCGA patients. Gene set enrichment analysis was performed to decipher the possible biological signaling, which indicated that two forces behind persister cells, stress response and survival adaptation, might fuel the tumor repopulation after radiation. Targeting these persister cells may represent a new prognostic and therapeutic approach to enhance radio-response and prevent radio-resistance induced by tumor repopulation.

A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma

Objective

In the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma.

Methods

Stromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model.

Results

We obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P<0.05, Hazard ratio [HR]>1) and acceptable predictive accuracy (AUCs>0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells.

Conclusions

The PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.

Potential Genes Associated with the Survival of Lung Adenocarcinoma Were Identified by Methylation

Background

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. The purpose of this study is to search for genes related to the prognosis of LUAD through methylation based on a linear mixed model (LMM).

Methods

Gene expression, methylation, and survival data of LUAD patients were downloaded from the TCGA database. Based on the LMM model, the GEMMA algorithm was used to screen the predictive genes related to LUAD survival. The Cox model was used to further screen the predicted genes, and then, protein-protein interaction (PPI) network was constructed. Through the software plugin Cytoscape MCODE 3.8.0, the most closely related genes in the PPI network module were selected for in-depth biological function analysis to further explore the interaction and correlation between genes.

Results

We screened out 97 predictive genes from 18,834 genes and eliminated one gene associated with lung squamous cell carcinoma from previous studies, leaving 96 genes. The MCODE and the Kaplan-Meier curve analysis were used to finally identify two genes ASB16 and NEDD4 that are related to the prognosis of LUAD.

Conclusions

The newly identified two genes associated with the prognosis of LUAD may provide a basis for the treatment of patients.