An immune relevant signature for predicting prognoses and immunotherapeutic responses in patients with muscle-invasive bladder cancer (MIBC)

Increased UBD Is a Potential Diagnostic and Prognostic Biomarker in Glioma

Various studies have demonstrated that ubiquitin D (UBD) is overexpressed in different cancer types and may serve as a potential prognostic factor. However, additional research is necessary to establish the prognostic significance and possible role of UBD in glioma. Transcriptomic expression data from The Cancer Genome Atlas database (TCGA) and Chinese Glioma Genome Atlas (CGGA) were analyzed to identify UBD expression differences in tumor and normal tissues. The relative levels of UBD in glioma and normal tissues were determined using qRT-PCR and WB. Logistic regression analysis was performed to investigate the association between UBD expression and clinicopathological characteristics of glioma patients. To evaluate the diagnostic and prognostic predictive values of UBD, we used Kaplan-Meier survival curves, Cox regression analysis, diagnostic receiver operating characteristic (ROC) curves, and nomogram model. We also conducted wound healing assays, transwell assays, EdU assays, and colony formation assays to verify the UBD function. Gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, as well as gene set enrichment analysis (GSEA), were employed to determine the functions of UBD. Finally, we performed the western blot assays to assess changes in EMT markers as well as p-PI3K, p-AKT, and p-mTOR expressions. Our study revealed a remarkable increase of UBD expression in glioma samples. Cox regression analysis demonstrated that high expression of UBD mRNA was an independent prognostic factor for overall survival (OS) in TCGA. ROC curve analysis showed that UBD expression levels could differentiate glioma from adjacent normal tissues accurately. Additionally, knockdown of UBD reduced the migration, invasion, and proliferation ability of glioma cells while UBD overexpression had the opposite effect. GSEA showed that the expression of UBD involved with various pathways including epithelial-mesenchymal transition (EMT), PI3K-AKT-mTOR signaling, P53 pathway, angiogenesis, inflammatory response, KRAS signaling, hypoxia, as well as TGF-β signaling. Furthermore, our findings suggest that UBD accelerates the activation of EMT and PI3K/AKT/mTOR pathway.

Prediction of immunotherapy response of bladder cancer with a pyroptosis-related signature indicating tumor immune microenvironment

Background

Although prognostic models based on pyroptosis-related genes (PRGs) have been constructed in bladder cancer (BLCA), the comprehensive impact of these genes on tumor microenvironment (TME) and immunotherapeutic response has yet to be investigated.

Methods

Based on expression profiles of 52 PRGs, we utilized the unsupervised clustering algorithm to identify PRGs subtypes and ssGSEA to quantify immune cells and hallmark pathways. Moreover, we screened feature genes of distinct PRGs subtypes and validated the associations with immune infiltrations in tissue using the multiplex immunofluorescence. Univariate, LASSO, and multivariate Cox regression analyses were employed to construct the scoring scheme.

Results

Four PRGs clusters were identified, samples in cluster C1 were infiltrated with more immune cells than those in others, implying a favorable response to immunotherapy. While the cluster C2, which shows an extremely low level of most immune cells, do not respond to immunotherapy. CXCL9/CXCL10 and SPINK1/DHSR2 were identified as feature genes of cluster C1 and C2, and the specimen with high CXCL9/CXCL10 was characterized by more CD8 + T cells, macrophages and less Tregs. Based on differentially expressed genes (DEGs) among PRGs subtypes, a predictive model (termed as PRGs score) including five genes (CACNA1D, PTK2B, APOL6, CDK6, ANXA2) was built. Survival probability of patients with low-PRGs score was significantly higher than those with high-PRGs score. Moreover, patients with low-PRGs score were more likely to benefit from anti-PD1/PD-L1 regimens.

Conclusion

PRGs are closely associated with TME and oncogenic pathways. PRGs score is a promising indicator for predicting clinical outcome and immunotherapy response.

Silencing immune-infiltrating biomarker CCDC80 inhibits malignant characterization and tumor formation in gastric cancer

OBJECTIVE

Tumor immune infiltration leads to poor prognosis of gastric cancer patients and seriously affects the life quality of gastric cancer patients. This study was based on bioinformatics to screen prognostic biomarkers in patients with high degree of immune invasion of gastric cancer. Meanwhile, the action of biomarker CCDC80 was explored in gastric cancer by cell and tumorigenesis experiments, to provide reference for the cure of gastric cancer patients.

METHODS

Data sets and clinical massage on gastric cancer were collected from TCGA database and GEO database. ConsensusClusterPlus was used to cluster gastric cancer patients based on the 28 immune cells infiltration in ssGSEA. R "Limma" package was applied to analyze differential mRNAs between Cluster 1 and Cluster 2. Differential expression genes were screened by single factor analysis. Stemness markers (SERPINF1, DCN, CCDC80, FBLN5, SPARCL1, CCL14, DPYSL3) were identified for differential expression genes. Prognostic value of CCDC80 was evaluated in gastric cancer. Differences in genomic mutation and tumor microenvironment immune infiltration were assessed between high or low CCDC80. Finally, gastric cancer cells (HGC-27 and MKN-45) were selected to evaluate the action of silencing CCDC80 on malignant characterization, macrophage polarization, and tumor formation.

RESULTS

Bioinformatics analysis showed that CCDC80, as a stemness marker, was significantly overexpressed in gastric cancer. CCDC80 was also related to the degree of gastric cancer immune invasion. CCDC80 was up-expressed in cells of gastric cancer. Silencing CCDC80 inhibited malignant characterization and subcutaneous tumor formation of gastric cancer cells. High expression of CCDC80 was positive correspondence with immune invasion. Silencing CCDC80 inhibited M2 polarization and promoted M1 polarization in tumor tissues. In addition, gastric cancer patients were likely to have mutations in CDH1, ACTRT1, GANAB, and CDH10 genes in the High-CCDC80 group.

CONCLUSION

Silencing CCDC80, a prognostic biomarker in patients with immune invasion of gastric cancer, could effectively inhibit the malignant characterization, M2 polarization, and tumor formation of gastric cancer.

Transcriptome Analysis Reveals Distinct Patterns Between the Invasive and Noninvasive Pituitary Neuroendocrine Tumors

Although most pituitary neuroendocrine tumors (PitNETs)/pituitary adenomas remain intrasellar, a significant proportion of tumors show parasellar invasive growth and 6% to 8% infiltrate the bone structures, thus affecting the prognosis. There is an unmet need to identify novel markers that can predict the parasellar growth of PitNETs. Furthermore, mechanisms that regulate bone invasiveness of PitNETs and factors related to tumor vascularization are largely unknown. We used genome-wide mRNA analysis in a cohort of 77 patients with PitNETs of different types to explore the differences in gene expression patterns between invasive and noninvasive tumors with respect to the parasellar growth and regarding the rare phenomenon of bone invasiveness. Additionally, we studied the genes correlated to the contrast enhancement quotient, a novel radiological parameter of tumor vascularization. Most of the genes differentially expressed related to the parasellar growth were genes involved in tumor invasiveness. Differentially expressed genes associated with bone invasiveness are involved in NF-κB pathway and antitumoral immune response. Lack of clear clustering regarding the parasellar and bone invasiveness may be explained by the influence of the cell lineage-related genes in this heterogeneous cohort of PitNETs. Our transcriptomics analysis revealed differences in the molecular fingerprints between invasive, including bone invasive, and noninvasive PitNETs, although without clear clustering. The contrast enhancement quotient emerged as a radiological parameter of tumor vascularization, correlating with several angiogenesis-related genes. Several of the top genes related to the PitNET invasiveness and vascularization have potential prognostic and therapeutic application requiring further research.

Identification of an inflammation-related risk signature for prognosis and immunotherapeutic response prediction in bladder cancer

Tumor inflammation is one of the hallmarks of tumors and is closely related to tumor occurrence and development, providing individualized prognostic prediction. However, few studies have evaluated the relationship between inflammation and the prognosis of bladder urothelial carcinoma (BLCA) patients. Therefore, we constructed a novel inflammation-related prognostic model that included six inflammation-related genes (IRGs) that can precisely predict the survival outcomes of BLCA patients. RNA-seq expression and corresponding clinical data from BLCA patients were downloaded from The Cancer Genome Atlas database. Enrichment analysis was subsequently performed to determine the enrichment of GO terms and KEGG pathways. K‒M analysis was used to compare overall survival (OS). Cox regression and LASSO regression were used to identify prognostic factors and construct the model. Finally, this prognostic model was used to evaluate cell infiltration in the BLCA tumor microenvironment and analyze the effect of immunotherapy in high- and low-risk patients. We established an IRG signature-based prognostic model with 6 IRGs (TNFRSF12A, NR1H3, ITIH4, IL1R1, ELN and CYP26B1), among which TNFRSF12A, IL1R1, ELN and CYP26B1 were unfavorable prognostic factors and NR1H3 and ITIH4 were protective indicators. High-risk score patients in the prognostic model had significantly poorer OS. Additionally, high-risk score patients were associated with an inhibitory immune tumor microenvironment and poor immunotherapy response. We also found a correlation between IRS-related genes and bladder cancer chemotherapy drugs in the drug sensitivity data. The IRG signature-based prognostic model we constructed can predict the prognosis of BLCA patients, providing additional information for individualized prognostic judgment and treatment selection.

Single-Cell Sequencing Combined with Transcriptome Sequencing Constructs a Predictive Model of Key Genes in Multiple Sclerosis and Explores Molecular Mechanisms Related to Cellular Communication

Background

Multiple sclerosis (MS) causes chronic inflammation and demyelination of the central nervous system and comprises a class of neurodegenerative diseases in which interactions between multiple immune cell types mediate the involvement of MS development. However, the early diagnosis and treatment of MS remain challenging.

Methods

Gene expression profiles of MS patients were obtained from the Gene Expression Omnibus (GEO) database. Single-cell and intercellular communication analyses were performed to identify candidate gene sets. Predictive models were constructed using LASSO regression. Relationships between genes and immune cells were analyzed by single sample gene set enrichment analysis (ssGSEA). The molecular mechanisms of key genes were explored using gene enrichment analysis. An miRNA network was constructed to search for target miRNAs related to key genes, and related transcription factors were searched by transcriptional regulation analysis. We utilized the GeneCard database to detect the correlations between disease-regulated genes and key genes. We verified the mRNA expression of 4 key genes by reverse transcription-quantitative PCR (RT‒qPCR).

Results

Monocyte marker genes were selected as candidate gene sets. CD3D, IL2RG, MS4A6A, and NCF2 were found to be the key genes by LASSO regression. We constructed a prediction model with AUC values of 0.7569 and 0.719. The key genes were closely related to immune factors and immune cells. We explored the signaling pathways and molecular mechanisms involving the key genes by gene enrichment analysis. We obtained and visualized the miRNAs associated with the key genes using the miRcode database. We also predicted the transcription factors involved. We used validated key genes in MS patients, several of which were confirmed by RT‒qPCR.

Conclusion

The prediction model constructed with the CD3D, IL2RG, MS4A6A, and NCF2 genes has good diagnostic efficacy and provides new ideas for the diagnosis and treatment of MS.

Hypomethylation of CD3D promoter induces immune cell infiltration and supports malignant phenotypes in uveal melanoma

Alterations in DNA methylation in malignant diseases have been heralded as promising targets for diagnostic, prognostic, and predictive values. This study was based on epigenetic alterations and immune cell infiltration analysis to investigate the mechanism of CD3D methylation in uveal melanoma (UM). Bioinformatics analysis was performed on transcriptome data, 450 K methylation data, and clinical information of UM patients from the TCGA database. Stromal and immune cell infiltration was evaluated by calculating the StromalScore and ImmuneScore of UM samples. UM samples were divided into high and low StromalScore and ImmuneScore groups, followed by differential and enrichment analyses. PPI network construction and correlation analysis was used to identify the core prognosis-related genes. The bioinformatics analysis results were confirmed in UM cell experiments. StromalScore and ImmuneScore were significantly associated with the prognosis of UM patients. CD3D, IRF1, CCL3, and FN1 were identified as core genes driven by methylation that affected the prognosis of UM patients. CD3D expression showed the highest correlation with its methylation and was closely related to the four key immune cells in UM development. CD3D was hypomethylated and abundantly expressed in UM cells, while silencing of CD3D inhibited the proliferation, migration, and invasion of UM cells in vitro. In summary, this study identifies hypomethylation of CD3D promoter in UM, which was associated with immune cell infiltration of UM.

A transcriptome study of p53-pathway related prognostic gene signature set in bladder cancer

p53 pathway is important in tumorigenesis. However, no study has been performed to specifically investigate the role of p53 pathway genes in bladder cancer (BLCA). In this study, transcriptomics data of muscle invasive bladder cancer patients (n = 411) from The Cancer Genome Atlas (TCGA) were investigated. Using the hallmark p53 pathway gene set, the Non-Negative Matrix factorization (NMF) analysis identified two subtypes (C1 and C2). Clinical, survival, and immunological analysis were done to validate distinct characteristics of the subtypes. Pathway enrichment analysis showed the subtype C1 with poor prognosis having enrichment in genes of the immunity related pathways, where C2 subtype with better prognosis being enriched in genes of the steroid synthesis and drug metabolism pathways. A signature gene set consisting of MDGA2, GNLY, GGT2, UGT2B4, DLX1, and DSC1 was created followed by a risk model. Their expressions were analyzed in RNA extracted from the blood and matched tumor tissues of BLCA patients (n = 10). DSC1 had significant difference of expression (p = 0.005) between the blood and tumor tissues in our BLCA samples. Contrary to the usual normal bladder tissue to blood ratio, DLX1 expression was lower (p = 0.02734) in tumor tissues than in blood. Being the first research of p53 pathway related signature gene set in bladder cancer, this study potentially has a substantial impact on the development of biomarkers for BLCA.

Identifying novel biomarkers associated with bladder cancer treatment outcomes

Bladder cancer is a complex disease with variable prognosis. Recent investigations into the molecular landscape of bladder cancer have revealed frequent genetic alterations and molecular subtypes with therapeutic implications. Consequently, a shift toward personalized treatment of bladder cancer is underway. To this end, several biomarkers have been developed and tested in their ability to predict response to treatment in patients with bladder cancer and potentially help direct therapy. We performed a search of recently published PubMed articles using terms "biomarker," "bladder cancer," and the respective treatment discussed (i.e., "neoadjuvant" or "BCG"). In this review, we summarize the latest studies on novel biomarkers in bladder cancer with a focus on those intended to improve risk stratification and treatment selection.

Development of a Molecular-Subtype-Associated Immune Prognostic Signature That Can Be Recognized by MRI Radiomics Features in Bladder Cancer

Background: Bladder cancer (BLCA) is highly heterogeneous with distinct molecular subtypes. This research aimed to investigate the heterogeneity of different molecular subtypes from a tumor microenvironment perspective and develop a molecular-subtype-associated immune prognostic signature that can be recognized by MRI radiomics features. Methods: Individuals with BLCA in The Cancer Genome Atlas (TCGA) and IMvigor210 were classified into luminal and basal subtypes according to the UNC classification. The proportions of tumor-infiltrating immune cells (TIICs) were examined using The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm. Immune-linked genes that were expressed differentially between luminal and basal subtypes and associated with prognosis were selected to develop the immune prognostic signature (IPS) and utilized for the classification of the selected individuals into low- and high-risk groups. Functional enrichment analysis (GSEA) was performed on the IPS. The data from RNA-sequencing and MRI images of 111 BLCA samples in our center were utilized to construct a least absolute shrinkage and selection operator (LASSO) model for the prediction of patients’ IPSs. Results: Half of the TIICs showed differential distributions between the luminal and basal subtypes. IPS was highly associated with molecular subtypes, critical immune checkpoint gene expression, prognoses, and immunotherapy response. The prognostic value of the IPS was further verified through several validation data sets (GSE32894, GSE31684, GSE13507, and GSE48277) and meta-analysis. GSEA revealed that some oncogenic pathways were co-enriched in the group at high risk. A novel performance of a LASSO model developed as per ten radiomics features was achieved in terms of IPS prediction in both the validation (area under the curve (AUC): 0.810) and the training (AUC: 0.839) sets. Conclusions: Dysregulation of TIICs contributed to the heterogeneity between the luminal and basal subtypes. The IPS can facilitate molecular subtyping, prognostic evaluation, and personalized immunotherapy. A LASSO model developed as per the MRI radiomics features can predict the IPSs of affected individuals.

Prediction of immune infiltration and prognosis for patients with urothelial bladder cancer based on the DNA damage repair-related genes signature

Objectives

To analyze the correlations between the expression and effect of DNA damage repair genes and the immune status and clinical outcomes of urothelial bladder cancer (BLCA) patients. In addition, we evaluate the efficacy and value of utilizing the DNA damage repair genes signature as a prognosis model for BLCA.

Methods

Two subtype groups (C1 and C2) were produced based on the varied expression of DNA damage repair genes. Significantly differentiated genes and predicted enriched gene pathways were obtained between the two subtypes. Seven key genes were obtained from the DNA damage repair-related genes and a 7-gene signature prognosis model was established based on the key genes. The efficacy and accuracy of this model in prognosis prediction was evaluated and verified in two independent databases. Also, the difference in biological functions, drug sensitivity, immune infiltration and affinity between the high-risk group and low-risk group was analyzed.

Results

The DNA damage repair gene signature could significantly differentiate the BLCA into two molecular subgroups with varied genetic expression and enriched gene pathways. Seven key genes were screened out from the 232 candidate genes for prognosis prediction and a 7-gene signature prognosis model was established based on them. Two independent patient cohorts (TCGA cohort and GEO cohort) were utilized to validate the efficacy of the prognosis model, which demonstrated an effective capability to differentiate and predict the overall survival of BLCA patients. Also, the high-risk group and low-risk group derived from the 7-gene model exhibited significantly differences in drug sensitivity, immune infiltration status and biological pathways enrichment.

Conclusions

Our established 7-gene signature model based on the DNA damage repair genes could serve as a novel prognosis predictive tool for BLCA. The differentiation of BLCA patients based on the 7-gene signature model may be of great value for the appropriate selection of specific chemotherapy agents and immune-checkpoint blockade therapy administration.

Distinct tumor microenvironment landscapes of rectal cancer for prognosis and prediction of immunotherapy response

PURPOSE

Tumor microenvironment (TME) affects the progression of rectal cancer (RC), and the clinical relevance of its immune elements was widely reported. Here we aim to delineate the complete TME landscape, including non-immune features, to improve our understanding of RC heterogeneity and provide a better strategy for precision medicine.

METHODS

Single-cell analysis of GSE161277 using Seurat and Cellcall was performed to identify cell-cell interactions. The ssGSEA was employed to quantify the TME elements in TCGA patients, which were further clustered into subtypes by hclust. WGCNA and LASSO were combined to construct a degenerated signature for prognosis, and its performance was validated in two GEO datasets.

RESULTS

We proposed a subtyping strategy based on the abundance of both immune and non-immune components, which divided all RC patients into 4 subtypes (Immune-, Canonical-, Dormant- and Stem-like). Different subtypes exhibited distinct mutation landscapes, biological features, immune characteristics, immunotherapy responses and prognoses. Next, WGCNA and LASSO regression were combined to construct a 10-gene signature based on differentially expressed genes among different subtypes. Subgroups divided by this signature also exhibited different clinical parameters and responses to immune checkpoint blockades. Diverse machine learning algorithms were applied to achieve higher accuracy for survival prediction and a nomogram was further established in combination with M stage and age to provide an accurate and visual prediction of prognosis.

CONCLUSIONS

We identified four TME-based RC subtypes with distinct biological and clinical features. Based on those subtypes, we also proposed a degenerated 10-gene signature to predict the prognosis and immunotherapy response.

NXPH4 Used as a New Prognostic and Immunotherapeutic Marker for Muscle-Invasive Bladder Cancer

Background

One of the most common malignant tumors of the urinary system is muscle-invasive bladder cancer (MIBC). With the increased use of immunotherapy, its importance in the field of cancer is becoming abundantly evident. This study classifies MIBC according to GSVA score from the perspective of the GSEA immune gene set.

Methods

This study integrated the sequencing and clinical data of MIBC patients in TCGA and GEO databases, then scored the data using the GSVA algorithm, the CNMF algorithm was implemented to divide the subtypes of GEO and TCGA datasets, respectively, and finally screened and determined the key pathways in combination with clinical data. Simultaneously, LASSO Cox regression model was constructed based on key pathway genes to assess the model's predictive ability (ROC) and describe the immune landscape differences between high- and low-risk groups; key genes were further analyzed and verified in patient tissues.

Results

404 TCGA and 297 GEO datasets were divided into C1-3 groups (TCGA-C1:120/C2:152/C3:132; GEO- C1:112/C2:101/C3:84), of which TCGA-C2 (n = 152) subtype and GEO-C1 (n = 112) subtype had the worst prognosis. LASSO Cox regression model with ROC (train set = 0.718, test set = 0.667) could be constructed. When combined with the Cancer Immunome Atlas database, it was found that patients with high-risk scores were more sensitive to PD-1 inhibitor and PD-1 inhibitor combined with CTLA-4. NXPH4, as a key gene, plays a role in MIBC with tissue validation results show that nxph4 is highly expressed in tumor.

Conclusion

The immune gene score of MIBC data in TCGA and GEO databases was successfully evaluated using GSVA in this research. The lasso Cox expression model was successfully constructed by screening immune genes, the high-risk group had a worse prognosis and higher sensitivity to immunotherapy, PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors can be preferentially used in high-risk patients who are sensitive to immunotherapy, and NXPH4 may be a molecular target to adjust the effect of immunotherapy.

Analysis and identification of the necroptosis landscape on therapy and prognosis in bladder cancer

Bladder cancer (BLCA) is one of the most common malignant tumors of the urinary system, but the current therapeutic strategy based on chemotherapy and immune checkpoint inhibitor (ICI) therapy cannot meet the treatment needs, mainly owing to the endogenous or acquired apoptotic resistance of cancer cells. Targeting necroptosis provides a novel strategy for chemotherapy and targeted drugs and improves the efficacy of ICIs because of strong immunogenicity of necroptosis. Therefore, we systemically analyzed the necroptosis landscape on therapy and prognosis in BLCA. We first divided BLCA patients from The Cancer Genome Atlas (TCGA) database into two necroptosis-related clusters (C1 and C2). Necroptosis C2 showed a significantly better prognosis than C1, and the differential genes of C2 and C1 were mainly related to the immune response according to GO and KEGG analyses. Next, we constructed a novel necroptosis-related gene (NRG) signature consisting of SIRT6, FASN, GNLY, FNDC4, SRC, ANXA1, AIM2, and IKBKB to predict the survival of TCGA-BLCA cohort, and the accuracy of the NRG score was also verified by external datasets. In addition, a nomogram combining NRG score and several clinicopathological features was established to more accurately and conveniently predict the BLCA patient’s survival. We also found that the NRG score was significantly related to the infiltration levels of CD8 T cells, NK cells, and iDC cells, the gene expression of CTLA4, PD-1, TIGIT, and LAG3 of TME, and the sensitivity to chemotherapy and targeted agents in BLCA patients. In conclusion, the NRG score has an excellent performance in evaluating the prognosis, clinicopathologic features, tumor microenvironment (TME), and therapeutic sensitivity of BLCA patients, which could be utilized as a guide for chemotherapy, ICI therapy, and combination therapy.

Aging-related features predict prognosis and immunotherapy efficacy in hepatocellular carcinoma

The aging microenvironment serves important roles in cancers. However, most studies focus on circumscribed hot spots such as immunity and metabolism. Thus, it is well ignored that the aging microenvironment contributes to the proliferation of tumor. Herein, we established three prognosis-distinctive aging microenvironment subtypes, including AME1, AME2, and AME3, based on aging-related genes and characterized them with “Immune Exclusion,” “Immune Infiltration,” and “Immune Intermediate” features separately. AME2-subtype tumors were characterized by specific activation of immune cells and were most likely to be sensitive to immunotherapy. AME1-subtype tumors were characterized by inhibition of immune cells with high proportion of Catenin Beta 1 (CTNNB1) mutation, which was more likely to be insensitive to immunotherapy. Furthermore, we found that CTNNB1 may inhibit the expression of C-C Motif Chemokine Ligand 19 (CCL19), thus restraining immune cells and attenuating the sensitivity to immunotherapy. Finally, we also established a robust aging prognostic model to predict the prognosis of patients with hepatocellular carcinoma. Overall, this research promotes a comprehensive understanding about the aging microenvironment and immunity in hepatocellular carcinoma and may provide potential therapeutic targets for immunotherapy.

Predicting prognosis and immunotherapy response among colorectal cancer patients based on a tumor immune microenvironment-related lncRNA signature

Long non-coding RNAs (lncRNAs) remodel the tumor immune microenvironment (TIME) by regulating the functions of tumor-infiltrating immune cells. It remains uncertain the way that TIME-related lncRNAs (TRLs) influence the prognosis and immunotherapy response of colorectal cancer (CRC). Aiming at providing survival and immunotherapy response predictions, a CRC TIME-related lncRNA signature (TRLs signature) was developed and the related potential regulatory mechanisms were explored with a comprehensive analysis on gene expression profiles from 97 immune cell lines, 61 CRC cell lines and 1807 CRC patients. Stratifying CRC patients with the TRLs signature, prolonged survival was observed in the low-risk group, while the patients in the high-risk group had significantly higher pro-tumor immune cells infiltration and higher immunotherapy response rate. Through the complex TRLs-mRNA regulation network, immunoregulation pathways and immunotherapy response pathways were found to be differently activated between the groups. In conclusion, the CRC TRLs signature is capable of making prognosis and immunotherapy response predictions, which may find application in stratifying patients for immunotherapy in the bedside.

Characterization of the immune related lncRNAs in bladder cancer to aid immunotherapy

Bladder cancer (BLCA) is the 10th most common form of cancer worldwide. Currently, the response rate of BLCA patients to novel immunotherapy and immune checkpoint inhibitor (ICI) treatment is around 30% or less. Therefore, there is an urgent clinical demand to understand the regulation of immune function in BLCA patients. LncRNAs are known to play fundamental roles in the regulation of the immune system in the tumor microenvironment. In this report, we performed a comprehensive analysis to identify immune-related lncRNAs (IRLs) in BLCA patients using The Cancer Genome Atlas (TCGA) databases. BLCA patients were divided into five TME subtypes. Subtype HMIE was strongly related to survival and high anti-tumor activity of patients. Through a four-step analysis, we identified 34 IRLs as subtype HMIE related lncRNAs (HMIE-lncs).The correlation analysis with immune cell infiltration and target gene pathway enrichment showed that 34 HMIE-lncs were correlated with immune cell activation and tumor cell killing. Among them, 24 lncRNAs were related to good prognosis. We constructed a risk model to predict BLCA. Cross tumor validation was performed, and the results showed that the 34 HMIE-lncs identified in the BLCA patients in this study were highly expressed in the immune-favorable TME subtype (IE) in most of the other cancer types.

Prognostic significance and immune characteristics of CMTM4 in hepatocellular carcinoma

Background

Previous study has shown that chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing family member 4 (CMTM4) can bind and maintain programmed cell death ligand 1 (PD-L1) expression to promote tumor progression by alleviating the suppression of tumor-specific T cell activity, suggesting its potential role in tumor immunotherapy. However, the role of CMTM4 in tumor immunity has not been well clarified, especially in hepatocellular carcinoma (HCC).

Methods

The protein expression of CMTM4/PD-L1/CD4/CD8 was detected by immunohistochemistry (IHC) detection in 90 cases of HCC tissues. The mRNA expression profiles and related prognosis data were obtained from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC). Two immune therapy cohorts were from Imvigor210 and GSE176307.

Results

Though the single protein expression of CMTM4, PD-L1, CD4 or CD8 in HCC tissues by IHC detection didn’t show a significant relationship with the prognosis of HCC patients, we found that high co-expression of CMTM4/PD-L1/CD4 showed a good prognosis of HCC patients. Further Timer 2.0 analysis identified that HCC patients with high expression of CMTM4/PD-L1 and high infiltration of CD4⁺ T cells had a better overall survival than those with low infiltration of CD4⁺ T cells. Moreover, a series of bioinformatics analyses revealed that CMTM4-related genes posed important effects on prognosis and immunity in HCC patients, and CMTM4 had a positive correlation with infiltration of CD4⁺ and CD8⁺ T cells in HCC. At last, we used two immunotherapy cohorts to verify that the combination of CMTM4 with PD-L1 could improve the prognosis of tumor patients underwent immunotherapy.

Conclusions

CMTM4 and PD-L1 co-expression with T cell infiltration shows prognostic significance in HCC, suggesting combined effect from multiple proteins should be considered in HCC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09999-y.

Identification of the Tumor Infiltrating Lymphocytes (TILs) Landscape in Pure Squamous Cell Carcinoma of the Bladder

Background: Tumor infiltrating lymphocytes (TILs) are known as important prognostic biomarkers and build the fundament for immunotherapy. However, the presence of TILs and its impact on outcome in pure squamous cell carcinoma (SCC) of the bladder remains uncertain. Methods: Out of 1600 patients undergoing radical cystectomy, 61 patients revealed pure bladder SCC in the final histopathological specimen. Retrospectively, immunohistochemical staining was performed on a subset of TILs (CD3+, CD4+, CD8+, CD20+). Endpoints were overall survival (OS), cancer-specific survival (CSS) and progression-free survival (PFS). The Kaplan−Meier method was used to evaluate survival outcomes. Results: Strong infiltration of CD3+ was found in 27 (44%); of CD4+ in 28 (46%); of CD8+ in 26 (43%); and of CD20+ in 27 tumors (44%). Improved OS was observed for strong CD3+ (p < 0.001); CD4+ (p = 0.045); CD8+ (p = 0.001); and CD20+ infiltration (p < 0.001). Increased rates of PFS were observed for CD3+ (p = 0.025) and CD20+ TILs (p = 0.002). In multivariate analyses, strong CD3+ (HR: 0.163, CI: 0.044−0.614) and strong CD8+ TILs (HR: 0.265, CI: 0.081−0.864) were revealed as predictors for OS and the strong infiltration of CD20+ cells (HR: 0.095, CI: 0.019−0.464) for PFS. Conclusions: These first results of TILs in bladder SCC revealed predictive values of CD3+, CD8+ and CD20+.

Computational identification of immune-related lncRNA signature for predicting the prognosis and immune landscape of human glioblastoma multiforme

Emerging evidence shows immune-related long noncoding RNAs (ir-lncRNAs) perform critical roles in tumor progression and prognosis assessment. However, the identification of ir-lncRNAs and their clinical significance in human glioblastoma multiforme (GBM) remain largely unexplored. Here, a designed computational frame based on immune score was used to identify differentially expressed ir-lncRNAs (DEir-lncRNAs) from The Cancer Genome Atlas (TCGA) GBM program. The immune-related lncRNA signature (IRLncSig) composed of prognosis-related DEir-lncRNAs selected by Cox regression analysis and its clinical predictive values were verified, which was further validated by another dataset from the Gene Expression Omnibus database (GEO). Subsequently, the association between IRLncSig and immune cell infiltration, immune checkpoint inhibitor (ICI) biomarkers, O6-methylguanine-DNA methyltransferase (MGMT) gene expression, and biological function were also analyzed. After calculation, five prognosis-related ir-lncRNAs were included in the establishment of IRLncSig. The risk assessment based on IRLncSig indicated that the high-IRLncSig-score group was significantly associated with poor prognosis (p < 0.001), significant aggregation of macrophages (p < 0.05), higher ICI biomarker expression, and MGMT gene expression (p < 0.05). Signature-related lncRNAs may be involved in immune activities in the tumorigenesis and progression of GBM. In summary, the novel IRLncSig shows a promising clinical value in predicting the prognosis and immune landscape of GBM.

Tumor-infiltrating lymphocytes predict survival in ≥ pT2 urothelial bladder cancer

Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in several types of cancers, including genitourinary cancers. However, multiple different scoring methods used to assess TILs complicate the comparison of different studies and are not always suitable for daily practice. In 2014, the International TILs Working Group (ITWG) proposed a simple and robust assessment method for a more standardized evaluation of TILs. Here, we validated this system in muscle-invasive urinary bladder cancer (MIBC). Patient history and histologic specimens from 203 patients with MIBC were retrospectively analyzed. The stromal TIL (sTIL) score was determined using the ITWG system and 3 groups were defined according to the degree of stromal lymphocytic infiltration: low (0-10%), intermediate (10-55%) and high (55-100%). Associations between sTIL score, clinicopathological variables, tumor-specific survival (TSS), overall survival (OS), and disease-free survival (DFS) were analyzed. High stromal lymphocytic infiltration was associated with significantly higher OS, TSS and DFS when compared to low grade sTILs. The survival benefit remained statistically significant in multivariate analyses, confirming that sTILs are a strong independent positive prognostic factor in patients with MIBC. In summary, the degree of sTILs as defined by the ITWG robustly predicts survival in MIBC patients. Prospective studies with larger case numbers are needed to determine whether sTILs should be included in staging guidelines and how they could aid in therapeutic decision making.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

A single-sample mRNA molecular classification of bladder cancer predicting prognosis and response to immunotherapy

Background

As an immunogenic cancer, crosstalk between cancer cells and immune cells has been gradually recognized in bladder cancer (BC). Several studies have emphasized the clinical significance of the molecular stratification of BC without highlighting the role of the immune microenvironment. Although immunotherapy acted as a prospective treatment, more precise molecular stratification should be established to select those sensitive to immunotherapy.

Methods

To select specific immune genes forming subtypes indicating disparate prognoses, we performed bioinformatic analysis using BC transcriptomic profiles from six published datasets, with 408 BC samples in The Cancer Genome Atlas (TCGA) database and 295 individuals in International Cancer Genome Consortium (ICGC) database. Survival analyses were conducted using Kaplan-Meier curves, while Kruskal-Wallis tests were applied to test the differences among groups. Except for unsupervised clustering based on the differential expression of genes, we additionally performed binomial logistic regression, focusing on the mRNA level of a single sample.

Results

Unsupervised clustering showed that 4 clusters captured the best segmentation. After validation with survival data and simplification using binomial logistic regression, we found that cluster B and cluster D showed worse survival outcomes (P=0.012). Considering the similar survival outcomes of these two clusters, we recombined and performed another survival analysis, which also showed significant survival differences (P=0.0041). Bonding with clinical data, a greater proportion of risk factors were assigned to the worse prognosis subtype, especially showing higher grades in the subtype (P<0.001). In addition, immune cell infiltration, single nucleotide polymorphism (SNP) and copy number variation (CNV) all showed differences between clusters, indicating changes in the immune microenvironment and mutation burden. Through phenotypical analysis, we found metabolism and proliferation phenotypes associated with the immune clusters and mutually exclusive in BC, of which proliferation contributed to worse outcomes. Using the tumor immune dysfunction and exclusion (TIDE) score, a worse immunotherapy benefit was predicted in clusters B&D, defined as the worse prognosis subtype.

Conclusions

With this novel clustering criterion based on immune-related genes, we provide a better understanding of the immune microenvironment, further guiding the use of immunotherapy.

The Value of the Stemness Index in Ovarian Cancer Prognosis

Ovarian cancer (OC) is one of the most common gynecological malignancies. It is associated with a difficult diagnosis and poor prognosis. Our study aimed to analyze tumor stemness to determine the prognosis feature of patients with OC. At this job, we selected the gene expression and the clinical profiles of patients with OC in the TCGA database. We calculated the stemness index of each patient using the one-class logistic regression (OCLR) algorithm and performed correlation analysis with immune infiltration. We used consensus clustering methods to classify OC patients into different stemness subtypes and compared the differences in immune infiltration between them. Finally, we established a prognostic signature by Cox and LASSO regression analysis. We found a significant negative correlation between a high stemness index and immune score. Pathway analysis indicated that the differentially expressed genes (DEGs) from the low- and high-mRNAsi groups were enriched in multiple functions and pathways, such as protein digestion and absorption, the PI3K-Akt signaling pathway, and the TGF-β signaling pathway. By consensus cluster analysis, patients with OC were split into two stemness subtypes, with subtype II having a better prognosis and higher immune infiltration. Furthermore, we identified 11 key genes to construct the prognostic signature for patients with OC. Among these genes, the expression levels of nine, including SFRP2, MFAP4, CCDC80, COL16A1, DUSP1, VSTM2L, TGFBI, PXDN, and GAS1, were increased in the high-risk group. The analysis of the KM and ROC curves indicated that this prognostic signature had a great survival prediction ability and could independently predict the prognosis for patients with OC. We established a stemness index-related risk prognostic module for OC, which has prognostic-independent capabilities and is expected to improve the diagnosis and treatment of patients with OC.

Identification of an immune gene-associated prognostic signature in patients with bladder cancer

A deeper understanding of the interaction between tumor cell and the immune microenvironment in bladder cancer may help select predictive and prognostic biomarkers. The current study aims to construct a prognostic signature for bladder cancer by analysis of molecular characteristics, as well as tumor-immune interactions. RNA-sequencing and clinical information from bladder cancer patients were downloaded from the TCGA database. The single sample Gene Sets Enrichment Analysis (ssGSEA) and Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) were employed to separate the samples into two clusters. Lasso Cox regression was performed to construct an immune gene signature for bladder cancer. The correlation between key target genes of immune checkpoint blockade and the prognostic signature was also analyzed. Dataset from Gene Expression Omnibus (GEO) was retrieved for validation. Two immunophenotypes and immunological characteristics were identified, and a 17-immune gene signature was constructed to provide an independent prognostic signature for bladder cancer. The signature was verified through external validation and correlated with genomic characteristics and clinicopathologic features. Finally, a nomogram was generated from the clinical characteristics and immune signature. Our study reveals a tumor-immune microenvironment signature useful for prognosis in bladder cancer. The results provide information on the potential development of treatment strategies for bladder cancer patients. Prospective studies are warranted to validate the prognostic capability of this model, but these data highlight the role of the microenvironment in the clinical outcome of patients.

CD3D: a prognostic biomarker associated with immune infiltration and immunotherapeutic response in head and neck squamous cell carcinoma

Recent studies have demonstrated that CD3D activates T-cell-related signal transduction and is associated with the antitumor immune response in several cancers. This study explored the role of CD3D in head and neck squamous cell carcinoma (HNSCC). A total of 499 HNSCC tissues and 44 normal controls were acquired from The Cancer Genome Atlas as the training cohort. GSE65858 included 270 HNSCC patients and was obtained from the Gene Expression Omnibus database as the test cohort. Overall, 172 HNSCC patients were collected as the validation cohort. CD3D expression in the validation cohort was measured by quantitative real-time polymerase chain reaction. The Kaplan-Meier plot revealed that high CD3D expression was associated with longer overall survival in HNSCC patients. Univariate and multivariate analyses showed that CD3D expression was an independent prognostic factor for HNSCC patients, which was confirmed in the test cohort and validation cohort. Furthermore, GO, KEGG, and GSEA analyses revealed the association of CD3D with immune-related pathways. Subsequently, ESTIMATE analysis showed the association between CD3D and the tumor microenvironment, while ssGSEA showed a remarkable positive link between CD3D and immune-related functions. Multiple algorithms demonstrated that high CD3D expression was associated with more immune effector cell infiltration. Finally, the tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS) showed that patients with high CD3D could benefit from immunotherapy. In summary, CD3D was an independent favorable prognostic biomarker and correlated with immune cell infiltration and immune-related function, as well as an efficient indicator of immunotherapy response for HNSCC patients.

Pyroptosis-Related Signature Predicts Prognosis and Immunotherapy Efficacy in Muscle-Invasive Bladder Cancer

Pyroptosis has profound impacts on tumor cell proliferation, invasion, and metastasis and is of great clinical significance for different cancers. However, the role of pyroptosis in the progression and prognosis of muscle invasive bladder cancer (MIBC) remains poorly characterized. Here, we collected multicenter MIBC data and performed integrated analysis to dissect the role of pyroptosis in MIBC and provide an optimized treatment for this disease. Based on transcriptomic data, we developed a novel prognostic model named the pyroptosis-related gene score (PRGScore), which summarizes immunological features, genomic alterations, and clinical characteristics associated with the pyroptosis phenotype. Samples with high PRGScore showed enhancement in CD8⁺ T cell effector function, antigen processing machinery and immune checkpoint and better response to immunotherapy by programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors, which indicates that PRGScore is a valuable signature in the identification of populations sensitive to immune checkpoint inhibitors. Collectively, our study provides insights into further research targeting pyroptosis and its tumor immune microenvironment (TME) and offers an opportunity to optimize the treatment of MIBC.

An immune-related lncRNA model for predicting prognosis, immune landscape and chemotherapeutic response in bladder cancer

Long noncoding RNAs (lncRNAs) participate in cancer immunity. We characterized the clinical significance of an immune-related lncRNA model and evaluated its association with immune infiltrations and chemosensitivity in bladder cancer. Transcriptome data of bladder cancer specimens were employed from The Cancer Genome Atlas. Dysregulated immune-related lncRNAs were screened via Pearson correlation and differential expression analyses, followed by recognition of lncRNA pairs. Then, a LASSO regression model was constructed, and receiver operator characteristic curves of one-, three- and five-year survival were established. Akaike information criterion (AIC) value of one-year survival was determined as the cutoff of high- and low-risk subgroups. The differences in survival, clinical features, immune cell infiltrations and chemosensitivity were compared between subgroups. Totally, 90 immune-related lncRNA pairs were identified, 15 of which were screened for constructing the prognostic model. The area under the curves of one-, three- and five-year survival were 0.806, 0.825 and 0.828, confirming the favorable predictive performance of this model. According to the AIC value, we clustered patients into high- and low-risk subgroups. High-risk score indicated unfavorable outcomes. The risk model was related to survival status, age, stage and TNM. Compared with conventional clinicopathological characteristics, the risk model displayed higher predictive efficacy and served as an independent predictor. Also, it could well characterize immune cell infiltration landscape and predict immune checkpoint expression and sensitivity to cisplatin and methotrexate. Collectively, the model conducted by paring immune-related lncRNAs regardless of expressions exhibits a favorable efficacy in predicting prognosis, immune landscape and chemotherapeutic response in bladder cancer.

Guanylate-Binding Protein 1 as a Potential Predictor of Immunotherapy: A Pan-Cancer Analysis

Background: Mainstream application of cancer immunotherapy is hampered by the low response rate of most cancer patients. A novel immunotherapeutic target or a biomarker predicting response to immunotherapy needs to be developed. Guanylate-binding protein 1 (GBP1) is an interferon (IFN)-inducible guanosine triphosphatases (GTPases) involving inflammation and infection. However, the immunological effects of GBP1 in pan-cancer patients are still obscure.

Methods: Using large-scale public data, we delineated the landscape of GBP1 across 33 cancer types. The correlation between GBP1 expression or mutation and immune cell infiltration was estimated by ESTIMATE, TIMER, xCell, and quanTIseq algorithms. GBP1-related genes and proteins were subjected to function enrichment analysis. Clustering analysis explored the relationship between GBP1 expression and anti-tumor immune phenotypes. We assessed the patient’s response to immunotherapy using the tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS). Furthermore, we validated the predictive power of GBP1 expression in four independent immunotherapy cohorts.

Results: GBP1 was differentially expressed in tumors and normal tissues in multiple cancer types. Distinct correlations existed between GBP1 expression and prognosis in cancer patients. GBP1 expression and mutation were positively associated with immune cell infiltration. Function enrichment analysis showed that GBP1-related genes were enriched in immune-related pathways. Positive correlations were also observed between GBP1 expression and the expression of immune checkpoints, as well as tumor mutation burden (TMB). Pan-cancer patients with higher GBP1 expression were more inclined to display “hot” anti-tumor immune phenotypes and had lower TIDE scores and higher immunophenoscore, suggesting that these patients had better responses to immunotherapy. Patients with higher GBP1 expression exhibited improved overall survival and clinical benefits in immunotherapy cohorts, including the Gide et al. cohort [area under the curve (AUC): 0.813], the IMvigor210 cohort (AUC: 0.607), the Lauss et al. cohort (AUC: 0.740), and the Kim et al. cohort (AUC: 0.793).

Conclusion: This study provides comprehensive insights into the role of GBP1 in a pan-cancer manner. We identify GBP1 expression as a predictive biomarker for immunotherapy, potentially enabling more precise and personalized immunotherapeutic strategies in the future.

Characterization of an Autophagy-Immune Related Genes Score Signature and Prognostic Model and its Correlation with Immune Response for Bladder Cancer

Purpose

The study aimed to identify an autophagy-related molecular subtype and characterize a novel defined autophagy-immune related genes score (AI-score) signature and prognosis model in bladder cancer (BLCA) patients using public databases.

Methods

The transcriptome cohorts downloaded from TCGA and GEO database were carried out with genomic analysis and unsupervised methods to obtain autophagy-related molecular subtypes. The single-sample gene-set enrichment analysis (ssGSEA) was utilized to perform immune subtype clustering. We defined a novel autophagy subtype and evaluated the role in TME cell infiltration. Then, the principal-component analysis (PCA) was applied to construct an AI-score signature. Subsequently, two immunotherapeutic cohorts were used to evaluate the predictive value in immunotherapeutic benefits and immune response. Finally, univariate, Lasso and multivariate Cox regression algorithm were used to construct and evaluate an autophagy-immune-related genes prognosis model. Also, qRT-PCR and IHC was applied to validate the expression of the 6 genes in the model.

Results

Three distinct autophagy clusters and immune-related clusters were identified, and a novel autophagy-related molecular subtypes were defined. Furthermore, the roles in TME cell infiltration and clinical traits for the autophagy subtypes were characterized. Meanwhile, we constructed an AI-score signature and demonstrated it could predict genetic mutation, clinicopathological traits, prognosis, and TME stromal activity. We found that it could accurately predict the clinicopathological characteristics and immune response of individual BLCA patients and provide guidance for selecting immunotherapy. Ultimately, we constructed and verified an autophagy-immune-related prognostic model of BLCA patients and established a prognostic nomogram with a good prediction accuracy.

Conclusion

We constructed AI-score signatures and prognosis risk model to characterize their role in clinical features and TME immune cell infiltration. It revealed that the AI-score signature and prognosis model could be a valid predictive tool, which could accurately predict the prognosis of BLCA patients and contribute to choosing effective personalized immunotherapy strategies.

GBP4 is an immune-related biomarker for patients with ileocolonic Crohn’s disease by comprehensive analysis

Background: Extensive evidence has shown that immune cell infiltration is associated with the pathogenesis of Crohn’s disease (CD).

Methods: Differentially expressed genes (DEGs) from the GSE179285 dataset in the intestinal mucosa of CD patients and healthy individuals were then identified. The infiltration pattern of 22 immune cell types was assessed using the CIBERSORT algorithm. The DEGs and 22 immune cell types were combined to find the key gene network using weighted gene co-expression network analysis (WGCNA). A linear regression model for the relationship between the expression of the hub genes in CD patients and infiltration of immune cells was also developed. The utility and accuracy of the hub genes for CD diagnosis were assessed using receiver operating characteristic (ROC) analysis. The accuracy of the model was validated using the GSE20881 dataset.

Results: There were 1135 DEGs between the intestinal mucosal tissue of CD patients and healthy individuals. Of these DEGs, 711 genes were upregulated, whereas 424 of them were downregulated. There was also a significant difference in the infiltration of immune cells to the intestinal mucosal between the CD patients and healthy individuals. WGCNA revealed that the turquoise module genes were strongly correlated with the infiltration of M1 macrophages (cor =0.68, p = 10−16). Finally, the expression of GBP4, the identified hub gene, strongly correlated with the infiltration of M1 macrophages (adjusted r-squared =0.661, p < 2×10−16), and is a relatively good marker for CD diagnostic prediction (AUC =0.736). The relationship between GBP4 expression and infiltration of M1 macrophages (adjusted r-squared =0.435, p < 2×10−16) and diagnostic value of the gene (AUC =0.702) were verified using the GSE20881 validation dataset.

Conclusion: The expression of GBP4 is associated with the infiltration of M1 macrophages to the intestinal mucosa of CD patients.

A Novel Immune-Gene Pair Signature Revealing the Tumor Microenvironment Features and Immunotherapy Prognosis of Muscle-Invasive Bladder Cancer

Immunotherapy has been a milestone for muscle-invasive bladder cancer (MIBC), but only a small portion of patients can benefit from it. Therefore, it is crucial to develop a robust individualized immune-related signature of MIBC to identify patients potentially benefiting from immunotherapy. The current study identified patients from the Cancer Genome Atlas (TCGA) and immune genes from the ImmPort database, and used improved data analytical methods to build up a 45 immune-related gene pair signature, which could classify patients into high-risk and low-risk groups. The signature was then independently validated by a Gene Expression Omnibus (GEO) dataset and IMvigor210 data. The subsequent analysis confirmed the worse survival outcomes of the high-risk group in both training (p < 0.001) and validation cohorts (p = 0.018). A signature-based risk score was proven to be an independent risk factor of overall survival (p < 0.001) and could predict superior clinical net benefit compared to other clinical factors. The CIBERSORT algorithm revealed the low-risk group had increased CD8⁺ T cells plus memory-activated CD4⁺ T-cell infiltration. The low-risk group also had higher expression of PDCD1 (PD-1), CD40, and CD27, and lower expression of CD276 (B7-H3) and PDCD1LG2 (PD-L2). Importantly, IMvigor210 data indicated that the low-risk group had higher percentage of “inflamed” phenotype plus less “desert” phenotype, and the survival outcomes were significantly better for low-risk patients after immunotherapy (p = 0.014). In conclusion, we proposed a novel and promising prognostic immune-related gene pair (IRGP) signature of MIBC, which could provide us a panoramic view of the tumor immune microenvironment of MIBC and independently identify MIBC patients who might benefit from immunotherapy.

DIO3OS as a potential biomarker of papillary thyroid cancer

BACKGROUND

Papillary thyroid carcinoma (PTC) is one of the common clinical tumors, where LncRNA plays an important role in tumorigenesis and its development. The purpose of this study was to explore the role of DIO3OS in PTC.

METHOD

Firstly, this study verified the expression of DIO3OS in PTC through the public database. Then, the differences in DIO3OS expression between the PTC group and paracancerous tissues were verified using the qRT-PCR. A series of in vitro experiments were conducted to verify the function of DIO3OS in PTC, while its involvement in possible pathways was analyzed by the GSEA. The ssGSEA algorithm estimated the immune status using the queue transcriptome graph derived from the TCGA database. Further, the correlation analysis was used to confirm the relationship between DIO3OS and the immune genes.

RESULT

The results showed that the expression of DIO3OS was low in PTC. The same results were also confirmed by qRT-PCR analysis (P= 0.0077). In vitro, DIO3OS was localized within the cytoplasm and exosomes. Overexpression of DIO3OS hindered the proliferation, invasion, and migration of PTC cells. According to the degree of immune cell infiltration, the tumor group was divided into high immune cell infiltration group, medium immune cell infiltration group, and low immune cell infiltration group. The results showed that the DIO3OS was highly expressed in the high immune cell infiltration group (P < 0.001), which was positively correlated with the immune cell infiltration and also correlated with multiple immune genes.

CONCLUSION

In summary, this study illustrated the expression pattern of DIO3OS in PTC, which may be involved in the immune-inflammatory pathway. Hence, our results may provide new diagnostic biomarkers and therapeutic targets for PTC.

Downregulation of the Coiled-Coil Domain Containing 80 and Its Perspective Mechanisms in Ovarian Carcinoma: A Comprehensive Study

INTRODUCTION

We aimed to explore the downregulation of the coiled-coil domain containing 80 (CCDC80) and its underlying molecular mechanisms in ovarian carcinoma (OVCA). Materials/Methods. Immunohistochemical staining was performed to confirm the expression status of CCDC80 protein. Combining the data from in-house tissue microarrays and high-throughput datasets, we identified the expression level of CCDC80 in OVCA. We utilized cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm and single-sample gene set enrichment analysis (ssGSEA) to explore the relationship between CCDC80 and the tumor microenvironment (TME) landscape in OVCA. Pathway enrichment, function annotation, and transcription factor (TFs) exploration were conducted to study the latent molecular mechanisms. Moreover, the cell line data in the Genomics of Drug Sensitivity in Cancer (GDSC) database was used to discover the relationship between CCDC80 and drug sensitivity.

RESULTS

An integrated standard mean difference (SMD) of -0.919 (95% CI: -1.515-0.324, P = 0.002) identified the downregulation of CCDC80 in OVCA based on 1048 samples, and the sROC (AUC = 0.76) showed a moderate discriminatory ability of CCDC80 in OVCA. The fraction of infiltrating naive B cells showed significant differences between the high- and low-CCDC80 expression groups. Also, CCDC80-related genes are enriched in the Ras signaling pathway and metabolic of lipid. Nuclear receptor subfamily three group C member 1 (NR3C1) may be an upstream TF of CCDC80, and CCDC80 may be related to the sensitivity of mitocycin C and nilotinib.

CONCLUSION

CCDC80 was downregulated in OVCA and may play a role as a tumor suppressor in OVCA.

Development and Validation of Prognostic Model in Transitional Bladder Cancer Based on Inflammatory Response-Associated Genes

Background

Bladder cancer is a common malignant type in the world, and over 90% are transitional cell carcinoma. While the impact of inflammatory response on cancer progression has been reported, the role of inflammatory response-associated genes (IRAGs) in transitional bladder cancer still needs to be understood.

Methods

In this study, IRAGs were download from Molecular Signature Database (MSigDB). The transcriptional expression and matched clinicopathological data were separately obtained from public databases. The TCGA-BLCA cohort was used to identify the differentially expressed IRAGs, and prognostic IRAGs were filtrated by univariate survival analysis. The intersection between them was displayed by Venn diagram. Based on least absolute shrinkage and selection operator (LASSO) regression analysis method, the TCGA-BLCA cohort was used to construct a risk signature. Survival analysis was conducted to calculate the overall survival (OS) in TCGA and GSE13507 cohort between two groups. We then conducted univariate and multivariate survival analyses to identify independently significant indicators for prognosis. Relationships between the risk scores and age, grade, stage, immune cell infiltration, immune function, and drug sensitivity were demonstrated by correlation analysis. The expression level of prognostic genes in vivo and in vitro were determined by qRT-PCR assay.

Results

Comparing with normal tissues, there were 49 differentially expressed IRAGs in cancer tissues, and 12 of them were markedly related to the prognosis in TCGA cohort for transitional bladder cancer patients. Based on LASSO regression analysis, a risk model consists of 10 IRAGs was established. Comparing with high-risk groups, survival analysis showed that patients in low-risk groups were more likely to have a better survival time in TCGA and GSE13507 cohorts. Besides, the accuracy of the model in predicting prognosis is acceptable, which is demonstrated by receiver operating characteristic curve (ROC) analysis. Age, stage, and risk scores variables were identified as the independently significant indicators for survival in transitional bladder cancer. Correlation analysis represented that the risk score was identified to be significantly related to the above variables except gender variable. Moreover, the expression level of prognostic genes in vivo and in vitro was markedly upregulated for transitional bladder cancer.

Conclusions

A novel model based on the 10 IRAGs that can be used to predict survival time for transitional bladder cancer. In addition, this study may provide treatment strategies according to the drug sensitivity in the future.

An immune cell infiltration-related gene signature predicts prognosis for bladder cancer

To explore novel therapeutic targets, develop a gene signature and construct a prognostic nomogram of bladder cancer (BCa). Transcriptome data and clinical traits of BCa were downloaded from UCSC Xena database and Gene Expression Omnibus (GEO) database. We then used the method of Single sample Gene Set Enrichment analysis (ssGSEA) to calculate the infiltration abundances of 24 immune cells in eligible BCa samples. By weighted correlation network analysis (WGCNA), we identified turquoise module with strong and significant association with the infiltration abundance of immune cells which were associated with overall survival of BCa patients. Subsequently, we developed an immune cell infiltration-related gene signature based on the module genes (MGs) and immune-related genes (IRGs) from the Immunology Database and Analysis Portal (ImmPort). Then, we tested the prognostic power and performance of the signature in both discovery and external validation datasets. A nomogram integrated with signature and clinical features were ultimately constructed and tested. Five prognostic immune cell infiltration-related module genes (PIRMGs), namely FPR1, CIITA, KLRC1, TNFRSF6B, and WFIKKN1, were identified and used for gene signature development. And the signature showed independent and stable prognosis predictive power. Ultimately, a nomogram consisting of signature, age and tumor stage was constructed, and it showed good and stable predictive ability on prognosis. Our prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for BCa. Further researches are needed to verify the clinical effectiveness of this nomogram and these biomarkers.

A robust signature of immune-related long non-coding RNA to predict the prognosis of bladder cancer

Background

Bladder cancer is the second most common malignant tumor in the urogenital system. The research investigated the prognostic role of immune‐related long non‐coding RNA (lncRNA) in bladder cancer.

Methods

We extracted 411 bladder cancer samples from The Cancer Genome Atlas database. Single‐sample gene set enrichment analysis was employed to assess the immune cell infiltration of these samples. We recognized differentially expressed lncRNAs between tumors and paracancerous tissues, and differentially expressed lncRNAs between the high and low immune cell infiltration groups. Venn diagram analysis detected differentially expressed lncRNAs that intersected the above groups. LncRNAs with prognostic significance were identified by regression analysis. Multivariate Cox analysis was used to establish the risk score model. Then we established and evaluated the nomogram. Additionally, we performed gene set enrichment analysis to explore the potential functions of the screened lncRNAs in tumor pathogenesis.

Results

Three hundred and twenty differentially expressed lncRNAs were recognized. We randomly divided patients into the training data set and the testing data set at a 2: 1 ratio. In the training data set, 9 immune‐related lncRNAs with prognostic significance were identified. The risk score model was constructed to classify patients as high‐ and low‐risk cohorts. Patients in the low‐risk cohort had better survival outcomes than those in the high‐risk cohort. The nomogram was established based on the indicators including age, gender, tumor‐node‐metastases stage, and risk score. The model's predictive performance was confirmed by the receiver operating characteristic curve analysis, concordance index method, calibration curve, and decision curve analysis. The testing data set also achieved similar results. Bioinformatics analysis suggested that the 9‐lncRNA signature was involved in the modulation of various immune responses, antigen processing and presentation, and T cell receptor signaling pathway.

Conclusions

Our study uncovered the prognostic value of immune‐related lncRNAs for bladder cancer and showed that they may regulate tumor pathogenesis in various ways.

A combination of ssGSEA and mass cytometry identifies immune microenvironment in muscle-invasive bladder cancer

BACKGROUND

Muscle-invasive bladder cancer (MIBC) is a heterogeneous disease with varying clinical courses and responses to treatment. To improve the prognosis of patients, it is necessary to understand such heterogeneity.

METHODS

We used single-sample gene set enrichment analysis to classify 35 MIBC cases into immunity-high and immunity-low groups. Bioinformatics analyses were conducted to compare the differences between these groups. Eventually, single-cell mass cytometry (CyTOF) was used to compare the characteristics of the immune microenvironment between the patients in the two groups.

RESULTS

Compared with patients in the immunity-low group, patients in the immunity-high group had a higher number of tumor-infiltrating immune cells and greater enrichment of gene sets associated with antitumor immune activity. Furthermore, positive immune response-related pathways were more enriched in the immunity-high group. We identified 26 immune cell subsets, including cytotoxic T cells (Tcs), helper T cells (Ths), regulatory T cells (Tregs), B cells, macrophages, natural killer (NK) cells, and dendritic cells (DCs) using CyTOF. Furthermore, there was a higher proportion of CD45+ lymphocytes and enrichment of one Tc subset in the immunity-high group. Additionally, M2 macrophages were highly enriched in the immunity-low group. Finally, there was higher expression of PD-1 and Tim-3 on Tregs as well as a higher proportion of PD-1+ Tregs in the immunity-low group than in the immunity-high group.

CONCLUSION

In summary, the immune microenvironments of the immunity-high and immunity-low groups of patients with MIBC are heterogeneous. Specifically, immune suppression was observed in the immune microenvironment of the patients in the immunity-low group.

Functional Roles of SPINK1 in Cancers

Serine Peptidase Inhibitor Kazal Type 1 (SPINK1) is a secreted protein known as a protease inhibitor of trypsin in the pancreas. However, emerging evidence shows its function in promoting cancer progression in various types of cancer. SPINK1 modulated tumor malignancies and induced the activation of the downstream signaling of epidermal growth factor receptor (EGFR) in cancer cells, due to the structural similarity with epidermal growth factor (EGF). The discoverable SPINK1 somatic mutations, expressional signatures, and prognostic significances in various types of cancer have attracted attention as a cancer biomarker in clinical applications. Emerging findings further clarify the direct and indirect biological effects of SPINK1 in regulating cancer proliferation, metastasis, drug resistance, transdifferentiation, and cancer stemness, warranting the exploration of the SPINK1-mediated molecular mechanism to identify a therapeutic strategy. In this review article, we first integrate the transcriptomic data of different types of cancer with clinical information and recent findings of SPINK1-mediated malignant phenotypes. In addition, a comprehensive summary of SPINK1 expression in a pan-cancer panel and individual cell types of specific organs at the single-cell level is presented to indicate the potential sites of tumorigenesis, which has not yet been reported. This review aims to shed light on the roles of SPINK1 in cancer and provide guidance and potential directions for scientists in this field.

A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma

Background

Cutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.

Methods

Gene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.

Results

A total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P < 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P < 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P< 0.001) and ulceration (P< 0.001).

Conclusions

The four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.

Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

Background

Tumor microenvironment (TME) is closely related to the progression of glioma and the therapeutic effect of drugs on this cancer. The aim of this study was to develop a signature associated with the tumor immune microenvironment using machine learning.

Methods

We downloaded the transcriptomic and clinical data of glioma patients from the Chinese Glioma Genome Atlas (CGGA) databases (mRNAseq_693). The single-sample Gene Set Enrichment Analysis (ssGSEA) database was used to quantify the relative abundance of immune cells. We divided patients into two different infiltration groups via unsupervised clustering analysis of immune cells and then selected differentially expressed genes (DEGs) between the two groups. Survival-related genes were determined using Cox regression analysis. We next randomly divided patients into a training set and a testing set at a ratio of 7 : 3. By integrating the DEGs into least absolute shrinkage and selection operator (LASSO) regression analysis in the training set, we were able to construct a 15-gene signature, which was validated in the testing and total sets. We further validated the signature in the mRNAseq_325 dataset of CGGA.

Results

We identified 74 DEGs associated with tumor immune infiltration, 70 of which were significantly associated with overall survival (OS). An immune-related gene signature was established, consisting of 15 key genes: adenosine triphosphate (ATP)-binding cassette subfamily C member 3 (ABCC3), collagen type IV alpha 1 chain (COL4A1), podoplanin (PDPN), annexin A1 (ANXA1), COL4A2, insulin-like growth factor binding protein 2 (IGFBP2), serpin family A member 3 (SERPINA3), CXXC-type zinc finger protein 11 (CXXC11), junctophilin 3 (JPH3), secretogranin III (SCG3), secreted protein acidic and rich in cysteine (SPARC)-related modular calcium-binding protein 1 (SMOC1), Cluster of Differentiation 14 (CD14), COL1A1, S100 calcium-binding protein A4 (S100A4), and transforming growth factor beta 1 (TGF-β1). The OS of patients in the high-risk group was worse than that of patients in the low-risk group. GSEA showed that interleukin-6 (IL-6)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling, interferon gamma (IFN-γ) response, angiogenesis, and coagulation were more highly enriched in the high-risk group and that oxidative phosphorylation was more highly enriched in the low-risk group.

Conclusion

We constructed a stable gene signature associated with immune infiltration to predict the survival rates of glioma patients.

Identification of a prognostic signature based on immune-related genes in bladder cancer

Bladder cancer (BLCA) has a high incidence and recurrence rate, and the effect of immunotherapy varies from person to person. Immune-related genes (IRGs) have been shown to be associated with immunotherapy and prognosis in many other cancers, but their role in immunogenic BLCA is less well defined. In this study, we constructed an eight-IRG risk model, which demonstrated strong prognostic and immunotherapeutic predictive power. The signature was significantly related to tumor clinicopathological characteristics, tumor class, immune cell infiltration and mutation status. Additionally, a nomogram containing the risk score and other potential risk factors could effectively predict the long-term overall survival probability of BLCA patients. The enriched mechanisms identified by gene set enrichment analysis suggested that the reason why this signature can accurately distinguish high- and low-risk populations may be closely related to the different degrees of innate immune response and T cell activation in different patients.

Preclinical analysis of novel prognostic transcription factors and immune-related gene signatures for bladder cancer via TCGA-based bioinformatic analysis

Bladder cancer (BLCA) is a common malignancy of human urinary tract, whose prognosis is influenced by complex gene interactions. Immune response activity can act as a potential prognostic factor in BLCA. The present study established a prognostic model, based on the identification of tumor transcription factors (TFs) and immune-related genes (IRGs), and further explored their therapeutic potential in BLCA. The enrichment scores of 29 IRG sets, identified in The Cancer Genome Atlas BLCA tumor samples, were quantified by single-sample Gene Set Enrichment Analysis. The abundance of infiltrated immune cells in tumor tissues was determined using the Estimating Relative algorithm. Tumor-related TFs and IRGs signatures were retrieved using Least Absolute Shrinkage and Selection Operator Cox regression analysis. A prognostic gene network was built using Pearson's correlation analysis as a means of predicting the regulatory relationship between prognostic TFs and IRGs. A nomogram was devised to also predict the overall survival (OS) rate of patients with BLCA. Based on the Genomics of Drug Sensitivity in Cancer data, potential therapeutic drugs were identified upon analyzing the relationship between the expression level of prognostic genes and respective IC₅₀ values. In vitro experiments were implemented for further validation. Respective TF binding profiles were acquired from the JASPAR 2020 database. The elevated infiltration of CD8⁺ T Cells was correlated with an improved OS of patients with BLCA. An innovative prognostic model for BLCA was then constructed that composed of nine putative gene markers: CXCL13, prepronociceptin, microtubule-associated protein tau, major histocompatibility class I polypeptide-related sequence B, prostaglandin E2 receptor EP3 subtype, IL20RA, proepiregulin, early growth response protein 1 and FOS-related antigen 1 (FOSL1). Furthermore, a theoretical basis for the correlation between the prognostic TFs and IRGs was reported. For this, 10 potentially effective drugs targeting the TFs in the present model for patients with BLCA were identified. It was then verified that downregulation of FOSL1 can lead to an enhanced sensitivity of the TW37 in T24 bladder cancer cells. Overall, the present prognostic model demonstrated a robust capability of predicting OS of patients with BLCA. Hence, the gene markers identified could be applied for targeted therapies against BLCA.

A Novel TGF-β Risk Score Predicts the Clinical Outcomes and Tumour Microenvironment Phenotypes in Bladder Cancer

Background

The TGF-β pathway plays critical roles in numerous malignancies. Nevertheless, its potential role in prognosis prediction and regulating tumour microenvironment (TME) characteristics require further elucidation in bladder cancer (BLCA).

Methods

TGF-β-related genes were comprehensively summarized from several databases. The TCGA-BLCA cohort (training cohort) was downloaded from the Cancer Genome Atlas, and the independent validation cohorts were gathered from Xiangya Hospital (Xinagya cohort) and Gene Expression Omnibus. Initially, we identified differentially expressed TGF-β genes (DEGs) between cancer and normal tissues. Subsequently, univariate Cox analysis was applied to identify prognostic DEGs, which were further used to develop the TGF-β risk score by performing LASSO and multivariate Cox analyses. Then, we studied the role of the TGF-β risk score in predicting prognosis and the TME phenotypes. In addition, the role of the TGF-β risk score in guiding precision treatments for BLCA has also been assessed.

Results

We successfully constructed a TGF-β risk score with an independent prognostic prediction value. A high TGF-β risk score indicated an inflamed TME, which was supported by the positive relationships between the risk score, enrichment scores of anticancer immunity steps, and the infiltration levels of tumour-infiltrating immune cells. In addition, the risk score positively correlated with the expression of several immune checkpoints and the T cell inflamed score. Consistently, the risk score was positively related to the enrichment scores of most immunotherapy-positive pathways. In addition, the sensitivities of six common chemotherapeutic drugs were positively associated with the risk score. Furthermore, higher risk score indicated higher sensitivity to radiotherapy and EGFR-targeted therapy. On the contrary, patients with low-risk scores were more sensitive to targeted therapies, including the blockade of FGFR3 and WNT-β-catenin networks.

Conclusions

We first constructed and validated a TGF-β signature that could predict the prognosis and TME phenotypes for BLCA. More importantly, the TGF-β risk score could aid in individual precision treatment for BLCA.

A TP53-Associated Immune Prognostic Signature for the Prediction of Overall Survival and Therapeutic Responses in Muscle-Invasive Bladder Cancer

Background

TP53 gene mutation is one of the most common mutations in human bladder cancer (BC) and has been implicated in the progression and prognosis of BC.

Methods

RNA sequencing data and TP53 mutation data in different populations and platforms were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database to determine and validate a TP53-associated immune prognostic signature (TIPS) based on differentially expressed immune-related genes (DEIGs) between muscle-invasive bladder cancer (MIBC) patients with and without TP53 mutations.

Results

A total of 99 DEIGs were identified based on TP53 mutation status. TIPS including ORM1, PTHLH, and CTSE were developed and validated to identify high-risk prognostic group who had a poorer prognosis than low-risk prognostic group in TCGA and GEO database. The high-risk prognostic group were characterized by a higher abundance of regulatory T cells, myeloid-derived suppressor cells, and tumor-associated macrophages than the low-risk prognostic group. Moreover, they exhibited a lower abundance of CD56bright NK cells, higher expression of CTLA4, LAG3, PDCD1, TIGIT, and HAVCR2, as well as being more likely to respond to anti–PD-1, and neoadjuvant chemotherapy than the low-risk prognostic group. Based on TIPS and other clinical characteristics, a nomogram was constructed for clinical use.

Conclusion

TIPS derived from TP53 mutation status is a potential prognostic signature or therapeutic target but additional prospective studies are necessary to confirm this potential.

A signature of tumor DNA repair genes associated with the prognosis of surgically-resected lung adenocarcinoma

Background

Lung cancer has the highest morbidity and mortality of cancers worldwide. Lung adenocarcinoma (LUAD) is the most common pathological subtype of lung cancer and surgery is its most common treatment. The dysregulated expression of DNA repair genes is found in a variety of cancers and has been shown to affect the origin and progression of these diseases. However, the function of DNA repair genes in surgically-treated LUAD is unclear.

Methods

We sought to determine the association between the signature of DNA repair genes for patients with surgical LUAD and their overall prognosis. We obtained gene expression data and corresponding clinical information of LUAD from The Cancer Genome Atlas (TCGA) database. The differently expressed DNA repair genes of surgically-treated LUAD and normal tissues were identified using the Wilcoxon rank-sum test. We used uni- and multivariate Cox regression analyses to shrink the aberrantly expressed genes, which were then used to construct the prognostic signature and the risk score formula associated with the independent prognosis of surgically-treated LUAD. We used Kaplan-Meier and Cox hazard ratio analyses to confirm the diagnostic and prognostic roles. Two validation sets (GSE31210 and GSE37745) were downloaded from the Gene Expression Omnibus (GEO) and were used to externally verify the prognostic value of the signature. OSluca online database verifies the hazard ratio for the DNA repair genes by which the signature was constructed. We investigated the correlation between the signature of the DNA repair genes and the clinical parameters. The potential molecular mechanisms and pathways of the prognostic signature were explored using Gene Set Enrichment Analysis (GSEA).

Results

We determined the prognostic signature based on six DNA repair genes (PLK1, FOXM1, PTTG1, CCNO, HIST3H2A, and BLM) and calculated the risk score based on this formula. Patients with surgically-treated LUAD were divided into high-risk and low-risk groups according to the median risk score. The high-risk group showed poorer overall survival than the low-risk group; the signature was used as an independent prognostic indicator and had a greater prognostic value in surgically-treated LUAD. The prognostic value was replicated in GSE31210 and GSE37745. OSluca online database analysis shows that six DNA repair genes were associated with poor prognosis in most lung cancer datasets. The prognostic signature risk score correlated with the pathological stage and smoking status in surgically-treated LUAD. The GSEA of the risk signature in high-risk patients showed pathways associated with the cell cycle, oocyte meiosis, mismatch repair, homologous recombination, and nucleotide excision repair.

Conclusions

A six-DNA repair gene signature was determined using TCGA data mining and GEO data verification. The gene signature may serve as a novel prognostic biomarker and therapeutic target for surgically-treated LUAD.

An immune landscape based prognostic signature predicts the immune status and immunotherapeutic responses of patients with colorectal cancer

AIMS

Colorectal cancer (CRC) is one of the most common cancers with poor prognosis worldwide. The advent of immunotherapy has greatly improved survival in refractory patients of CRC. In this study, we aimed to identify reliable immune classification and biomarkers that predict immunotherapeutic responses in CRC patients.

MATERIALS AND METHODS

Based on transcriptome profiles of two publicly available CRC datasets, we performed single-sample gene set enrichment analysis (ssGSEA) to calculate the relative abundance of 29 immune-related items of each sample. Unsupervised clustering was used to classify CRC patients. Furthermore, an immune prognostic signature was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis.

KEY FINDINGS

The CRC patients were clustered into high, medium, low immune infiltration subtypes based on the immune landscape. There was significant heterogeneity among the three subtypes. The high immune infiltration group showed higher expression of programmed cell death-ligand 1 and better prognosis than the median and low immune infiltration groups. Furthermore, we constructed a 7-immune-related prognostic gene signature and found that the signature had high predictive value and was superior to other clinicopathological parameters. Finally, the correlation analysis of the signature with immune cell infiltration and immune checkpoint molecules suggested that the signature had the potential to assess the immunotherapeutic responses of CRC patients.

SIGNIFICANCE

The immune landscape and prognostic signature of CRC contribute to a deeper understanding of the tumor microenvironment and guide accurate immunotherapy.

An immune relevant signature for predicting prognoses and immunotherapeutic responses in patients with muscle-invasive bladder cancer (MIBC)

Immune checkpoint inhibitors (ICIs) are novel treatments that significantly improve the survival time of MIBC patients, but immunotherapeutic responses are different among MIBC patients. Therefore, it is urgent to find predictive biomarkers that can accurately identify MIBC patients who are sensitive to ICIs. In this study, we computed the relative abundances of 24 immune cells based on the expression profiles of MIBC patients using single‐sample gene set enrichment analysis (ssGSEA). Unsupervised clustering analysis of the 24 immune cells was performed to classify MIBC patients into different immune‐infiltrating groups. Genome (gene mutation and copy number variation), transcriptome (mRNA, lncRNA, and miRNA), and functional enrichment were found to be heterogeneous among different immune‐infiltrating groups. We identified 282 differentially expressed genes (DEGs) associated with immune infiltration by comparing the expression profiles of patients with different immune infiltration profiles, and 20 core prognostic DEGs were identified by univariate Cox regression analysis. An immune‐relevant gene signature (TIM signature) consisting of nine key prognostic DEGs (CCDC80, CD3D, CIITA, FN1, GBP4, GNLY, SPINK1, UBD, and VIM) was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Receiver operating characteristic (ROC) curves and subgroup analysis confirmed that the TIM signature was an ideal biomarker for predicting the prognosis of MIBC patients. Its value in predicting immunotherapeutic responses was also validated in The Cancer Genome Atlas (TCGA) cohort (AUC = 0.69, 95% CI = 0.63‐0.74) and the IMvigor210 cohort (AUC = 0.64, 95% = 0.55‐0.74). The TIM signature demonstrates a powerful ability to distinguish MIBC patients with different prognoses and immunotherapeutic responses, but more prospective studies are needed to assess its reliability in the future.