IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis

NALCN expression is down-regulated and associated with immune infiltration in gastric cancer

Background

NALCN has been identified as a tumor suppressor gene, and its role in human cancer progression has garnered significant attention. However, there is a paucity of experimental studies specifically addressing the relationship between NALCN and immune cell infiltration in gastric cancer (GC).

Methods

The expression levels of NALCN in tumor tissues, peripheral blood and gastric cancer cells lines from patients with GC were assessed using RNA sequencing, immunohistochemistry (IHC) staining and RT-qPCR. Data obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were utilized to investigate the correlation between NALCN expression and immune cell infiltration in GC. Subsequently, the relationship between NALCN expression and infiltrating immune cells in GC tissues was examined through immunofluorescence method. Additionally, in vitro experiments were conducted to evaluate the impact of NALCN knockdown on T cells function in GC cell lines.

Results

RNA sequencing analysis revealed that NALCN expression was significantly downregulated in GC tissues. Specifically, NALCN levels were lower in GC tumor tissues and plasma compared to adjacent non-tumor tissues and healthy controls. Consistent with these findings, the expression trend of NALCN mRNA in the GEO database mirrored the experimental results. Mechanistically, NALCN knockdown markedly enhanced cell proliferation, colony formation and migration while reducing apoptosis rates in AGS and GES-1 cells. Analysis of the TCGA database indicated a positive correlation between NALCN expression and the infiltration of B cells, cytotoxic cells, immature dendritic cells (iDC) cells, CD8+ T cells, and others in GC tissue. Conversely, Th17 and Th2 cells infiltration exhibited a negative correlation with NALCN expression. Immunofluorescence staining confirmed that B cells and CD8 T cells were more abundant in GC tumor tissues with high NALCN expression, whereas Th17 and Th2 cells were less prevalent. Subsequently, we co-cultured GC cells transfected with NALCN knockdown or control vectors along with their supernatants with T cells. The results demonstrated that NALCN knockdown in GC cells or their supernatants inhibited T cell proliferation compared to control conditions. Moreover, NALCN may play a role in glucose and glutamine uptake.

Conclusions

NALCN facilitates immune cell aggregation in GC and has potential as a biomarker for immune infiltration.

Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma

BACKGROUND

Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients.

METHODS

DC-related biological functions and genes were identified using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing. DCs-related gene signature (DCRGS) was constructed using integrated machine learning algorithms. Expression of key genes in clinical samples was examined by real-time q-PCR. Performance of the prognostic model, DCRGS, for the prognostic evaluation, was assessed using a multiple time-dependent receiver operating characteristic (ROC) curve, the R package, "timeROC", and validated using GEO datasets.

RESULTS

Analysis of scRNA-seq data showed that there is a significant upregulation of LGALS9 expression in DCs isolated from malignant pleural effusion samples. Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. A high predictive capability nomogram was constructed by combining DCRGS with clinical features. We found that patients with a high-DCRGS score had immunosuppression, activated tumor-associated pathways, and elevated somatic mutational load and copy number variant load. In contrast, patients in the low-DCRGS subgroup were resistant to chemotherapy but sensitive to the CTLA-4 immune checkpoint inhibitor and targeted therapy.

CONCLUSION

We have innovatively established a deep learning-based prediction model, DCRGS, for the prediction of the prognosis of patients with LUAD. The model possesses a strong prognostic prediction performance with high accuracy and sensitivity and could be clinically useful to guide the management of LUAD. Furthermore, the findings of this study could provide an important reference for individualized clinical treatment and prognostic prediction of patients with LUAD.

A nomogram for cancer-specific survival of lung adenocarcinoma patients: A SEER based analysis

Background

Non-small cell lung cancer (NSCLC) accounts for 85 % of lung cancer cases. Among NSCLC subtypes, lung adenocarcinoma (LUAD) stands as the most prevalent. Regrettably, LUAD continues to exhibit a notably unfavorable overall prognosis. This study's primary aim was to develop and validate prognostic tools capable of predicting the likelihood of cancer-specific survival (CSS) in patients with LUAD.

Methods

We retrospectively collected 21,099 patients diagnosed with LUAD between 2010 and 2015, and 8290 patients diagnosed between 2004 and 2009 from SEER database. The cohort of 21,099 patients served as the prognostic group for the exploration of LUAD-related prognostic risk factors. The cohort of 8290 patients was designated for external validation. We created a training set and an internal validation set in the prognostic group for the development and internal validation of CSS nomograms. CSS predictors were identified through the least absolute shrinkage and selection operator (Lasso) regression analysis. Prognostic model was constructed via Cox hazard regression analysis, presented in the form of both static and dynamic network-based nomograms.

Results

Several independent prognostic factors were incorporated into the construction of nomogram. The nomogram accurately predicted CSS at 1, 3, and 5 years, with respective AUC values of 0.769, 0.761, and 0.748 for the training group, and 0.741, 0.752, and 0.740 for the testing group. The study demonstrated a strong agreement between anticipated and actual CSS values, supported by decision curve analysis (DCA) and time-dependent calibrated curves. High-risk patients based on the nomogram exhibiting significantly lower survival rates compared to their low-risk counterparts according to Kaplan-Meier (K-M) curves. The nomogram demonstrates excellent predictive power in the external validation cohort.

Conclusions

A dependable and user-friendly nomogram has been developed, available in both static and online dynamic calculator formats, to facilitate healthcare professionals in accurately estimating the likelihood of CSS for patients diagnosed LUAD.

Identification of Immune-Related Gene Signature Model for Predicting Lung Cancer Survival and Response to Immunotherapy

INTRODUCTION

Studies have shown that immune-related genes play a crucial role in tumor development and treatment. However, the specific roles and potential value of these genes in lung cancer patients are still not fully understood. Therefore, this study aims to establish a novel risk model based on immune-related genes for evaluating the prognostic risk and response to immune therapy in lung cancer patients.

METHODS

Gene expression and clinical data of lung cancer patients were retrieved from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, while immune-related genes were obtained from the ImmPort database. A risk signature model was developed using univariate Cox analysis and LASSO regression analysis. The prognostic value of the model and its response to immunotherapy were analyzed by survival analysis, immune infiltration analysis, and immunotherapy response analysis.

RESULTS

We have developed a risk signature model based on eight key immune-related genes, which can classify patients into high-risk and low-risk groups. The prognosis of the high-risk group was significantly lower than that of the low-risk group and was validated in multiple GEO datasets. The mutation frequency was lower in the low-risk group compared to the high-risk group (TP53: 55% vs. 65%; TTN: 52% vs. 60%; CSMD3: 34% vs. 45%). Futhermore, CD274 expression was lower in the low-risk patients, and the high-risk patients in the IMvigor210 cohort had lower survival. Immune infiltration analyses showed that the high-risk group was negatively correlated with the infiltration level of B cells, CD4+ T cells, and NK cells. Importantly, patients in the low-risk group exhibit significantly lower TIDE scores, suggesting that they are more responsive to immunotherapy.

CONCLUSION

Our study has established a novel and robust immune-related gene risk model that can assist in evaluating the prognostic risk and immune therapy response of lung cancer patients.

Identification and prognostic analysis of ferroptosis‑related gene HSPA5 to predict the progression of lung squamous cell carcinoma

Ferroptosis, an iron-dependent form of regulated cell death driven by excessive lipid peroxidation, is implicated in the development and therapeutic responses of cancer. However, the role of ferroptosis-related gene profiles in lung squamous cell carcinoma (LSCC) remains largely unknown. The present study aimed to identify the prognostic roles of ferroptosis-related genes in LSCC. Sequencing data from the Cancer Genome Atlas were analyzed and ferroptosis-related gene expression between tumor and para-tumor tissue was identified. The prognostic role of these genes was also assessed using Kaplan-Meier analyses and univariate and multivariate Cox proportional hazards regression model analyses. Immunological correlation, tumor stemness, drug sensitivity and the transcriptional differences of heat shock protein (HSP)A5 in LSCC were also analyzed. Thereafter, the expression of HSPA5 in 100 patients with metastatic LSCC was evaluated using immunohistochemistry (IHC) and the clinical significance of these markers with different risk factors was assessed. Of the 22 ferroptosis-related genes, the expression of HSPA5, HSPB1, glutathione peroxidase 4, Fanconi anemia complementation group D2, CDGSH iron sulfur domain 1, farnesyl-diphosphate farnesyltransferase 1, nuclear factor erythroid 2 like 2, solute carrier (SLC)1A5, ribosomal protein L8, nuclear receptor coactivator 4, transferrin receptor and SLC7A11 was significantly increased in LSCC compared with adjacent tissues. However, only high expression of HSPA5 was able to predict progression-free survival (PFS) and disease-free survival in LSCC. Although HSPA5 was also significantly elevated in patients with lung adenocarcinoma, HSPA5 expression did not predict the prognosis of patients with lung adenocarcinoma. Of note, a higher expression of HSPA5 was related to higher responses to chemotherapy but not to immunotherapy. In addition, HSPA5 expression was positively correlated with 'ferroptosis', 'cellular responses to hypoxia', 'tumor proliferation signature', 'G2M checkpoint', 'MYC targets' and 'TGFB'. IHC analysis also demonstrated that a high expression of HSPA5 in patients with metastatic LSCC in the study cohort was associated with shorter PFS and overall survival. In conclusion, the present study demonstrated that the expression of the ferroptosis-related gene HSPA5 may be a negative prognostic marker for LSCC.

Cuproptosis and Immune-Related Gene Signature Predicts Immunotherapy Response and Prognosis in Lung Adenocarcinoma

Cuproptosis and associated immune-related genes (IRG) have been implicated in tumorigenesis and tumor progression. However, their effects on lung adenocarcinoma (LUAD) have not been elucidated. Therefore, we investigated the impact of cuproptosis-associated IRGs on the immunotherapy response and prognosis of LUAD using a bioinformatical approach and in vitro experiments analyzing clinical samples. Using the cuproptosis-associated IRG signature, we classified LUAD into two subtypes, cluster 1 and cluster 2, and identified three key cuproptosis-associated IRGs, NRAS, TRAV38-2DV8, and SORT1. These three genes were employed to establish a risk model and nomogram, and to classify the LUAD cohort into low- and high-risk subgroups. Biofunctional annotation revealed that cluster 2, remarkably downregulating epigenetic, stemness, and proliferation pathways activity, had a higher overall survival (OS) and immunoinfiltration abundance compared to cluster 1. Real-time quantitative PCR (RT-qPCR) validated the differential expression of these three genes in both subgroups. scRNA-seq demonstrated elevated expression of NRAS and SORT1 in macrophages. Immunity and oncogenic and stromal activation pathways were dramatically enriched in the low-risk subgroup, and patients in this subgroup responded better to immunotherapy. Our data suggest that the cuproptosis-associated IRG signature can be used to effectively predict the immunotherapy response and prognosis in LUAD. Our work provides enlightenment for immunotherapy response assessment, prognosis prediction, and the development of potential prognostic biomarkers for LUAD patients.

Dissecting the alternation landscape of mitochondrial metabolism-related genes in lung adenocarcinoma and their latent mechanisms

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer with high incidence and unsatisfactory prognosis. The majority of LUAD patients eventually succumb to local and/or distinct metastatic recurrence. Genomic research of LUAD has broadened our understanding of this disease's biology and improved target therapies. However, the alternation landscape and characteristics of mitochondrial metabolism-related genes (MMRGs) in LUAD progression remain poorly understood. We performed a comprehensive analysis to identify the function and mechanism of MMRGs in LUAD based on the TCGA and GEO databases, which might offer therapeutic values for clinical researchers. Then, we figured out three hub prognosis-associated MMRGs (also termed as PMMRGs: ACOT11, ALDH2, and TXNRD1) that were engaged in the evolution of LUAD. To investigate the correlation between clinicopathological characteristics and MMRGs, we divided LUAD samples into two clusters (C1 and C2) based on key MMRGs. In addition, important pathways and the immune infiltration landscape affected by LUAD clusters were also delineated. Further, we nominated potential regulatory mechanisms underlying the MMRGs in LUAD development and progression. In conclusion, our integrative analysis enables a more comprehensive understanding of the mutation landscape of MMRGs in LUAD and provides an opportunity for more precise treatment.

Establishing a metastasis-related diagnosis and prognosis model for lung adenocarcinoma through CRISPR library and TCGA database

PURPOSE

Existing biomarkers for diagnosing and predicting metastasis of lung adenocarcinoma (LUAD) may not meet the demands of clinical practice. Risk prediction models with multiple markers may provide better prognostic factors for accurate diagnosis and prediction of metastatic LUAD.

METHODS

An animal model of LUAD metastasis was constructed using CRISPR technology, and genes related to LUAD metastasis were screened by mRNA sequencing of normal and metastatic tissues. The immune characteristics of different subtypes were analyzed, and differentially expressed genes were subjected to survival and Cox regression analyses to identify the specific genes involved in metastasis for constructing a prediction model. The biological function of RFLNA was verified by analyzing CCK-8, migration, invasion, and apoptosis in LUAD cell lines.

RESULTS

We identified 108 differentially expressed genes related to metastasis and classified LUAD samples into two subtypes according to gene expression. Subsequently, a prediction model composed of eight metastasis-related genes (RHOBTB2, KIAA1524, CENPW, DEPDC1, RFLNA, COL7A1, MMP12, and HOXB9) was constructed. The areas under the curves of the logistic regression and neural network were 0.946 and 0.856, respectively. The model effectively classified patients into low- and high-risk groups. The low-risk group had a better prognosis in both the training and test cohorts, indicating that the prediction model had good diagnostic and predictive power. Upregulation of RFLNA successfully promoted cell proliferation, migration, invasion, and attenuated apoptosis, suggesting that RFLNA plays a role in promoting LUAD development and metastasis.

CONCLUSION

The model has important diagnostic and prognostic value for metastatic LUAD and may be useful in clinical applications.

Prognostic Index for Nonsmall Cell Lung Cancer Based on Immune-Related Genes Expression

Immune system dysregulation is associated with tumor incidence and growth. Here, we established an RNA-based individualized immune signature associated with prognosis for nonsmall cell lung cancer (NSCLC) to guide adjuvant therapy. We downloaded publicly accessible data on RNA expression and clinical characteristics of NSCLC from the Cancer Genome Atlas (TCGA). From immune-related genes (IRGs) retrieved from the immunology database and analysis portal (ImmPort) database, we then screened differentially expressed immune-related genes (DEIRGs). Using overall survival (OS) as a clinical endpoint, we identified 26 prognostic DEIRGs via univariate and multivariate Cox regression analysis, and then developed a risk model based on these 26 IRGs with an area under the curve (AUC) of 0.701, and its predictive ability independent from other clinical factors. We also downloaded tumor immune infiltrate data and analyzed the correlations between lymphocytic infiltration with our risk scores, but found no significant association. Furthermore, we retrieved 86 differentially expressed transcription factors (TFs) to assess their regulatory relationships with the 26 prognostic DEIRGs. In summary, we developed a robust risk model to predict survival in patients with NSCLC, based on the expression of 26 IRGs. It provides novel predictive and therapeutic molecular targets.

A New Risk Score Based on Eight Hepatocellular Carcinoma- Immune Gene Expression Can Predict the Prognosis of the Patients

Background

Hepatocellular carcinoma (HCC) is one of the malignant tumors with high morbidity and mortality worldwide. Immunotherapy has emerged as an increasingly important cancer treatment modality. However, the potential relationship between immune genes and HCC still needs to be explored. The purpose of this study is to construct a new prognostic risk signature to predict the prognosis of HCC patients based on the expression of immune-related genes (IRGs) and explore its potential mechanism.

Methods

We analyzed the gene expression data of 332 HCC patient samples and 46 adjacent normal tissues samples (Solid Tissue Normal including cirrhotic tissue) in The Cancer Genome Atlas (TCGA) database and clinical characteristics. We analyzed the gene expression data, identified differentially expressed IRGs in HCC tissues, filtered IRGs with prognostic value to construct an IRG signature, and classified patients into high and low gene expression groups based on the expression of IRGs in their tumor tissues. We also investigated the potential molecular mechanisms of IRGs through a bioinformatics approach using Protein-Protein Interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis and Gene Ontology (GO) database analysis. Differentially expressed IRGs associated with significant clinical outcomes (SIRGs) were identified by univariate Cox regression analysis. An immune-related risk score model (IRRSM) was established based on Lasso Cox regression analysis and multivariate Cox regression analysis. Based on the IRRSM, the immune score of the patients was calculated, and the patients were divided into high-risk and low-risk patients according to the median score, and the differences in survival between the two groups were compared. Then, the correlation analysis between the IRRSM and clinical characteristics was performed, and the IRRSM was validated using the International Cancer Genome Consortium (ICGC) database.

Results

The IRRSM was eventually constructed and confirmed to be an independent prognostic model for HCC patients. The IRRSM was shown to be positively correlated with the infiltration of four types of immune cells.

Conclusion

Our results showed that some SIRGs have potential value for predicting the prognosis and clinical outcomes of HCC patients. IRGs affect the prognosis of HCC patients by regulating the tumor immune microenvironment (TIME). This study provides a new insight for immune research and treatment strategies in HCC patients.

Construction of a ceRNA Network and Analysis of Tumor Immune Infiltration in Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is characterized by high malignancy, frequent metastasis, and recurrence with an unfavorable prognosis. This study is aimed at constructing a prognostic model for tumor-infiltrating immune cells and a competing endogenous RNA (ceRNA) network in PAAD and analyzing susceptibilities of chemotherapy and immunotherapy of PAAD. Gene expression profiles and clinical information of PAAD were downloaded from The Cancer Genome Atlas (TCGA) database and divided into the tumor group and the normal group. A total of five PAAD survival-related key genes in the ceRNA network and three survival-related immune infiltrating cells were uncovered, and two survival risk models and nomograms were constructed. The efficiency and performance of the two models were verified using multi-index area under the curve analysis at different time points, decision curve analysis, and calibration curves. Co-expression analysis showed that LRRC1, MIR600HG, and RNF166 in the ceRNA network and tumor-infiltrating immune cells including CD8 T cells and M1 macrophages were likely related to the PAAD prognosis, and the expression of key ceRNA-related genes was experimently validated in tissues and cell lines by RT-qPCR. Patients with low risk scores for key genes in the ceRNA network displayed a positive response to anti-programmed death-1 (PD-1) treatment and greater sensitivity to chemotherapeutic drugs such as docetaxel, lapatinib, and paclitaxel. More importantly, our results suggested that the IC50 values of gemcitabine in PAAD were not significantly different between the high and low risk groups. The expression levels of immune checkpoints were significantly different in the high-risk and low-risk groups. The prognostic model, nomogram, and drug analysis may provide an essential reference for PAAD patient management in the clinic.

Tumor and Peripheral Immune Status in Soft Tissue Sarcoma: Implications for Immunotherapy

Simple Summary

Soft Tissue Sarcomas are a rare and heterogeneous group of tumors, which have a characteristic complexity, leading to a difficult diagnosis and a lack of response to treatment. The aim of this review is to summarize the role of immune cells, soluble plasmatic factors, immune checkpoints; and the expression of immune-related genes predicting survival, response to therapy, and potential immunotherapeutic agents or targets in Soft Tissue Sarcomas.

Abstract

Soft Tissue Sarcomas (STS) are a heterogeneous and rare group of tumors. Immune cells, soluble factors, and immune checkpoints are key elements of the complex tumor microenvironment. Monitoring these elements could be used to predict the outcome of the disease, the response to therapy, and lead to the development of new immunotherapeutic approaches. Tumor-infiltrating B cells, Natural Killer (NK) cells, tumor-associated neutrophils (TANs), and dendritic cells (DCs) were associated with a better outcome. On the contrary, tumor-associated macrophages (TAMs) were correlated with a poor outcome. The evaluation of peripheral blood immunological status in STS could also be important and is still underexplored. The increased lymphocyte-to-monocyte ratio (LMR) and neutrophil-to-lymphocyte ratio (NLR), higher levels of monocytic myeloid-derived suppressor cells (M-MDSCs), and Tim-3 positive CD8 T cells appear to be negative prognostic markers. Meanwhile, NKG2D-positive CD8 T cells were correlated with a better outcome. Some soluble factors, such as cytokines, chemokines, growth factors, and immune checkpoints were associated with the prognosis. Similarly, the expression of immune-related genes in STS was also reviewed. Despite these efforts, only very little is known, and much research is still needed to clarify the role of the immune system in STS.

Ferroptosis-Related Gene Signature and Patterns of Immune Infiltration Predict the Overall Survival in Patients With Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is a malignant tumor with high heterogeneity and poor prognosis. Ferroptosis, a form of regulated cell-death–related iron, has been proven to trigger inflammation-associated immunosuppression in the tumor microenvironment, which promotes tumor growth. Therefore, the clinical prognostic value of ferroptosis-related genes in LUAD needs to be further explored.

Method: In this study, we downloaded the mRNA expression profiles and corresponding clinical data of LUAD patients from the Cancer Genome Atlas database. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct ferroptosis-related gene signature. Based on these, we established the nomograms for prognosis prediction and validated the model in the GSE72094 dataset. The cell type was identified using the CIBERSORT algorithm for estimating relative subsets of RNA transcripts, which was then used to screen significant tumor immune-infiltrating cells associated with the LUAD prognosis prediction model. Subsequently, we applied co-expression analysis to reveal the relationship between ferroptosis-related genes and significant immune cells.

Results: The univariate COX regression analysis showed that 20 genes were associated with the overall survival (OS) as prognostic differentially expressed genes (DEGs) (FDR <0.05). Patients were divided into two risk groups using a 13-gene signature, with the high-risk group having a significantly worse OS than their low-risk counterparts (p < 0.001). We used receiver operating characteristic (ROC) curve analysis to confirm the predictive capacity of the signature. Besides, we identified seven pairs of ferroptosis-related genes and tumor-infiltrating immune cells associated with the prognosis of LUAD patients.

Conclusion: In this study, we construct a ferroptosis-related gene signature that can be used for prognostic prediction in LUAD. In addition, we reveal a potential connection between ferroptosis and tumor-infiltrating immune cells.

An immune-related model based on INHBA, JAG2 and CCL19 to predict the prognoses of colon cancer patients

Background

Colorectal cancer (CRC) is the leading cause of cancer deaths and most common malignant tumors worldwide. Immune-related genes (IRGs) can predict prognoses of patients and the effects of immunotherapy. A series of colon cancer (CCa) samples from The Cancer Genome Atlas (TCGA) were analyzed to provide a new perspective into this field.

Methods

Differential IRGs and IRGs with significant clinical outcomes (sIRGs) were calculated by the limma algorithm and univariate COX regression analysis. The potential molecular mechanisms of IRGs were detected by PPI, KEGG and GO analysis. Immune-related risk score model (IRRSM) was established based on multivariate COX regression analysis. Based on the median risk score of IRRSM, the high-risk group and low-risk group were distinguished. The expression levels of IHNBA and JAG2 and relationships between IHNBA and clinical features were verified by RT-qPCR.

Results

6 differential sIRGs of patients with CCa were selected by univariate COX regression analysis. Based on the sIRGs (INHBA, JAG2 and CCL19), the IRRSM was established to predict survival probability of CCa patients and to explore the potential correlations with clinical features. Furthermore, IRRSM reflected the infiltration status of 22 types of immune cells. The expression levels of IHNBA and JAG2 were higher in CCa tissues than that in adjacent normal tissues. The expression levels of IHNBA and JAG2 were increased in advanced T stages.

Conclusion

Our results illustrated that some sIRGs showed the latent value of predicting the prognoses of CCa patients and the clinical features. This study could provide a new insight for immune research and treatment strategies in CCa patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02000-z.

Multi-omics analysis of genomics, epigenomics and transcriptomics for molecular subtypes and core genes for lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most frequently diagnosed histological subtype of lung cancer. Our purpose was to explore molecular subtypes and core genes for LUAD using multi-omics analysis.

Methods

Methylation, transcriptome, copy number variation (CNV), mutations and clinical feature information concerning LUAD were retrieved from The Cancer Genome Atlas Database (TCGA). Molecular subtypes were conducted via the “iClusterPlus” package in R, followed by Kaplan-Meier survival analysis. Correlation between iCluster subtypes and immune cells was analyzed. Core genes were screened out by integration of methylation, CNV and gene expression, which were externally validated by independent datasets.

Results

Two iCluster subtypes were conducted for LUAD. Patients in imprinting centre 1 (iC1) subtype had a poorer prognosis than those in iC2 subtype. Furthermore, iC2 subtype had a higher level of B cell infiltration than iC1 subtype. Two core genes including CNTN4 and RFTN1 were screened out, both of which had higher expression levels in iC2 subtype than iC1 subtype. There were distinct differences in CNV and methylation of them between two subtypes. After validation, low expression of CNTN4 and RFTN1 predicted poorer clinical outcomes for LUAD patients.

Conclusion

Our findings comprehensively analyzed genomics, epigenomics, and transcriptomics of LUAD, offering novel underlying molecular mechanisms for LUAD. Two multi-omics-based core genes (CNTN4 and RFTN1) could become potential therapeutic targets for LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07888-4.

Identification of an Individualized Immune-Related Prognostic Risk Score in Lung Squamous Cell Cancer

Background: Lung squamous cell carcinoma (LUSC) is one of the most common histological subtypes of non-small cell lung cancer (NSCLC), and its morbidity and mortality are steadily increasing. The purpose of this study was to study the relationship between the immune-related gene (IRGs) profile and the outcome of LUSC in patients by analyzing datasets from The Cancer Genome Atlas (TCGA). Methods: We obtained publicly available LUSC RNA expression data and clinical survival data from The Cancer Genome Atlas (TCGA), and filtered IRGs based on The ImmPort database. Then, we identified risk immune-related genes (r-IRGs) for model construction using Cox regression analysis and defined the risk score in this model as the immune gene risk index (IRI). Multivariate analysis was used to verify the independent prognostic value of IRI and its association with other clinicopathological features. Pearson correlation analysis was used to explore the molecular mechanism affecting the expression of IRGs and the correlation between IRI and immune cell infiltration. Results: We screened 15 r-IRGs for constructing the risk model. The median value of IRI stratified the patients and there were significant survival differences between the two groups (p = 4.271E-06). IRI was confirmed to be an independent prognostic factor (p < 0.001) and had a close correlation with the patients' age (p < 0.05). Interestingly, the infiltration of neutrophils or dendritic cells was strongly upregulated in the high-IRI groups (p < 0.05). Furthermore, by investigating differential transcription factors (TFs) and functional enrichment analysis, we explored potential mechanisms that may affect IRGs expression in tumor cells. Conclusion: In short, this study used 15 IRGs to build an effective risk prediction model, and demonstrated the significance of IRGs-based personalized immune scores in LUSC prognosis.

Development and validation of an immune gene-set based prognostic signature for soft tissue sarcoma

Background

Sarcomas is a group of heterogeneous malignant tumors originated from mesenchymal tissue and different types of sarcomas have disparate outcomes. The present study aims to identify the prognostic value of immune-related genes (IRGs) in sarcoma and establish a prognostic signature based on IRGs.

Methods

We collected the expression profile and clinical information of 255 soft tissue sarcoma samples from The Cancer Genome Atlas (TCGA) database and 2498 IRGs from the ImmPort database. The LASSO algorithm and Cox regression analysis were used to identify the best candidate genes and construct a signature. The prognostic ability of the signature was evaluated by ROC curves and Kaplan-Meier survival curves and validated in an independent cohort. Besides, a nomogram based on the IRGs and independent prognostic clinical variables was developed.

Results

A total of 19 IRGs were incorporated into the signature. In the training cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.938, 0.937 and 0.935, respectively. The Kaplan-Meier survival curve indicated that high-risk patients were significantly worse prognosis (P < 0.001). In the validation cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.730, 0.717 and 0.647, respectively. The Kaplan-Meier survival curve also showed significant distinct survival outcome between two risk groups. Furthermore, a nomogram based on the signature and four prognostic variables showed great accuracy in whole sarcoma patients and subgroup analyses. More importantly, the results of the TF regulatory network and immune infiltration analysis revealed the potential molecular mechanism of IRGs.

Conclusions

In general, we identified and validated an IRG-based signature, which can be used as an independent prognostic signature in evaluating the prognosis of sarcoma patients and provide potential novel immunotherapy targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07852-2.

Identification of Hypoxia Signature to Assess the Tumor Immune Microenvironment and Predict Prognosis in Patients with Ovarian Cancer

BACKGROUND

The 5-year overall survival rate of ovarian cancer (OC) patients is less than 40%. Hypoxia promotes the proliferation of OC cells and leads to the decline of cell immunity. It is crucial to find potential predictors or risk model related to OC prognosis. This study aimed at establishing the hypoxia-associated gene signature to assess tumor immune microenvironment and predicting the prognosis of OC.

METHODS

The gene expression data of 378 OC patients and 370 OC patients were downloaded from datasets. The hypoxia risk model was constructed to reflect the immune microenvironment in OC and predict prognosis.

RESULTS

8 genes (AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI) were included in the hypoxic gene signature. Patients in the high hypoxia risk group showed worse survival. Hypoxia signature significantly related to clinical features and may serve as an independent prognostic factor for OC patients. 2 types of immune cells, plasmacytoid dendritic cell and regulatory T cell, showed a significant infiltration in the tissues of the high hypoxia risk group patients. Most of the immunosuppressive genes (such as ARG1, CD160, CD244, CXCL12, DNMT1, and HAVCR1) and immune checkpoints (such as CD80, CTLA4, and CD274) were upregulated in the high hypoxia risk group. Gene sets related to the high hypoxia risk group were associated with signaling pathways of cell cycle, MAPK, mTOR, PI3K-Akt, VEGF, and AMPK.

CONCLUSION

The hypoxia risk model could serve as an independent prognostic indicator and reflect overall immune response intensity in the OC microenvironment.

The Combined Detection of Immune Genes for Predicting the Prognosis of Patients With Non-Small Cell Lung Cancer

Lung cancer is one of the leading causes of cancer-related death. In recent years, there has been an increasing interest in the fields of tumor and immunity. This study focused on the possible prognostic value of immune genes in non-small cell lung cancer patients. We used The Cancer Genome Atlas (TCGA) to download gene expression data and clinical information of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). The immune gene list was downloaded from the Immport database. We then constructed immune gene prognostic models on the basis of Cox regression analysis. We further evaluated the clinical significance of the models via survival analysis, receiver operating characteristic (ROC) curves, and independent prognostic factor analysis. Moreover, we analyzed the associations of prognostic models with both mutation burdens and neoantigens. Using the Gene Expression Omnibus (GEO) and Kaplan-Meier plotter databases, we evaluated the validity of the prognostic models. The prognostic model of LUAD included 13 immune genes, and the prognostic model of LUSC contained 10 immune genes. High-risk patients based on prognostic models had a lower 5-year survival rate than did low-risk patients. The ROC curve analysis demonstrated the prediction accuracy of the prognostic models, as the area under the curve (AUC) was 0.742, 0.707, and 0.711 for LUAD, and 0.668, 0.703, and 0.668 for LUSC, when the predicted survival times were 1, 3, and 5 years, respectively. The mutation burden analysis showed that mutation level was associated with the risk score in patients with LUAD. The analysis based on GEO and Kaplan-Meier plotter demonstrated the prognostic validity of the models. Therefore, immune gene-related models of LUAD and LUSC can predict prognosis. Further study of these genes may enable us to better distinguish between LUAD and LUSC and lead to improvement in immunotherapy for lung cancer.

Prognostic landscape of tumor-infiltrating immune cells and immune-related genes in the tumor microenvironment of gastric cancer

The tumor microenvironment is closely related to the progression and immune escape of tumor cells. Tumor-infiltrating immune cells (TIICs) and immune-related genes (IRGs) are indispensable components of the tumor microenvironment and have been demonstrated to be highly valuable in determining the prognosis of multiple cancers. To elucidate the prognostic value of TIICs and IRGs in gastric cancer, we conducted a comprehensive analysis focusing on the abundances of 22 types of TIICs and differentially expressed IRGs based on a dataset from The Cancer Genome Atlas (TCGA). The results showed that great composition differences in TIICs and immune cell subfractions were associated with survival outcomes in different stages. Additionally, 29 hub genes were characterized from 345 differentially expressed IRGs and found to be significantly associated with survival outcomes. Then, an independent prognostic indicator based on ten IRGs was successfully constructed after multivariate adjustment for some clinical parameters. Further validation revealed that these hub IRGs could reflect the infiltration levels of immune cells. Thus, our results confirmed the clinical significance of TIICs and IRGs in gastric cancer and may establish a foundation for further exploring immune cell and gene targets for personalized treatment.

Development of a Prognostic Signature Based on Autophagy-related Genes for Head and Neck Squamous Cell Carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor with relative low survival rate. Increasingly evidences have emphasized the importance of autophagy in cancer initiation, progression, and the responses to cancer treatment.

AIM OF THE STUDY

This study aimed to investigate the potential biological and prognostic significance of autophagy-related genes (ARGs) in HNSCC patients.

METHODS

We collected a list of ARGs from Human Autophagy Database and obtained expression profiles and clinical information of HNSCC samples from the Cancer Genome Atlas (TCGA) portal. Differential expression analysis and functional enrichment analysis were performed by R software. The prognostic value of differentially expressed ARGs was detected by Cox regression analysis and prognosis-related ARGs were subjected to LASSO regression analysis. Univariate and multivariate Cox regression analysis were applied to identify promising independent prognosticators for HNSCC.

RESULTS

A total of 35 differentially expressed ARGs were screened out and functional enrichment analysis results indicated these genes were mainly associated with autophagy-related biological processes and pathways. Seven prognosis-related ARGs (ITGA3, CDKN2A, FADD, NKX2-3, BAK1, CXCR4, and HSPB8) were selected to construct a risk signature, which proved to be effective in predicting the survival rate of HNSCC patients. Moreover, univariate analysis showed risk score, tumor stage, T stage, and N stage were negatively correlated with patient overall survival and the multivariate Cox regression analysis results indicated risk score, age, and N stage was significantly associated with patient prognosis.

CONCLUSIONS

Our findings may provide novel evidences for the diagnosis and prognosis evaluation for HNSCC.

Identification and validation of the prognostic value of immune-related genes in non-small cell lung cancer

Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The aim of this research study was to identify differentially expressed immune-related genes (DEIRGs) and establish a Cox prediction model for the evaluation of prognosis in patients with non-small cell lung cancer (NSCLC). Transcription expression data, immune gene data, and tumor transcription factor data from The Cancer Genome Atlas (TCGA), the Immunology Database and Analysis Portal, and the Cistrome Cancer database were analyzed to detect differentially expressed genes (DEGs), DEIRGs, and differentially expressed transcription factors (DETFs). Multivariate Cox regression analysis was used to obtain potential DEIRGs as independent prognostic factors. Oncomine, The Human Protein Atlas (HPA), TIMER databases were performed to validate the mRNA and protein expression level of DEIRGs. TIMER database was performed to explore the immunocytes infiltration of DEIRGs. In total, 7448 DEGs, 536 DEIRGs, 87 DETFs were identified from 1,037 NSCLC tissues and 108 normal tissues in TCGA database. Fifteen-DEIRG signatures (THBS1, S100P, S100A16, DLL4, CD70, DKK1, IL33, NRTN, PDGFB, STC2, VGF, GCGR, HTR3A, LGR4, SHC3) could be perceived as independent prognostic factors for predicting the overall survival of patients with NSCLC (P = 4.89e--09). Immune cell correlation analysis showed that neutrophils and b cells were positively and negatively correlated with the riskscore of the prediction model, respectively. Our study identified a Cox prediction model based on DEIRGs to predict the overall survival of patients with NSCLC. The immunocyte infiltration analysis provided a novel horizon for monitoring the status of the NSCLC immune microenvironment.

Comprehensive profiling of immune-related genes in soft tissue sarcoma patients

Background

Immune-related genes (IRGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of IRGs and their clinical significance in soft tissue sarcoma (STS) patients is lacking.

Methods

Gene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed immune-related genes (DEIRGs) were determined by matching the DEG and ImmPort gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEIRGs was conducted, and associations with prognosis, the tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for progression free survival (PFS), were established and validated in an independent set. Finally, two transcription factor (TF)-IRG regulatory networks were constructed, and a crucial regulatory axis was validated.

Results

In total, 364 DEIRGs and four clusters were identified. OS, TME scores, five immune checkpoints, and 12 types of immune cells were found to be significantly different among the four clusters. The two prognostic signatures incorporating 20 DEIRGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.879 (95%CI 0.832 ~ 0.926) and 0.825 (95%CI 0.776 ~ 0.874) for the OS and PFS signatures, respectively. Finally, TF-IRG regulatory networks were established, and the MYH11-ADM regulatory axis was verified in three independent datasets.

Conclusion

This comprehensive analysis of the IRG landscape in soft tissue sarcoma revealed novel IRGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

Identification of a novel immune prognostic model in gastric cancer

PURPOSE

The tumor immune microenvironment (TIME) is now considered as an important factor during gastric cancer (GC) development. This study identified a novel immune-related risk model for predicting prognosis and assessing the immune status of GC patients.

METHODS

Transcriptomic data were obtained from the TCGA database. The differential expressed immune-related genes (IRGs) were identified through the ImmPort portal. Enrichment analysis was performed to explore the potential molecular mechanism of these IRGs. By the Cox regression analysis, we constructed the immune prognostic model. Then, the association between the model and the immune microenvironment was estimated. The model was validated in the GSE84433 dataset.

RESULTS

Totally, we identified 222 differentially expressed IRGs. These IRGs were closely correlated with immune response and immune signaling pathways. Through the Cox regression analysis, we developed the immune prognostic model based on the expression of seven IRGs (CXCL3, NOX4, PROC, FAM19A4, RNASE2, IGHD2-15, CGB5). Patients were stratified into two groups according to immune-related risk scores. Survival analysis indicated that the prognosis of high-risk patients was poorer than low-risk patients. Moreover, the immune-related risk score was an independent prognostic biomarker. More importantly, we found that the infiltration level of immunosuppressive cells and the expression of inhibitory immune checkpoints were higher in high-risk patients. The immune microenvironment tended to be a suppressive status in patients with high-risk scores.

CONCLUSION

This study demonstrated that our model had predictive value for prognosis and the TIME in GC. It might be a robust tool to improve personalized patient management.

Identification of the Prognostic Value of Immune-Related Genes in Esophageal Cancer

Esophageal cancer (EC) is a serious malignant tumor, both in terms of mortality and prognosis, and immune-related genes (IRGs) are key contributors to its development. In recent years, immunotherapy for tumors has been widely studied, but a practical prognostic model based on immune-related genes (IRGs) in EC has not been established and reported. This study aimed to develop an immunogenomic risk score for predicting survival outcomes among EC patients. In this study, we downloaded the transcriptome profiling data and matched clinical data of EC patients from The Cancer Genome Atlas (TCGA) database and found 4,094 differentially expressed genes (DEGs) between EC and normal esophageal tissue (p < 0.05 and fold change >2). Then, the intersection of DEGs and the immune genes in the “ImmPort” database resulted in 303 differentially expressed immune-related genes (DEIRGs). Next, through univariate Cox regression analysis of DEIRGs, we obtained 17 immune genes related to prognosis. We detected nine optimal survival-associated IRGs (HSPA6, CACYBP, DKK1, EGF, FGF19, GAST, OSM, ANGPTL3, NR2F2) by using Lasso regression and multivariate Cox regression analyses. Finally, we used those survival-associated IRGs to construct a risk model to predict the prognosis of EC patients. This model could accurately predict overall survival in EC and could be used as a classifier for the evaluation of low-risk and high-risk groups. In conclusion, we identified a practical and robust nine-gene prognostic model based on immune gene dataset. These genes may provide valuable biomarkers and prognostic predictors for EC patients and could be further studied to help understand the mechanism of EC occurrence and development.

Analysis of Lung Adenocarcinoma Subtypes Based on Immune Signatures Identifies Clinical Implications for Cancer Therapy

Lung cancer is the most common cause of cancer deaths worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. However, the prognostic and predictive outcomes differ because of this cancer type heterogeneity. LUAD subtypes were identified on the basis of the immunogenomic profiling of 29 immune signatures. We named three LUAD subtypes: Immunity High, Immunity Medium, and Immunity Low. The Immunity High subtype was characterized by immune activation, e.g., increased immune scores, elevated stromal scores and the highest infiltration of CD8⁺ T cells, and decreased tumor purities. Activated expressions of human leukocyte antigen (HLA) genes, immune checkpoint molecules, and T helper 1 (Th1)/interferon-gamma (IFNγ) gene signature were also observed in the Immunity High subtype. N ⁶-methyladenosine (m⁶A) RNA methylation, associated with cancer initiation and progression, was reduced in the Immunity High subtype. Functional and signaling pathway enrichment analysis further showed that differentially expressed genes between the Immunity High subtype and the other subtypes mainly participated in immune response and some cancer-associated pathways. In addition, the Immunity High subtype exhibited more sensitivity to immunotherapy and chemotherapy. Finally, candidate compounds that aimed at LUAD subtype differentiation were identified. Comprehensively characterizing the LUAD subtypes based on immune signatures may help to provide potential strategies for LUAD treatment.