Development and Validation of a TNF Family-Based Signature for Predicting Prognosis, Tumor Immune Characteristics, and Immunotherapy Response in Colorectal Cancer Patients

Transcriptionally activates CCL28 expression to inhibit M2 polarization of macrophages and prevent immune escape in colorectal cancer cells

OBJECTIVE

This study aimed to investigate the potential molecular mechanism of SPDEF in immune evasion of colorectal cancer (CRC) and examine its impact on macrophage M2 polarization using the TCGA and GEO databases.

METHODS

By combining TCGA and GEO databases, differential gene expression between CRC samples and standard tissue samples was analyzed to screen for immune-related genes (IRGs) associated with the prognosis of CRC patients. A predictive risk model was constructed based on 18 key IRGs, which were then validated using the GEO dataset. The relationship between transcription factors and IRGs was further explored to investigate their regulatory network in CRC. In vivo and in vitro experiments were carried out to validate these regulatory relationships and explore the function of SPDEF and CCL28 in CRC.

RESULTS

Twelve key IRGs associated with clinical and pathological characteristics of CRC patients were identified. Among them, CCL28 significantly impacted macrophage infiltration in CRC cells and may be a critical factor in immune evasion. In both in vitro and in vivo experiments, overexpression of SPDEF upregulated CCL28 expression, thereby suppressing M2 polarization of macrophages and inhibiting CRC cell proliferation and tumor growth. Notably, interference with CCL28 could reverse the effect of SPDEF overexpression.

CONCLUSION

SPDEF can suppress immune evasion of CRC cells by activating CCL28, which is achieved through the modulation of M2 polarization of macrophages. This provides a new research direction and potential therapeutic target for immunotherapy in CRC.

PRKCSH contributes to TNFSF resistance by extending IGF1R half-life and activation in lung cancer

Tumor necrosis factor superfamily (TNFSF) resistance contributes to the development and progression of tumors and resistance to various cancer therapies. Tumor-intrinsic alterations involved in the adaptation to the TNFSF response remain largely unknown. Here, we demonstrate that protein kinase C substrate 80K-H (PRKCSH) abundance in lung cancers boosts oncogenic IGF1R activation, leading to TNFSF resistance. PRKCSH abundance is correlated with IGF1R upregulation in lung cancer tissues. Specifically, PRKCSH interacts with IGF1R and extends its half-life. The PRKCSH-IGF1R axis in tumor cells impairs caspase-8 activation, increases Mcl-1 expression, and inhibits caspase-9, leading to an imbalance between cell death and survival. PRKCSH deficiency augmented the antitumor effects of natural killer (NK) cells, representative TNFSF effector cells, in a tumor xenograft IL-2Rg-deficient NOD/SCID (NIG) mouse model. Our data suggest that PRKCSH plays a critical role in TNFSF resistance and may be a potential target to improve the efficacy of NK cell-based cancer therapy.

Co-expression pattern of SLC transporter genes associated with the immune landscape and clinical outcomes in gastric cancer

Solute carrier (SLC) transporters play a dual role in the occurrence and progression of tumours by acting as both suppressors and promoters. However, the overall impact of SLC transcriptome signatures on the tumour microenvironment, biological behaviour and clinical stratification of gastric cancer has not been thoroughly investigated. Therefore, we comprehensively analysed the expression profiles of the SLC transporter family members to identify novel molecular subtypes in gastric cancer. We identified two distinct SLC subtypes, SLC-S1 and SLC-S2, using non-negative matrix factorization. These subtypes were markedly linked with the tumour microenvironment landscape, biological pathway activation and distinct clinical features of gastric cancer. Furthermore, a new scoring model, the SLC score, was developed to quantify the SLC subtypes. High SLC scores indicated a pattern of 'SLC-S2', characterized by stromal infiltration and activation, poor prognosis and insensitivity to chemotherapy and immunotherapy, but high sensitivity to imatinib. The SLC score could serve as a supplement to the Tumour Node Metastasis (TNM) staging system to guide personalized treatment strategies and predict prognosis for patients with gastric cancer.

Tumour Necrosis Factor Alpha (TNF-α) and Oral Squamous Cell Carcinoma

Uncovering the inflammatory mechanisms underpinning initiation, progression, and promotion of oral squamous cell carcinoma (OSCC) development is fundamental to the rational pursuit of targeted therapeutics. Here we present a review of the current knowledge of the role of TNF-α in the aetiology, pathogenesis, and potential therapies with regards to OSCC. TNF-α is worthy of particular attention in OSCC, with its presence demonstrated to enhance cell proliferation and its downregulation demonstrated to inhibit proliferation and migration in other carcinomas in both in vitro and in vivo models and oral cancer patients. Increased TNF-α in the OSCC tumour microenvironment has been demonstrated to favour invasion through promotion of firstly the pro-inflammatory, pro-invasive phenotypes of OSCC cells and secondly its paracrine mechanism mediating recruitment and activation of inflammatory cells. Polymorphisms affecting the gene expression of TNF-α have been strongly associated with an increased risk for oral squamous cell carcinoma. A number of studies have considered TNF-α within biofluids, including saliva and serum, as a potential biomarker for the early detection of OSCC, as well as its staging, differentiation, and prognosis. The broad and multifaceted role that TNF-α plays in many inflammatory states presents an obvious confounder, particularly with demonstrated increased TNF-α levels in common oral disease states. Lastly, biologic agents targeting TNF-α are currently in clinical use for immune-mediated inflammatory rheumatological and gastrointestinal diseases. There is the potential that these biological agents might have an adjunctive role in OSCC prevention and treatment.

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

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

The prognostic significance of tumor-associated neutrophils and circulating neutrophils in glioblastoma (WHO CNS5 classification)

BACKGROUND

Tumor-associated neutrophils (TANs) in the tumor microenvironment are prognostic biomarkers in many malignancies. However, it is unclear whether TANs can serve as a prognostic marker for clinical outcomes in patients with glioblastoma (GBM), as classified according to World Health Organization Classification of Tumors of the Central Nervous System, fifth edition (CNS5). In the present study, we analyzed correlations of TANs and peripheral blood neutrophils prior to radiotherapy with overall survival (OS) in GBM (CNS5).

METHODS

RNA-seq expression profiles of patients with newly diagnosed GBM (CNS5) were extracted from The Cancer Genome Atlas (TCGA), and The Chinese Glioma Genome Atlas (CGGA). TAN infiltration was inferred using CIBERSORTx algorithm. Neutrophil counts prior to radiotherapy in newly diagnosed GBM (CNS5) were obtained from the First Affiliated Hospital of Fujian Medical University. The prognostic value of TANs and peripheral blood neutrophils before radiotherapy was investigated using Kaplan-Meier analysis and Cox proportional hazards models. The robustness of these findings was evaluated by sensitivity analysis, and E values were calculated.

RESULTS

A total of 146 and 173 individuals with GBM (CNS5) were identified from the TCGA and CGGA cohorts, respectively. High infiltration of TANs was of prognostic of poor OS in TCGA (HR = 1.621, 95% CI: 1.004-2.619) and CGGA (HR = 1.546, 95% CI: 1.029-2.323). Levels of peripheral blood neutrophils before radiotherapy (HR = 2.073, 95% CI: 1.077-3.990) were independently associated with poor prognosis. Sensitivity analysis determined that the E-value of high TANs infiltration was 2.140 and 2.465 in the TCGA and CGGA cohorts.

CONCLUSIONS

TANs and peripheral blood neutrophil levels before radiotherapy are prognostic of poor outcomes in GBM (CNS5).

Identification and validation of necroptosis-related prognostic gene signature and tumor immune microenvironment infiltration characterization in esophageal carcinoma

Background

Esophageal carcinoma (ESCA) is a common malignancy with a poor prognosis. Previous research has suggested that necroptosis is involved in anti-tumor immunity and promotes oncogenesis and cancer metastasis, which in turn affects tumor prognosis. However, the role of necroptosis in ESCA is unclear. This study aimed to investigate the relationships between necroptosis-related genes (NRGs) and ESCA.

Methods and results

The clinical data and gene expression profiles of ESCA patients were extracted from The Cancer Genome Atlas (TCGA), and 159 NRGs were screened from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We then identified 52 differentially expressed NRGs associated with ESCA and used them for further analysis. Gene ontology (GO) and KEGG functional enrichment analyses showed that these NRGs were mostly associated with the regulation of necroptosis, Influenza A, apoptosis, NOD-like receptor, and NF-Kappa B signaling pathway. Next, univariate and multivariate Cox regression and LASSO analysis were used to identify the correlation between NRGs and the prognosis of ESCA. We constructed a prognostic model to predict the prognosis of ESCA based on SLC25A5, PPIA, and TNFRSF10B; the model classified patients into high- and low-risk subgroups based on the patient’s risk score. Furthermore, the receiver operating characteristic (ROC) curve was plotted, and the model was affirmed to perform moderately well for prognostic predictions. In addition, Gene Expression Omnibus (GEO) datasets were selected to validate the applicability and prognostic value of our predictive model. Based on different clinical variables, we compared the risk scores between the subgroups of different clinical features. We also analyzed the predictive value of this model for drug sensitivity. Moreover, Immunohistochemical (IHC) validation experiments explored that these three NRGs were expressed significantly higher in ESCA tissues than in adjacent non-tumor tissues. In addition, a significant correlation was observed between the three NRGs and immune-cell infiltration and immune checkpoints in ESCA.

Conclusions

In summary, we successfully constructed and validated a novel necroptosis-related signature containing three genes (SLC25A5, PPIA, and TNFRSF10B) for predicting prognosis in patients with ESCA; these three genes might also play a crucial role in the progression and immune microenvironment of ESCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02423-6.

Comprehensive Molecular Analyses of a TNF Family-Based Gene Signature as a Potentially Novel Prognostic Biomarker for Cervical Cancer

Background

Increasing evidence suggests that tumour necrosis factor (TNF) family genes play important roles in cervical cancer (CC). However, whether TNF family genes can be used as prognostic biomarkers of CC and the molecular mechanisms of TNF family genes remain unclear.

Methods

A total of 306 CC and 13 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. We identified differentially expressed TNF family genes between CC and normal samples and subjected them to univariate Cox regression analysis for selecting prognostic TNF family genes. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were performed to screen genes to establish a TNF family gene signature. Gene set enrichment analysis (GSEA) was performed to investigate the biological functions of the TNF family gene signature. Finally, methylation and copy number variation data of CC were used to analyse the potential molecular mechanisms of TNF family genes.

Results

A total of 26 differentially expressed TNF family genes were identified between the CC and normal samples. Next, a TNF family gene signature, including CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 was constructed based on univariate Cox, LASSO, and multivariate Cox regression analyses. The TNF family gene signature was related to age, pathological stages M and N, and could predict patient survival independently of clinical factors. Moreover, KEGG enrichment analysis suggested that the TNF family gene signature was mainly involved in the TGF-β signaling pathway, and the TNF family gene signature could affect the immunotherapy response. Finally, we confirmed that the mRNA expressions of CD27, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 were upregulated in CC, while that of EDA was downregulated. The mRNA expressions of CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 might be influenced by gene methylation and copy number variation.

Conclusion

Our study is the first to demonstrate that CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 might be used as prognostic biomarkers of CC and are associated with the immunotherapy response of CC.

A Prognostic Model Using Immune-Related Genes for Colorectal Cancer

There is evidence suggesting that immune genes play pivotal roles in the development and progression of colorectal cancer (CRC). Colorectal carcinoma patient data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were randomly classified into a training set, a test set, and an external validation set. Differentially expressed gene (DEG) analyses, univariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) were used to identify survival-associated immune genes and develop a prognosis model. Receiver operating characteristic (ROC) analysis and principal component analysis (PCA) were used to evaluate the discrimination of the risk models. The model genes predicted were verified using the Human Protein Atlas (HPA) databases, colorectal cell lines, and fresh CRC and adjacent tissues. To understand the relationship between IRGs and immune invasion and the TME, we analyzed the content of immune cells and scored the TME using CIBERSORT and ESTIMATE algorithms. Finally, we predicted the potential sensitive chemotherapeutic drugs in different risk score groups by the Genomics of Drug Sensitivity in Cancer (GDSC). A total of 491 IRGs were screened, and 14 IRGs were identified to be significantly related to overall survival (OS) and applied to construct an immune-related gene (IRG) prognostic signature (IRGSig) for CRC patients. Calibration plots showed that nomograms have powerful predictive ability. PCA and ROC analysis further verified the predictive value of this fourteen-gene prognostic model in three independent databases. Furthermore, we discovered that the tumor microenvironment changed significantly during the tumor development process, from early to middle to late stage, which may be an essential factor for tumor deterioration. Finally, we selected six commonly used chemotherapeutic drugs that have the potential to be useful in the treatment of CRC. Altogether, immune genes were used to construct a prognosis model for CRC patients, and a variety of methods were used to test the accuracy of this model. In addition, we explored the immune mechanisms of CRC through immune cell infiltration and TME in CRC. Furthermore, we assessed the therapeutic sensitivity of many commonly used chemotherapeutic medicines in individuals with varying risk factors. Finally, the immune risk model and immune mechanism of CRC were thoroughly investigated in this paper.

Identification and validation of a prognostic risk-scoring model based on sphingolipid metabolism-associated cluster in colon adenocarcinoma

Colon adenocarcinoma (COAD) is the primary factor responsible for cancer-related mortalities in western countries, and its development and progression are affected by altered sphingolipid metabolism. The current study aimed at investigating the effects of sphingolipid metabolism-related (SLP) genes on multiple human cancers, especially on COAD. We obtained 1287 SLP genes from the GeneCard and MsigDb databases along with the public transcriptome data and the related clinical information. The univariate Cox regression analysis suggested that 26 SLP genes were substantially related to the prognosis of COAD, and a majority of SLP genes served as the risk genes for the tumor, insinuating a potential pathogenic effect of SLP in COAD development. Pan-cancer characterization of SLP genes summarized their expression traits, mutation traits, and methylation levels. Subsequently, we focused on the thorough research of COAD. With the help of unsupervised clustering, 1008 COAD patients were successfully divided into two distinct subtypes (C1 and C2). C1 subtype is characterized by a poor prognosis, activation of SLP pathways, high expression of SLP genes, disordered carcinogenic pathways, and immune microenvironment. Based on the clusters of SLP, we developed and validated a novel prognostic model, consisting of ANO1, C2CD4A, EEF1A2, GRP, HEYL, IGF1, LAMA2, LSAMP, RBP1, and TCEAL2, to quantitatively evaluate the clinical outcomes of COAD. The Kaplain-Meier survival curves and ROC curves highlighted the accuracy of our SLP model in both internal and external cohorts. Compared to normal colon tissues, expression of C2CD4A was detected to be significantly higher in COAD; whereas, expression levels of EEF1A2, IGF1, and TCEAL2 were detected to be significantly lower in COAD. Overall, our research emphasized the pathogenic role of SLP in COAD and found that targeting SLP might help improve the clinical outcomes of COAD. The risk model based on SLP metabolism provided a new horizon for prognosis assessment and customized patient intervention.