Individualized immune-related gene signature predicts immune status and oncologic outcomes in clear cell renal cell carcinoma patients

Analysis of Pyroptosis-Related Immune Signatures and Identification of Pyroptosis-Related LncRNA Prognostic Signature in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common urinary system malignant tumor with a high incidence and recurrence rate. Pyroptosis is a kind of programmed cell death caused by inflammasomes. More and more evidence had confirmed that pyroptosis plays a very significant part in cancer, and it is controversial whether pyroptosis promotes or inhibits tumors. Consistently, its potential role in ccRCC treatment efficacy and prognosis remains unclear. In this study, we systematically investigated the role of pyroptosis in the ccRCC samples from The Cancer Genome Atlas (TCGA) database. Based on the differentially expressed pyroptosis-related genes (DEPRGs), we identified three pyroptosis subtypes with different clinical outcomes, immune signatures, and responses to immunotherapy. Gene set variation analysis (GSVA), Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that pyroptosis activation meant infiltration of more immune cells that is conducive to tumor progression. To further investigate the immunomodulatory effect of pyroptosis in ccRCC, we constructed a pyroptosis-score based on the common differential prognostic genes of the three pyroptosis subtypes. It was found that patients with high pyroptosis-score were in an unfavorable immune environment and the prognosis was worse. Gene set enrichment analysis suggested that immune-related biological processes were activated in the high pyroptosis-score group. Then, the least absolute shrinkage and selection operator (LASSO) Cox regression was implemented for constructing a prognostic model of eight pyroptosis-related long noncoding RNAs (PRlncRNAs) in the TCGA dataset, and the outcomes revealed that, compared with the low-risk group, the model-based high-risk group was intently associated with poor overall survival (OS). We further explored the relationship between high- and low-risk groups with tumor microenvironment (TME), immune infiltration, and drug therapy. Finally, we constructed and confirmed a robust and reliable PRlncRNA pairs prediction model of ccRCC, identified PRlncRNA, and verified it by experiments. Our findings suggested the potential role of pyroptosis in ccRCC, offering new insights into the prognosis of ccRCC and guiding effectual targeted therapy and immunotherapy.

Avoiding Absolute Quantification Trap: A Novel Predictive Signature of Clinical Benefit to Anti-PD-1 Immunotherapy in Non-Small Cell Lung Cancer

Immunotherapy has been focused on by many oncologists and researchers. While, due to technical biases of absolute quantification, few traditional biomarkers for anti-PD-1 immunotherapy have been applied in regular clinical practice of non-small cell lung cancer (NSCLC). Therefore, there is an urgent and unmet need for a feasible tool—immune to data source bias—for identifying patients who might benefit from ICIs in clinical practice. Using the strategy based on the relative ranking of gene expression levels, we herein proposed the novel BRGP index (BRGPI): four BRGPs significantly related with progression-free survival of NSCLC patients treated with anti-PD-1 immunotherapy in the multicohort analysis. Moreover, stratification and multivariate Cox regression analyses demonstrated that BRGPI was an independent prognostic factor. Notably, compared to PD-L1, BRGPI exerted the best predictive ability. Further analysis showed that the patients in the BRGPI-low and PD-L1-high subgroup derived more clinical benefits from anti-PD-1 immunotherapy. In conclusion, the prospect of applying the BRGPI to real clinical practice is promising owing to its powerful and reliable predictive value.

Development and Validation of an Immune-Related Gene Pairs Signature in Grade II/III Glioma

BACKGROUND

Gliomas are prevalent primary intracerebral malignant tumors. Increasing evidence indicates an association between the immune signature and Grade II/III glioma prognosis. Thus, we aimed to develop an immune-related gene pair (IRGP) signature that can be used as a prognostic tool in Grade II/III glioma.

METHODS

The gene expression levels and clinical information of Grade II/III glioma patients were collected from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The TCGA data were randomly divided into a training cohort (n = 249) and a validation cohort (n = 162), and a CGGA dataset served as an external validation group (n = 605). IRGPs significantly associated with prognosis were selected by Cox regression. Gene set enrichment analysis and filtration were performed with the IRGPs.

RESULTS

Within a set of 1991 immune genes, 8 IRGPs including 15 unique genes that significantly affect survival constituted a gene signature. In the validation datasets, the IRGP signature significantly stratified patients with Grade II/III glioma into low- and high-risk groups (P < 0.001), and the IRGP index was found to be an independent prognostic factor through univariate and multivariate analyses (P < 0.05). Additionally, 26 functional pathways were identified through the intersection of Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) enrichment analysis.

CONCLUSION

The IRGP signature demonstrated good prognostic value for Grade II/III gliomas, which may provide new insights into individual treatment for glioma patients. The IRGPs might function through the identified 26 functional pathways.

Identification of an Immune-Related Long Noncoding RNA Pairs Model to Predict Survival and Immune Features in Gastric Cancer

Background: Gastric cancer (GC) remains one of the most malignant tumors around the world, and an accurate model that reliably predicts survival and therapeutic efficacy is urgently needed. As a novel predictor for prognosis in a variety of cancers, immune-related long noncoding RNA pairs (IRlncRNAPs) have been reported to predict tumor prognosis. Herein, we integrated an IRlncRNAPs model to predict the clinical outcome, immune features, and chemotherapeutic efficacy of GC.

Methods: Based on the GC data obtained from The Cancer Genome Atlas (TCGA) database and the Immunology Database and Analysis Portal (ImmPort), differentially expressed immune-related long noncoding RNAs (DEIRlncRNAs) were identified. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis were used to select the most appropriate overall survival (OS)-related IRlncRNAPs to develop a prognostic signature. The riskScore of each sample was calculated by comparing the long noncoding RNA expression level in each IRlncRNAP. Based on the riskScore for each patient, GC patients were divided into high- and low-risk groups. Then, the correlation of the signature and riskScore with OS, clinical features, immune cell infiltration, immune-related gene (IRG) expression and chemotherapeutic efficacy in GC was analyzed.

Results: A total of 107 DEIRlncRNAs were identified which formed 4297 IRlncRNAPs. Fifteen OS-related IRlncRNAPs were selected to develop a prognostic model. GC patients could be accurately classified into high- and low-risk groups according to the riskScore of the prognostic model. The 1-, 2-, 3-, and 5-year receiver operating characteristic (ROC) curves for the riskScore were drawn and the area under the curve (AUC) values were found to be 0.788, 0.810, 0.825, and 0.868, respectively, demonstrating a high sensitivity and accuracy of this prognostic signature. Moreover, the immune-related riskScore was an independent risk factor. Patients showed a poorer outcome within the high-risk group. In addition, the riskScore was found to be significantly correlated with the clinical features, immune infiltration status, IRG expression, and chemotherapeutic efficacy in GC.

Conclusion: The prognostic model of IRlncRNAPs offers great promise in predicting the prognosis, immune infiltration status, and chemotherapeutic efficacy in GC, which might be helpful for the selection of chemo- and immuno-therapy of GC.

Evaluation of immune infiltrating of thyroid cancer based on the intrinsic correlation between pair-wise immune genes

INTRODUCTION

Unlike most mutation-driven cancers, thyroid cancer is thought to be highly dependent on changes in human hormone levels. It has become research hotspot using the change of gene expression level as a detection and diagnostic marker. The internal relationship between two genes and disease development is used to avoid the instability caused by single gene fluctuation. Aim It is possible to achieve early diagnosis in thyroid cancer during tumorigenesis and recurrence using IGPS (immune gene pairs).

METHODS

We extracted thyroid cancer data from The Cancer Genome Atlas (TCGA), using CIBERSORT algorithm to infiltrate out 22 immune cells types. We screened out IGPS that differ significantly between different groups, then used LinearSVC model to learn and screen features, combined with deep learning neural network model to predict benign and malignant cancer as well as patients at different groups.

KEY FINDINGS

There are significant differences of immune cell ratio in tumor stages and relapse samples. We screen out 42 and 64 IGPS for in normal-tumor and non-relapsed groups respectively, for example ASCC3-MAP3K7 and ATF2-SOCS5, have significant correlation in IGPS expression. Then we use the IGPS to train the tumor diagnostic classifier, obtain average AUC are both 0.99 after ten times cross-validation.

SIGNIFICANCE

The IGPS gives us new insight to explore immune cell infiltration of thyroid cancer, deep learning model can be further used in early diagnosis of thyroid cancer and estimation of the risk of recurrence.

Comprehensive analysis of a 14 immune-related gene pair signature to predict the prognosis and immune features of gastric cancer

BACKGROUND

As a new method for predicting tumor prognosis, the predictive effect of immune-related gene pairs (IRGPs) has been confirmed in several cancers, but there is no comprehensive analysis of the clinical significance of IRGPs in gastric cancer (GC).

METHOD

Clinical and gene expression profile data of GC patients were obtained from the GEO database. Based on the ImmPort database, differentially expressed immune-related gene (DEIRG) events were determined by a comparison of GC samples and adjacent normal samples. Cox proportional regression was used to construct an IRGP signature, and its availability was validated using three external validation datasets. In addition, we explored the association between clinical data and immune features and established a nomogram to predict outcomes in GC patients.

RESULT

A total of 88 DEIRGs were identified in GC from the training set, which formed 3828 IRGPs. Fourteen overall survival (OS)-related IRGPs were used to construct the prognostic signature. As a result, patients in the high-risk group exhibited poorer OS compared to those in the low-risk group. In addition, the fraction of CD8+ T cells, plasma cells, CD4 memory activated T cells, and M1 macrophages was higher in the high-risk group. Expression of two immune checkpoints, CD276 and VTCN1, was significantly higher in the high-risk group as well. Based on the independent prognostic factors, a nomogram was established and showed excellent performance.

CONCLUSION

The 14 OS-related IRGP signature was associated with OS, immune cells, and immune checkpoints in GC patients, and it could provide the basis for related immunotherapy.

Profiles of overall survival-related gene expression-based risk signature and their prognostic implications in clear cell renal cell carcinoma

The present work aimed to evaluate the prognostic value of overall survival (OS)-related genes in clear cell renal cell carcinoma (ccRCC) and to develop a nomogram for clinical use. Transcriptome data from The Cancer Genome Atlas (TCGA) were collected to screen differentially expressed genes (DEGs) between ccRCC patients with OS > 5 years (149 patients) and those with <1 year (52 patients). In TCGA training set (265 patients), seven DEGs (cytochrome P450 family 3 subfamily A member 7 (CYP3A7), contactin-associated protein family member 5 (CNTNAP5), adenylate cyclase 2 (ADCY2), TOX high mobility group box family member 3 (TOX3), plasminogen (PLG), enamelin (ENAM), and collagen type VII α 1 chain (COL7A1)) were further selected to build a prognostic risk signature by the least absolute shrinkage and selection operator (LASSO) Cox regression model. Survival analysis confirmed that the OS in the high-risk group was dramatically shorter than their low-risk counterparts. Next, univariate and multivariate Cox regression revealed the seven genes-based risk score, age, and Tumor, lymph Node, and Metastasis staging system (TNM) stage were independent prognostic factors to OS, based on which a novel nomogram was constructed and validated in both TCGA validation set (265 patients) and the International Cancer Genome Consortium cohort (ICGC, 84 patients). A decent predictive performance of the nomogram was observed, the C-indices and corresponding 95% confidence intervals of TCGA training set, validation set, and ICGC cohort were 0.78 (0.74–0.82), 0.75 (0.70–0.80), and 0.70 (0.60–0.80), respectively. Moreover, the calibration plots of 3- and 5 years survival probability indicated favorable curve-fitting performance in the above three groups. In conclusion, the proposed seven genes signature-based nomogram is a promising and robust tool for predicting the OS of ccRCC, which may help tailor individualized therapeutic strategies.

Genomic profiling in renal cell carcinoma

The treatment landscape of metastatic renal cell carcinoma (RCC) has been revolutionized over the past two decades, bringing forth an era in which more than a dozen therapeutic agents are now available to treat patients. As a consequence, personalized care has become a critical part of developing effective treatment guidelines and improving patient outcomes. One of the most important emerging aspects of precision medicine in cancer is matching patients and treatments based on the genomic characteristics of an individual and their tumour. Despite the lack of a single genomic predictor of treatment response or prognostication feature in RCC, emerging research suggests that the identification of such markers remains promising. Mutations in VHL and alterations in its downstream pathways are the mainstay of RCC development and progression. However, the predictive value of VHL mutations has been questioned. Further research has examined mutations in genes involved in chromosome remodelling (for example, PBRM1, BAP1 and SETD2), DNA methylation and DNA damage repair, all of which have been associated with clinical outcomes. Here, we provide a comprehensive overview of genomic evidence in the context of RCC and its potential predictive and prognostic value.