Development and Validation of an Individualized Immune-Related Gene Pairs Prognostic Signature in Papillary Renal Cell Carcinoma

A novel immune-related gene signature for predicting immunotherapy outcomes and survival in clear cell renal cell carcinoma

Clear cell renal carcinoma (ccRCC) is one of the most common cancers worldwide. In this study, a new model of immune-related genes was developed to predict the overall survival and immunotherapy efficacy in patients with ccRCC. Immune-related genes were obtained from the ImmPort database. Clinical data and transcriptomics of ccRCC samples were downloaded from GSE29609 and The Cancer Genome Atlas. An immune-related gene-based prognostic model (IRGPM) was developed using the least absolute shrinkage and selection operator regression algorithm and multivariate Cox regression. The reliability of the developed models was evaluated by Kaplan-Meier survival curves and time-dependent receiver operating characteristic curves. Furthermore, we constructed a nomogram based on the IRGPM and multiple clinicopathological factors, along with a calibration curve to examine the predictive power of the nomogram. Overall, this study investigated the association of IRGPM with immunotherapeutic efficacy, immune checkpoints, and immune cell infiltration. Eleven IRGs based on 528 ccRCC samples significantly associated with survival were used to construct the IRGPM. Remarkably, the IRGPM, which consists of 11 hub genes (SAA1, IL4, PLAUR, PLXNB3, ANGPTL3, AMH, KLRC2, NR3C2, KL, CSF2, and SEMA3G), was found to predict the survival of ccRCC patients accurately. The calibration curve revealed that the nomogram developed with the IRGPM showed high predictive performance for the survival probability of ccRCC patients. Moreover, the IRGPM subgroups showed different levels of immune checkpoints and immune cell infiltration in patients with ccRCC. IRGPM might be a promising biomarker of immunotherapeutic responses in patients with ccRCC. Overall, the established IRGPM was valuable for predicting survival, reflecting the immunotherapy response and immune microenvironment in patients with ccRCC.

Insulin-like growth factor 2 receptor is a key immune-related gene that is correlated with a poor prognosis in patients with triple-negative breast cancer: A bioinformatics analysis

BACKGROUND

Immunotherapy plays an important role in the treatment of triple-negative breast cancer (TNBC). This study aimed to identify immune-related genes that are associated with the prognosis of patients with TNBC as possible targets of immunotherapy, alongside their related tumor-infiltrating lymphocytes (TILs).

METHODS

The clinical data and gene expression profiles of patients with breast cancer were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and divided into training (n = 1,053) and verification (n = 508) groups. CIBERSORT was used to predict the differences in immune cell infiltration in patient subsets that were stratified according to risk. Gene Ontology (GO) enrichment analysis was used to identify pathways associated with immune-related genes in patient subsets that were stratified according to risk. The clinical data and insulin-like growth factor 2 receptor (IGF2R) expression profiles of patients with breast cancer were extracted from METABRIC. The expression of IGF2R and TILs were evaluated in a cohort containing 282 untreated patients with TNBC. The correlations of IGF2R expression, TILs, and clinicopathological parameters with patient prognosis were analyzed in the whole cohort.

RESULTS

The prognostic model, which was composed of 26 immune-related gene pairs, significantly distinguished between high- and low-risk patients. Univariate and multivariate analyses indicated that the model was an independent prognostic factor for breast cancer. Among the identified genes, the expression of IGF2R significantly distinguished between high- and low-risk patients in TCGA (P = 0.008) and in METABRIC patients (P < 0.001). The expression of IGF2R was significantly associated with clinical risk factors such as TNBC, estrogen receptor (ER)-negative expression, human epidermal growth factor receptor 2 (HER2)-positive expression, and age ≤60 years old in METABRIC patients. In addition, the patients with IGF2R-positive expression had lower disease-free survival (DFS) rates than those with IGF2R-negative expression in the TNBC cohort (67.8% vs. 78.5%, P = 0.023). IGF2R expression also was significantly negatively correlated with TILs, particularly with CD8+ TILs and CD19+ TILs in the cohort of patients with TNBC.

CONCLUSION

IGF2R can be used as an indicator of a poor prognosis in patients with TNBC and as a potential target and research direction for TNBC immunotherapy in the future.

Identification of an immune-related gene prognostic index for predicting survival and immunotherapy efficacy in papillary renal cell carcinoma

Despite considerable progress has been made in the understanding of the genetics and molecular biology of renal cell carcinoma (RCC), therapeutic options of patients with papillary renal cell carcinoma (PRCC) are limited. Immunotherapy based on immune checkpoint inhibitors (ICIs) has become a hot point in researching new drug for tumor and been tested in a number of human clinical trials. In this study, an immune-related gene prognostic index (IRGPI) was developed and provided a comprehensive and systematic analysis of distinct phenotypic and molecular portraits in the recognition, surveillance, and prognosis of PRCC. The reliability of the IRGPI was evaluated using independent datasets from GEO database and the expression levels of the genes in the IRGPI detected by real-time PCR. Collectively, the currently established IRGPI could be used as a potential biomarker to evaluate the response and efficacy of immunotherapy in PRCC.

A 25 Immune-Related Gene Pair Signature Predicts Overall Survival in Cervical Cancer

Mounting evidence suggests that the tumor microenvironment plays an important role in the occurrence and development of cancer, with immune system dysfunction being closely related to malignant cancers. We aimed to screen immune-related genes (IRGs) to generate an IRG pair (IRGP)-based prognostic signature for cervical cancer (CC). Datasets were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases and used as training and validation cohorts, respectively. Using the ImmPort database, IRGs in control and CC samples were compared, and differentially expressed genes were identified to construct an IRGP prognostic signature. Based on this analysis, 25 IRGPs were identified as important factors for the prognosis of CC. Univariate and multivariate Cox regression analyses further showed that the IRGP signature was an independent prognostic factor of overall survival. In summary, we successfully constructed an IRGP prognostic signature of CC, providing insights into immunotherapy for CC.

Identification and validation of an immune-related gene pairs signature for three urologic cancers

Reliable biomarkers are needed to recognize urologic cancer patients at high risk for recurrence. In this study, we built a novel immune-related gene pairs signature to simultaneously predict recurrence for three urologic cancers. We gathered 14 publicly available gene expression profiles including bladder, prostate and kidney cancer. A total of 2,700 samples were classified into the training set (n = 1,622) and validation set (n = 1,078). The 25 immune-related gene pairs signature consisting of 41 unique genes was developed by the least absolute shrinkage and selection operator regression analysis and Cox regression model. The signature stratified patients into high- and low-risk groups with significantly different relapse-free survival in the meta-training set and its subpopulations, and was an independent prognostic factor of urologic cancers. This signature showed a robust ability in the meta-validation and multiple independent validation cohorts. Immune and inflammatory response, chemotaxis and cytokine activity were enriched with genes relevant to the signature. A significantly higher infiltration level of M1 macrophages was found in the high-risk group versus the low-risk group. In conclusion, our signature is a promising prognostic biomarker for predicting relapse-free survival in patients with urologic cancer.

Anti-Tumor Role of CAMK2B in Remodeling the Stromal Microenvironment and Inhibiting Proliferation in Papillary Renal Cell Carcinoma

The tumor microenvironment (TME) is variable across tumor types and has diverse effects on malignant progression, based on the type and number of infiltrating stromal cells. In particular, TME effector genes and their competitive endogenous RNA (ceRNA) networks play a critical role in regulating malignant tumor progression. However, the core effector molecules involved in TME modulation of kidney renal papillary cell carcinoma (KIRP) are poorly understood. To address this question, a cohort containing 233 KIRP patients was derived from The Cancer Genome Atlas (TCGA) database, and the data were processed using the ESTIMATE algorithm. We further evaluated the relationship between immune scores (ISs) and stromal scores (SSs) and disease progression and found that high SSs were associated with a poor prognosis in KIRP. Differentially expressed genes (DEGs) were therefore screened based on SS scores, resulting in 2509 DEGs, including 1668 mRNAs, 783 long noncoding (lnc)RNAs, and 58 micro (mi)RNAs. DEGs were then filtered using the random variance and subjected to hierarchical clustering using EPCLUST. Weighted gene co-expression network analysis (WGCNA) was used to assess the prognostic capacity of these DEGs and identify target ceRNA networks, and lncRNA GUSBP11/miR-432-5p/CAMK2B in the turquoise module was selected as a promising ceRNA network. From this analysis CAMK2B was selected as the core gene predicted to be involved in stromal TMA regulation. We therefore explored the expression and function of CAMK2B in vitro and in vivo and provide evidence that this protein promotes stromal TME remodulation and inhibits proliferation in KIRP. Lastly, we show that vascular endothelial growth factor (VEGF), transforming growth factor (TGF)β, and close homolog of L1 (CHL1) act as downstream effectors of CAMK2B in KIRP. Thus, in this study, we show that the TME determines prognosis of KIRP patients via the core effector molecule CAMK2B, which mediates both microenvironmental remodeling and tumor progression. Based on these findings, we propose that remodeling of the stromal microenvironment could represent an improved therapeutic approach relative to immunotherapy for KIRP.

The Identification of a Tumor Infiltration CD8+ T-Cell Gene Signature That Can Potentially Improve the Prognosis and Prediction of Immunization Responses in Papillary Renal Cell Carcinoma

Background

CD8+ T cells, vital effectors pertaining to adaptive immunity, display close relationships to the immunization responses to kill tumor cells. Understanding the effect exerted by tumor infiltration CD8+ T cells in papillary renal cell carcinoma (papRCC) is critical for assessing the prognosis process and responses to immunization therapy in cases with this disease.

Materials and Approaches

The single-cell transcriptome data of papRCC were used for screening CD8+ T-cell-correlated differentially expressed genes to achieve the following investigations. On that basis, a prognosis gene signature associated with tumor infiltration CD8+ T cell was built and verified with The Cancer Genome Atlas data set. Risk scores were determined for papRCC cases and categorized as high- or low-risk groups. The prognosis significance for risk scores was assessed with multiple-variate Cox investigation and Kaplan–Meier survival curves. In addition, the possible capability exhibited by the genetic profiles of cases to assess the response to immunization therapy was further explored.

Results

Six hundred twenty-one cell death-inhibiting RNA genes were screened using single-cell RNA sequencing. A gene signature consisting of seven genes (LYAR, YBX1, PNRC1, TCF25, MYL12B, MINOS1, and LINC01420) was then identified, and this collective was considered to be an independent prognosis indicator that could strongly assess overall survival in papRCC. In addition, the data allowed papRCC cases to fall to cohorts at high and low risks, exhibiting a wide range of clinically related features as well as different CD8+ T-cell immunization infiltration and immunization therapy responses.

Conclusions

Our work provides a possible explanation for the limited response of current immunization checkpoint-inhibiting elements for combating papRCC. Furthermore, the researchers built a novel genetic signature that was able to assess the prognosis and immunotherapeutic response of cases. This may also be considered as a promising therapeutic target for the disease.

A construction and comprehensive analysis of ceRNA networks and infiltrating immune cells in papillary renal cell carcinoma

Background

As the second most common malignancy in adults, papillary renal cell carcinoma (PRCC) has shown an increasing trend in both incidence and mortality. Effective treatment for advanced metastatic PRCC is still lacking. In this study, we aimed to establish competitive endogenous RNA (ceRNA) networks related to PRCC tumorigenesis, and analyze the specific role of differentially expressed ceRNA components and infiltrating immune cells in tumorigenesis.

Methods

CeRNA networks were established to identify the key ceRNAs related to PRCC tumorigenesis based on the 318 samples from The Cancer Genome Atlas database (TCGA), including 285 PRCC and 33 normal control samples. The R package, “CIBERSORT,” was used to evaluate the infiltration of 22 types of immune cells. Then we identified the significant ceRNAs and immune cells, based on which two nomograms were obtained for predicting the prognosis in PRCC patients. Finally, we investigated the co‐expression of PRCC‐specific immune cells and core ceRNAs via Pearson correlation test.

Results

COL1A1, H19, ITPKB, LDLR, TCF4, and WNK3 were identified as hub genes in ceRNA networks. Four prognostic‐related tumor‐infiltrating immune cells, including T cells CD4 memory resting, Macrophages M1, and Macrophages M2 were revealed. Pearson correlation test indicated that Macrophage M1 was negatively related with COL1A1 (p < 0.01) and LDLR (p < 0.01), while Macrophage M2 was positively related with COL1A1 (p < 0.01), TCF4 (p < 0.01), and H19 (p = 0.032). Two nomograms were conducted with favorable accuracies (area under curve of 1‐year survival: 0.935 and 0.877; 3‐year survival: 0.849 and 0.841; and 5‐year survival: 0.818 and 0.775, respectively).

Conclusion

The study constructed two nomograms suited for PRCC prognosis predicting. Moreover, we concluded that H19‐miR‐29c‐3p‐COL1A1 axis might promote the polarization of M2 macrophages and inhibit M1 macrophage activation through Wnt signaling pathway, collaborating to promote PRCC tumorigenesis and lead to poor overall survival of PRCC patients.

A united risk model of 11 immune‑related gene pairs and clinical stage for prediction of overall survival in clear cell renal cell carcinoma patients

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer. Currently, we lack effective risk models for the prognosis of ccRCC patients. Given the significant role of cancer immunity in ccRCC, we aimed to establish a novel united risk model including clinical stage and immune-related gene pairs (IRGPs) to assess the prognosis. The gene expression profile and clinical data of ccRCC patients from The Cancer Genome Atlas and Arrayexpress were divided into training cohort (n = 381), validation cohort 1 (n = 156), and validation cohort 2 (n = 101). Through univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator analysis, 11 IRGPs were obtained. After further analysis, it was found that clinical stage could be an independent prognostic factor; hence, we used it to construct a united prognostic model with 11 IRGPs. Based on this model, patients were divided into high-risk and low-risk groups. In Kaplan–Meier analysis, a significant difference was observed in overall survival (OS) among all three cohorts (p < 0.001). The calibration curve revealed that the signature model is in high accordance with the observed values of each data cohort. The 1-year, 3-year, and 5-year receiver operating characteristic curves of each data cohort showed better performance than only IRGP signatures. The results of immune infiltration analysis revealed significantly (p < 0.05) higher abundance of macrophages M0, T follicular helper cells, and other tumor infiltrating cells. In summary, we successfully established a united prognostic risk model, which can effectively assess the OS of ccRCC patients.

A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas

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The Immune-related gene pairs (IRGPs) pronostic signature for LGG is correlated with immune cells infiltration.

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WGCNA presented a gene set correlating with immune cells infiltration and genes co-expression relationships were visualized.

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The nomogram constrcted by three IRGPs and clinical factors is a novel tailored tool for individual-level prediction.

Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.