Copper homeostasis and cuproptosis in cancer immunity and therapy.

Copper is an essential nutrient for maintaining enzyme activity and transcription factor function. Excess copper results in the aggregation of lipoylated dihydrolipoamide S-acetyltransferase (DLAT), which correlates to the mitochondrial tricarboxylic acid (TCA) cycle, resulting in proteotoxic stress and eliciting a novel cell death modality: cuproptosis. Cuproptosis exerts an indispensable role in cancer progression, which is considered a promising strategy for cancer therapy. Cancer immunotherapy has gained extensive attention owing to breakthroughs in immune checkpoint blockade; furthermore, cuproptosis is strongly connected to the modulation of antitumor immunity. Thus, a thorough recognition concerning the mechanisms involved in the modulation of copper metabolism and cuproptosis may facilitate improvement in cancer management. This review outlines the cellular and molecular mechanisms and characteristics of cuproptosis and the links of the novel regulated cell death modality with human cancers. We also review the current knowledge on the complex effects of cuproptosis on antitumor immunity and immune response. Furthermore, potential agents that elicit cuproptosis pathways are summarized. Lastly, we discuss the influence of cuproptosis induction on the tumor microenvironment as well as the challenges of adding cuproptosis regulators to therapeutic strategies beyond traditional therapy.

Identification of prognostic and therapeutic biomarkers in type 2 papillary renal cell carcinoma.

BACKGROUND: Papillary renal cell carcinoma (PRCC) can be divided into type 1 (PRCC1) and type 2 (PRCC2) and PRCC2 share a more invasive phenotype and worse prognosis. This study aims to identify potential prognostic and therapeutic biomarkers in PRCC2. METHODS: A cohort from The Cancer Genome Atlas and two datasets from Gene Expression Omnibus were examined. Common differentially expressed genes (DEGs) were screened and potential biomarkers were explored by using Kaplan-Meier method and cox regression analysis. Functional enrichment analysis was utilized to evaluate the potential biological functions. Tumor infiltrating immune cells were estimated by CIBERSORT algorithm. Ninety-two PRCC2 samples from Fudan University Shanghai Cancer Center were obtained, and immunostaining was performed to validate prognostic and therapeutic significance of the potential biomarker. RESULTS: PRCC2 has worse overall survival and shares distinct molecular characteristics from PRCC1. There was significant higher expression level of Targeting protein for Xklp2 (TPX2) in PRCC2 compared with normal tissues. Higher expression level of TPX2 was significantly associated with worse overall survival in PRCC2 and kinesin family genes expression were found significantly elevated in high risk PRCC2. Abundance of tumor infiltrating M1 macrophage was significantly higher in PRCC2 and it was also associated with worse overall survival. In the FUSCC cohort, higher TPX2 expression was significantly correlated with worse overall and progression-free survival. Retrospective analysis indicated that mTOR inhibitor (everolimus) had greater efficacy in the high-risk group than in the low-risk group (overall response rate: 28.6% vs. 16.7%) and that everolimus had greater efficacy than sunitinib in the high-risk group (overall response rate: 28.6% vs. 20%). CONCLUSIONS: TPX2 was a prognostic and therapeutic biomarker in PRCC2. Higher abundance of tumor infiltrating M1 macrophage was significantly associated with worse overall survival in PRCC2. mTOR inhibitors may have good efficacy in patients with high-risk PRCC2.

The Inflammasomes Adaptor Protein PYCARD Is a Potential Pyroptosis Biomarker Related to Immune Response and Prognosis in Clear Cell Renal Cell Carcinoma.

PYCARD is a protein engaged in inflammation, pyroptosis, and apoptosis. However, the function of PYCARD in human cancers remains unclear. The objective of our study was to explore PYCARD expression and prognostic value in human cancers. Public databases were used to assess PYCARD expression and prognostic value. The TISIDB database was used to explore the associations between PYCARD expression and different immune subtypes. The correlations between PYCARD expression and ICP genes, MMR genes, MSI, and TMB were also investigated. The immunotherapy response was assessed using the TIDE database. Single-cell RNA databases evaluated the PYCARD expression of immune cells. External datasets and immunohistochemical staining were conducted to validate PYCARD expression and prognostic value. The results showed that PYCARD expression varied in several cancers and was associated with prognosis, immune-related genes, published biomarkers, and immunotherapy response. Of note, PYCARD expression was upregulated in renal cancers with high diagnostic ability. Upregulation of PYCARD was correlated with worse prognosis in KIRC and external validation cohorts. In conclusion, PYCARD demonstrated strong correlations with prognosis, immune response, and disease progression in pan-cancer analysis. In ccRCC, PYCARD might serve as a biomarker for diagnosis and therapeutic target-boosting immunotherapy response.

♣Evaluation of clinicopathological profiles and development of a risk model in renal epithelioid angiomyolipoma patients: a large-scale retrospective cohort study.

BACKGROUND: To identify the malignant potential and prognostic indicators of renal epithelioid angiomyolipoma (eAML), clinicopathological and molecular features as well as the drug efficacy of 67 eAML cases were analyzed. MATERIALS AND METHODS: Sixty-seven renal eAML patients were enrolled and the immunohistochemical features of these patients were examined. FFPE slides of all patients were re-examined. 21 patients with metastasis received Everolimus 10 mg orally once daily. Responses were evaluated with RECIST criteria by three authors. A risk stratification model was constructed using the following factors: pT3 and pT4, presence of necrosis, mitotic count ≥ 2; the presence of atypical mitoses; severe nuclear atypia, SMA negative, Ki-67 ≥ 10%. RESULTS: The average percentage of the epithelioid component was 85.6% (range 80-95%). Immunohistochemically, Ki-67 ≥ 10% and negative SMA staining were significantly correlated with malignant characteristics (Ki-67: p < 0.001; SMA: p = 0.001). Survival analysis suggested that pT3-pT4 stage, presence of necrosis, severe nuclear atypia, presence of atypical mitoses, mitotic count ≥ 2, Ki-67 ≥ 10% and negative SMA expression were significantly associated with poorer PFS and OS (p < 0.05). The risk model sufficiently discriminated recurrence/metastasis (AUC = 0.897) and cancer-specific mortality (AUC = 0.932) of renal eAML patients in different risk groups. 21 patients had received Everolimus targeted therapy after recurrence/metastasis. The best response for Everolimus treatment was 8/21 (38.1%) partial responses (PR), 9/21 (42.9%) stable disease (SD) and 4/21 (19.0%) progressive disease (PD). CONCLUSION: The risk stratification model could well distinguish eAML patients at high risk of recurrence/metastasis. Everolimus targeted treatment showed good efficacy in patients with recurrence/metastasis.

Adversarial training for prostate cancer classification using magnetic resonance imaging.

Background: To use adversarial training to increase the generalizability and diagnostic accuracy of deep learning models for prostate cancer diagnosis. Methods: This multicenter study retrospectively included 396 prostate cancer patients who underwent magnetic resonance imaging (development set, 297 patients from Shanghai Jiao Tong University Affiliated Sixth People's Hospital and Eighth People's Hospital; test set, 99 patients from Renmin Hospital of Wuhan University). Two binary classification deep learning models for clinically significant prostate cancer classification [PM1, pretraining Visual Geometry Group network (VGGNet)-16-based model 1; PM2, pretraining residual network (ResNet)-50-based model 2] and two multiclass classification deep learning models for prostate cancer grading (PM3, pretraining VGGNet-16-based model 3; PM4: pretraining ResNet-50-based model 4) were built using apparent diffusion coefficient and T2-weighted images. These models were then retrained with adversarial examples starting from the initial random model parameters (AM1, adversarial training VGGNet-16 model 1; AM2, adversarial training ResNet-50 model 2; AM3, adversarial training VGGNet-16 model 3; AM4, adversarial training ResNet-50 model 4, respectively). To verify whether adversarial training can improve the diagnostic model's effectiveness, we compared the diagnostic performance of the deep learning methods before and after adversarial training. Receiver operating characteristic curve analysis was performed to evaluate significant prostate cancer classification models. Differences in areas under the curve (AUCs) were compared using Delong's tests. The quadratic weighted kappa score was used to verify the PCa grading models. Results: AM1 and AM2 had significantly higher AUCs than PM1 and PM2 in the internal validation dataset (0.84 vs. 0.89 and 0.83 vs. 0.87) and test dataset (0.73 vs. 0.86 and 0.72 vs. 0.82). AM3 and AM4 showed higher κ values than PM3 and PM4 in the internal validation dataset {0.266 [95% confidence interval (CI): 0.152-0.379] vs. 0.292 (95% CI: 0.178-0.405) and 0.254 (95% CI: 0.159-0.390) vs. 0.279 (95% CI: 0.163-0.396)} and test set [0.196 (95% CI: 0.029-0.362) vs. 0.268 (95% CI: 0.109-0.427) and 0.183 (95% CI: 0.015-0.351) vs. 0.228 (95% CI: 0.068-0.389)]. Conclusions: Using adversarial examples to train prostate cancer classification deep learning models can improve their generalizability and classification abilities.

Increased expression of tribbles homolog 3 predicts poor prognosis and correlates with tumor immunity in clear cell renal cell carcinoma: a bioinformatics study.

Tribbles homolog 3 (TRIB3), a pseudokinase that regulates multiple intracellular signaling pathways, has been reported to promote the growth of multiple tumors. However, its role in clear cell renal cell carcinoma (ccRCC) remains unelucidated. We evaluated the role of TRIB3 in ccRCC using publicly available data from The Cancer Genome Atlas and analyzed its relationship with the tumor microenvironment; moreover, we used gene knockout and overexpression techniques to detect the effects of TRIB3 on the biological behavior of ccRCC cells. RT-qPCR and western blotting were used to detect transfection efficiency, and the invasiveness of ccRCC cells was determined by Transwell migration assays. We found that TRIB3 overexpression was significantly associated with increased grade, stage, and distant metastasis, positively correlated with ccRCC invasiveness, and also an independent risk factor for overall survival (OS). In addition, 361 differentially expressed genes (DEGs) related to TRIB3 were identified. Functional enrichment analysis showed that DEGs were mainly enriched in humoral immune responses, collagen-containing extracellular matrix, and serine hydrolase activity. Immune landscape characterization revealed that TRIB3 expression was significantly and negatively associated with CD8+ T and hematopoietic stem cells, whereas it was positively associated with NK T and macrophage M1 cells. Single-cell sequencing showed that localization and binding targets of TRIB3 mainly involved monocytes/macrophages and CD4+ and CD8+ T cells. Overall, our study revealed that elevated TRIB3 expression represents a promising prognostic marker for ccRCC patients and may play a key role in tumor microenvironment modulation.

Identification of prognostic biomarkers in papillary renal cell carcinoma and PTTG1 may serve as a biomarker for predicting immunotherapy response.

OBJECTIVE: This study aims to identify potential prognostic and therapeutic biomarkers in papillary renal cell carcinoma (pRCC). METHODS: Two microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified. The protein-protein interaction (PPI) networks and functional annotations of DEGs were established. Survival analysis was utilized to evaluate the prognostic significance of the DEGs and the association between the expression level of candidate biomarkers and various tumour-infiltrating immune cells was explored. The role of PTTG1 in tumour microenvironments (TME) was further explored using Single-cell RNA-seq and its prognostic and therapeutic significance was validated in Fudan University Shanghai Cancer Centre (FUSCC) cohort. RESULTS: Eight genes, including BUB1B, CCNB1, CCNB2, MAD2L1, TTK, CDC20, PTTG1, and MCM were found to be negatively associated with patients' prognosis. The expression level of PTTG1 was found to be significantly associated with lymphocytes, immunomodulators, and chemokine in the TCGA cohort. Single-cell RNA-seq information indicated that PTTG1 was strongly associated with the proliferation of T cells. In the FUSCC cohort, the expression level of PTTG1 was also statistically significant for both progression-free survival (PFS) and overall survival (OS) prediction (HR = 2.683, p < .001; HR = 2.673, p = .001). And higher expression level of PTTG1 was significantly associated with immune checkpoint blockade (ICB) response in the FUSCC cohort (χ2=3.99, p < .05). CONCLUSIONS: Eight genes were identified as a prognostic biomarker and the expression level of PTTG1 was also found to serve as a potential predictor for ICB response in pRCC patients.Key messages:Eight genes, including BUB1B, CCNB1, CCNB2, MAD2L1, TTK, CDC20, PTTG1, and MCM were found to be negatively associated with pRCC patients' prognosis.Expression level of PTTG1 was significantly associated with tumour microenvironment including lymphocytes, immunomodulators, and chemokines.Higher expression level of PTTG1 was significantly associated with immune checkpoint blockade (ICB) response in FUSCC cohort.

Large-Scale Proteomics Data Reveal Integrated Prognosis-Related Protein Signatures and Role of SMAD4 and RAD50 in Prognosis and Immune Infiltrations of Prostate Cancer Microenvironment.

As prostate cancer (PCa) is one of the most commonly diagnosed cancer worldwide, identifying potential prognostic biomarkers is crucial. In this study, the survival information, gene expression, and protein expression data of 344 PCa cases were collected from the Cancer Proteome Atlas (TCPA) and the Cancer Genome Atlas (TCGA) to investigate the potential prognostic biomarkers. The integrated prognosis-related proteins (IPRPs) model was constructed based on the risk score of each patients using machine-learning algorithm. IPRPs model suggested that Elevated RAD50 expression (p = 0.016) and down-regulated SMAD4 expression (p = 0.017) were significantly correlated with unfavorable outcomes for PCa patients. Immunohistochemical (IHC) staining and western blot (WB) analysis revealed significant differential expression of SMAD4 and RAD50 protein between tumor and normal tissues in validation cohort. According to the overall IHC score, patients with low SMAD4 (p < 0.0001) expression and high RAD50 expression (p = 0.0001) were significantly correlated with poor outcomes. Besides, expression of SMAD4 showed significantly negative correlation with most immune checkpoint molecules, and the low SMAD4 expression group exhibited significantly high levels of LAG3 (p < 0.05), TGFß (p < 0.001), and PD-L1 (p < 0.05) compared with the high SMAD4 expression group in the validation cohort. Patients with low SMAD4 expression had significantly higher infiltration of memory B cells (p = 0.002), CD8 + T cells (p < 0.001), regulatory T cells (p = 0.006), M2-type macrophages (p < 0.001), and significantly lower infiltration of naïve B cells (p = 0.002), plasma cells (p < 0.001), resting memory CD4 + T cells (p < 0.001) and eosinophils (p = 0.045). Candidate proteins were mainly involved in antigen processing and presentation, stem cell differentiation, and type I interferon pathways. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-022-00070-1.

Protumorigenic Role of Elevated Levels of DNA Polymerase Epsilon Predicts an Immune-Suppressive Microenvironment in Clear Cell Renal Cell Carcinoma.

Increasing evidence indicates that DNA polymerase epsilon (POLE), which mediates DNA damage repair, is significantly associated with tumor prognosis. This study aimed to analyze POLE expression in tumor samples and its prognostic value for patients with clear cell renal cell carcinoma (ccRCC). We found significantly elevated POLE expression in ccRCC tissues compared with normal tissues of multiple independent cohorts. The POLE expression levels of 523 patients with ccRCC (The Cancer Genome Atlas RNA-seq data) and 179 patients with ccRCC with immunohistochemical data (Fudan University Shanghai Cancer Center) were analyzed to investigate the prognostic implications of POLE expression. Cox regression analyses were implemented to explore the effect of POLE expression on the prognosis of pan-cancer. These findings revealed that elevated POLE expression levels significantly correlated with shorter overall survival (p < 0.001, n = 701) of patients with ccRCC. These data indicate that POLE expression may serve as a prognostic biomarker for cancers. Although POLE mutations were not significantly associated with survival benefits conferred upon patients with ccRCC, a CD4+ T cell-regulated immune microenvironment was significantly activated. Moreover, we found that POLE expression in cancers significantly correlated with an immunosuppressive tumor microenvironment, higher intratumoral heterogeneity, and expression of immune checkpoint genes PDCD1, CTLA4, and CD86, possibly mediated via the JAK/STAT and Notch signaling pathways. In conclusion, the present study is the first to our knowledge to indicate that elevated POLE expression is significantly associated with poor survival and an immune-suppressive tumor microenvironment in ccRCC. These findings suggest that POLE can serve as a biomarker for guiding molecular diagnosis and facilitating the development of novel individual therapeutic strategies for patients with advanced ccRCC.

Synthesizing High-b-Value Diffusion-weighted Imaging of the Prostate Using Generative Adversarial Networks.

PURPOSE: To develop and evaluate a diffusion-weighted imaging (DWI) deep learning framework based on the generative adversarial network (GAN) to generate synthetic high-b-value (b =1500 sec/mm2) DWI (SYNb1500) sets from acquired standard-b-value (b = 800 sec/mm2) DWI (ACQb800) and acquired standard-b-value (b = 1000 sec/mm2) DWI (ACQb1000) sets. MATERIALS AND METHODS: This retrospective multicenter study included 395 patients who underwent prostate multiparametric MRI. This cohort was split into internal training (96 patients) and external testing (299 patients) datasets. To create SYNb1500 sets from ACQb800 and ACQb1000 sets, a deep learning model based on GAN (M0) was developed by using the internal dataset. M0 was trained and compared with a conventional model based on the cycle GAN (Mcyc). M0 was further optimized by using denoising and edge-enhancement techniques (optimized version of the M0 [Opt-M0]). The SYNb1500 sets were synthesized by using the M0 and the Opt-M0 were synthesized by using ACQb800 and ACQb1000 sets from the external testing dataset. For comparison, traditional calculated (b =1500 sec/mm2) DWI (CALb1500) sets were also obtained. Reader ratings for image quality and prostate cancer detection were performed on the acquired high-b-value (b = 1500 sec/mm2) DWI (ACQb1500), CALb1500, and SYNb1500 sets and the SYNb1500 set generated by the Opt-M0 (Opt-SYNb1500). Wilcoxon signed rank tests were used to compare the readers' scores. A multiple-reader multiple-case receiver operating characteristic curve was used to compare the diagnostic utility of each DWI set. RESULTS: When compared with the Mcyc, the M0 yielded a lower mean squared difference and higher mean scores for the peak signal-to-noise ratio, structural similarity, and feature similarity (P < .001 for all). Opt-SYNb1500 resulted in significantly better image quality (P ≤ .001 for all) and a higher mean area under the curve than ACQb1500 and CALb1500 (P ≤ .042 for all). CONCLUSION: A deep learning framework based on GAN is a promising method to synthesize realistic high-b-value DWI sets with good image quality and accuracy in prostate cancer detection.Keywords: Prostate Cancer, Abdomen/GI, Diffusion-weighted Imaging, Deep Learning Framework, High b Value, Generative Adversarial Networks© RSNA, 2021 Supplemental material is available for this article.

Clear Cell Papillary Renal Cell Carcinoma Shares Distinct Molecular Characteristics and may be Significantly Associated With Higher Risk of Developing Second Primary Malignancy.

Traditionally, clear cell papillary renal cell carcinoma (ccpRCC) was considered to share similar molecular and histological characteristics with clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC). Here we aimed to identify somatic and germline variants of ccpRCC. For this purpose, we conducted whole-exome sequencing to detect somatic variants in the tissues of 18 patients with pathologically confirmed ccpRCC, who underwent surgical treatment at Fudan University Shanghai Cancer Center. Targeted sequencing was conducted to detect germline variants in paired tumor or normal tissues or blood. Somatic and germline variants of ccRCC and Renal cell carcinoma included in The Cancer Genome Atlas data and other published data were analyzed as well. The molecular profiles of ccpRCC, ccRCC and pRCC were compared. Among the 387 somatic variants identified, TCEB1 (3/18) and VHL (3/18) variants occurred at the highest frequencies. Germline mutation detection showed that nine variants associated with Fanconi anemia (VAFAs) pathway (FANCA, 6/18; FANCI, 3/18) were identified in 18 ccpRCC patients. Among ccpRCC patients with VAFAs, five out of eight patients had second primary malignancy or family history of cancer. Somatic variants characteristics may distinguish ccpRCC from ccRCC or pRCC and germline VAFAs may be a molecular characterization of ccpRCC. Compared with ccRCC or pRCC, ccpRCC patients may be significantly correlated with higher risk of developing second primary malignancy.

[Generation and phenotypic characterization of S100A9 gene knockout mice by CRISPR/Cas9-mediated gene targeting].

S100 calcium binding protein A9 (S100A9) is involved in a variety of biological processes such as inflammation and tumor cell migration and invasion regulation. The purpose of this study was to construct S100A9 gene-edited mice by using CRISPR/Cas9 technology, thereby providing an animal model for exploring the biological functions of this gene. According to the S100A9 gene sequence, the single-stranded small guide RNA (sgRNA) targeting exons 2 and 3 was transcribed in vitro, and a mixture of Cas9 mRNA and candidate sgRNA was injected into mouse fertilized eggs by microinjection. Early embryos were obtained and transferred to surrogate mice, and F0 mice were obtained and identified by PCR identification and gene sequencing. F0 mice were further mated with wild-type C57BL/6 mice to obtain F1 heterozygous mice, and then homozygous offspring were obtained through F1 mice self-crossing. Real-time PCR, Western blot and immunohistochemistry (IHC) were used to verify the expression and distribution of S100A9. In order to observe the pathological changes of mouse lung tissue using HE staining, an allergic asthma model was induced by ovalbumin from chicken egg white (OVA). The results showed that the 2 492 bp of exons 2, 3 of the S100A9 gene was successfully knocked out, and S100A9-/- mice with stable inheritance were obtained. Furthermore, it was found that S100A9 gene was highly expressed in the lung and spleen of wild-type mice. The expression of S100A9 mRNA and protein was not detected in the lung and spleen of S100A9-/- mice. However, compared with wild-type mice, the lungs of S100A9-/- mice showed a significantly worse inflammatory phenotype, and the proportion of eosinophils in bronchoalveolar lavage fluid (BALF) was significantly increased in response to the treatment of OVA. These results suggest we have successfully constructed a new strain of S100A9-/- mice, and preliminarily confirmed that the lack of S100A9 function can aggravate airway inflammation in asthmatic mice, providing a new mouse model for further study of S100A9 gene function.

Application of fluorescein combined with methylene blue in sentinel lymph node biopsy of breast cancer.

Sentinel lymph node biopsy (SLNB) for axillary lymph node staging in early breast cancer has been widely recognized. The combination of radio-colloids and dye method is the best method recognized. The reagents and equipment required in the process of the combined method are complex and expensive, so there are certain restrictions in the use of primary medical institutions. As a new tracer, fluorescent tracer technology has attracted much attention. We aimed to evaluate the feasibility and safety of fluorescein for SLNB in breast cancer. In this study, a total of 123 patients with breast cancer were divided into group A (n = 67) and group B (n = 56). The efficacy of Indocyanine green (ICG) combined with methylene blue (group A) and fluorescein combined with methylene blue (group B) in SLNB of breast cancer was compared, complications were observed at the same time. No local or systemic reactions were observed in the two groups. In group A, Sentinel lymph nodes of breast cancer were detected in 63 patients, with a detection rate of 94.0% (63/67), a false-negative rate of 7.5% (4/53). In group B, Sentinel lymph nodes of breast cancer were detected in 52 patients, with a detection rate of 92.9% (52/56), a false-negative rate of 7.5% (3/40). There was no significant difference in biopsy results between the two groups. This prospective clinical study suggests that SLNB using fluorescein and ultraviolet LED light is feasible in breast cancer patients. No adverse reactions were observed in this study, but larger studies are needed to properly assess the adverse reaction rate.

Construction of an immune-related LncRNA signature with prognostic significance for bladder cancer.

Bladder cancer (BLCA) is one of the most common urological cancer with increasing cases and deaths every year. In the present study, we aim to construct an immune-related prognostic lncRNA signature (IRPLS) in bladder cancer (BLCA) patients and explore its immunogenomic implications in pan-cancers. First, the immune-related differentially expressed lncRNAs (IRDELs) were identified by 'limma' R package and the score of IRPLS in every patient were evaluated by Cox regression. The dysregulation of IRDELs expression between cancer and para-cancer normal tissues was validated through RT-qPCR. Then, we further explore the biological functions of a novel lncRNA from IRPLS, RP11-89 in BLCA using CCK8 assay, Transwell assay and Apoptosis analysis, which indicated that RP11-89 was able to promote cell proliferation and invasive capacity while inhibits cell apoptosis in BLCA. In addition, we performed bioinformatic methods and RIP to investigate and validate the RP11-89/miR-27a-3p/PPARγ pathway in order to explore the mechanism. Next, CIBERSORT and ESTIMATE algorithm were used to evaluate abundance of tumour-infiltrating immune cells and scores of tumour environment elements in BLCA with different level of IRPLS risk scores. Finally, multiple bioinformatic methods were performed to show us the immune landscape of these four lncRNAs for pan-cancers. In conclusion, this study first constructed an immune-related prognostic lncRNA signature, which consists of RP11-89, PSORS1C3, LINC02672 and MIR100HG and might shed lights on novel targets for individualized immunotherapy for BLCA patients.

Construction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real-world data.

This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large-scale multiomics analysis and real-world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis-related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI's GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P < .001), TFRC (P < .001), TSC1 (P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki-67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients' prognosis using proteomic data, gene expression data and real-world data and this prognostic model showed stronger predictive value than other biomarkers (Ki-67, Beta-catenin, etc) in multi-cohorts.

High Expression of CD39 is Associated with Poor Prognosis and Immune Infiltrates in Clear Cell Renal Cell Carcinoma.

INTRODUCTION: The cell-surface ectonucleotidase CD39 is a key molecule of the immunosuppressive adenosine pathway within the tumor microenvironment. However, the relationship between CD39 and clear cell renal cell carcinoma (ccRCC) is rarely reported and still remains unclear. METHODS: CD39 expression was first analyzed using the Oncomine and the Tumor IMmune Estimation Resource (TIMER) databases, and then examined in ccRCC patients (n=367) who had undergone radical nephrectomy using immunohistochemistry (IHC) and real-time quantitative PCR analysis (qPCR). The prognosis value of CD39 in ccRCC was evaluated by Cox proportional hazards analysis. Functional and gene set enrichment analysis (GSEA) was performed using transcriptomic data of ccRCC from TCGA. Correlation analysis between CD39 and tumor-infiltrating lymphocytes (TILs) was performed using the TISIDB database. The impact of CD39 on immune checkpoint therapy (ICT) was evaluated by two public cohorts. RESULTS: CD39 mRNA and protein expression was upregulated in tumor tissues from ccRCC patients and aberrant expression of CD39 was associated with advanced tumor stage and poor prognosis in ccRCC patients. EMT, IL-2/STAT5, inflammatory response, interferon gamma and KRAS hallmark gene sets were identified as CD39-related signaling pathway. The expression level of CD39 was significantly and positively correlated with high abundance of the regulatory TILs including NK cells, macrophages, Th cells and Treg cells. CD39 was correlated with expression of several immune checkpoints and higher CD39 expression was associated with better OS of ccRCC patients who received ICT. CONCLUSION: CD39 is a powerful prognostic marker of ccRCC patients. Increased tumor expression of CD39 mRNA is significantly correlated with infiltrating levels of TILs, and better efficacy of ICT to ccRCC. CD39 could be a novel therapeutic target for ccRCC.

Identification, Validation, and Functional Annotations of Genome-Wide Profile Variation between Melanocytic Nevus and Malignant Melanoma.

Cutaneous melanoma (CM) is known as an aggressive malignant cancer; some of which are directly derived from melanocytic nevi, which have been attracting growing attention from the last decades. This study focused on comprehensive identification, validation, and functional annotations of prognostic differentially expressed genes (DEGs) between melanocytic nevus and malignant melanoma in genome-wide profiles. DEGs were obtained using three chip datasets from GEO database to identify after standardization annotation. A total of 73 DEGs were identified as possible candidate prognostic biomarkers between melanocytic nevus and malignant melanoma. In addition, survival curves indicated that six hub genes, including FABP5, IVL, KRT6A, KRT15, KRT16, and TIMP2, were significant prognostic signatures for CM and of significant value to predict transformation from nevi to melanoma. Furthermore, immunohistochemistry staining was performed to validate differential expression levels and prognostic implications of six hub genes between CM tissue and nevus tissues from the First Affiliated Hospital of Soochow University cohort. It suggested that significantly elevated FABP5, IVL, KRT6A, KRT15, KRT16, and TIMP2 proteins expressed in the CM than in the nevus tissues. Functional enrichment and significant pathways of the six significant hub genes indicated that the mostly involved hallmarks include the P53 pathway, K-ras signaling, estrogen response late, and estrogen response early. In summary, this study identified significant DEGs participating in the process of malignant transformation from nevus to melanoma tissues based on comprehensive genomic profiles. Transcription profiles of FABP5, IVL, KRT6A, KRT15, KRT16, and TIMP2 provided clues of prognostic implications, which might help us evaluate malignant potential of nevus and underlying carcinogenesis progress from melanocytic nevus to melanoma.

Identification and validation of novel metastasis-related signatures of clear cell renal cell carcinoma using gene expression databases.

Patients with clear cell renal cell carcinoma (ccRCC) typically face aggressive disease progression when metastasis occurs. Here, we screened and identified differentially expressed genes in three microarray datasets from the Gene Expression Omnibus database. We identified 112 differentially expressed genes with functional enrichment as candidate prognostic biomarkers. Lasso Cox regression suggested 10 significant oncogenic hub genes involved in earlier recurrence and poor prognosis of ccRCC. Receiver operating characteristic curves validated the specificity and sensitivity of the Cox regression penalty used to predict prognosis. The area under the curve indexes of the integrated genes scores were 0.758 and 0.772 for overall and disease-free survival, respectively. The prognostic values of ADAMTS9, C1S, DPYSL3, H2AFX, MINA, PLOD2, RUNX1, SLC19A1, TPX2, and TRIB3 were validated through an analysis of 10 hub genes in 380 patients with ccRCC from a real-world cohort. The expression levels of were of high prognostic value for predicting metastatic potential. These findings will likely significantly contribute to our understanding of the underlying mechanisms of ccRCC, which will enhance efforts to optimize therapy.

Carbonic Anhydrase 4 serves as a Clinicopathological Biomarker for Outcomes and Immune Infiltration in Renal Cell Carcinoma, Lower Grade Glioma, Lung Adenocarcinoma and Uveal Melanoma.

Background: Carbonic anhydrase 4 (CA4) maintains homeostasis of carbon dioxide and bicarbonate. It is suggested to be a potential prognostic biomarker, while the correlations between CA4 and different cancers are indistinct. Methods: Differential mRNA expression of CA4 among different cancers and corresponding normal tissues was compared based on datasets on the Cancer Genome Atlas (TCGA) platforms. Then, survival analysis was performed using Tumor-immune system interactionsplatform and TCGA cohort on the basis of distinct comparison expression of CA4 in five kinds of tumors. In addition, molecular penal analysis and functional annotations of CA4-related genes was elaborated. The correlation between CA4 mRNA expression and tumor immune microenvironment were analyzed in detail. Results: Compared with adjacent normal tissues, CA4 mRNA expressions were found significantly lower in various tumors. Moreover, decreased expression of CA4 was significantly related to worse overall survival (OS) and progression-free survival (PFS) in kidney renal clear cell carcinoma (KIRC), brain lower grade glioma (LGG), lung adenocarcinoma (LUAD) and uveal melanoma (UVM), and worse OS of prostate adenocarcinoma (PRAD) (p<0.05). Cox regression analyses indicated that CA4 was a significant prognostic biomarker in KIRC, LGG, LUAD and UVM. Moreover, CA4 showed markedly relationship with tumor immune environment and diverse immune infiltration signatures in KIRC, LGG, LUAD and UVM. Conclusions: Our study revealed that CA4 was a potential biomarker for aggressive progression and poor prognosis in KIRC, LGG, LUAD, PRAD and UVM, correlated with immune infiltration in various tumor environments. These results suggested that CA4 possibly served as a promising prognostic and immune infiltration biomarker in many cancers.