Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening

Structure-based design, synthesis and biological evaluation of a novel d-amino acid-containing peptide inhibitor by blocking the RAD51-BRCA2 interaction for the treatment of kidney cancer

RAD51 is involved in the homologous recombination of DNA double-strand breaks by being directed to single-stranded DNA with the assistance of the BRCA2 protein. Therefore, blocking the interaction between RAD51 and BRCA2 is considered to be a potential anticancer therapy. Currently, D-peptide inhibitors are widely recognized for their biological stability, low immunogenicity and target specificity. Here, we have identified a novel, potent and biostable d-amino acid-containing peptide inhibitor (RB-1) that blocks the RAD51-BRCA2 interaction through an integrated virtual screening protocol. MST and FP experiments showed that RB-1 had excellent binding affinity for RAD51. MD simulation confirmed the stable binding of RB-1 to the active binding site of RAD51. Furthermore, RB-1 exhibited significant antiproliferative activity on a panel of kidney cancer cell lines and less toxicity to normal cells, suggesting its potential therapeutic effects. Meanwhile, RB-1 exerted antitumor effects by inhibiting HR repair. In addition, RB-1 had good biological stability in mouse serum, highlighting its potential for in vivo activity. In vivo studies showed that RB-1 can effectively suppress tumor growth in mice without causing serious systemic side effects. In conclusion, these results suggest that d-amino acid-containing peptide RB-1 is a promising antitumor agent for kidney cancer and merits further investigation.

Identification of gasdermin B function in the progression of renal clear cell carcinoma by a pan-cancer analysis

The Gasdermin (GSDM) protein family is critically involved in pyroptosis, which participates in the onset and progression of human malignancies. The exact role and impact of the GSDM family genes in various malignancies, particularly renal clear cell carcinoma (KIRC), is still uncertain. The present results indicated GSDMB gene expression significantly upregulated in individuals with KIRC, whose diagnostic effectiveness was confirmed through ROC analysis. Kaplan-Meier analysis also revealed KIRC patients had poor survival prognosis. The high expression of GSDMB served as an independent risk factor for overall survival (OS) in KIRC, based on multivariate cox analysis for confirmation. A nomogram based on GSDMB expression and clinical characteristics displayed remarkable diagnostic effectiveness for KIRC. Collectively, these findings may shed light on functions of GSDM family genes in tumor progression and offer new directions for future research into their potential as therapeutic targets in various types of tumors. Furthermore, the outcomes of this research highlighted that the prediction of treatment responses in KIRC patients may get improved through in-depth exploration into the impact of GSDMB expression on individuals with KIRC patients.

Bioinformatic Approaches for the Identification of Novel Tumor Suppressor Genes and Cancer Pathways in Renal Clear Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC, KIRC) is the most prevalent subtype of RCC, and even with different available therapies, the average progression-free survival is worse. Therefore, the identification of new molecular targets could be helpful for its therapeutic purposes.

Materials and Methods

We used the Cancer Genome Atlas to perform bioinformatic analyses for genes with possible tumor suppressor roles in KIRC.

Objective

This research aims to identify new prognostic biomarkers and potential therapeutic targets for this type of cancer.

Results

We identified 14 down-regulated genes in KIRC that had not previously been studied or poorly studied, with the majority of them impacted by increased promoter methylation. Eight genes showed shorter overall survival and worse prognosis, indicating their function as tumor suppressors, and six genes revealed good prognosis. From the 8 genes, C7ORF41 and CTXN3 revealed only downregulation in most cancers, proposing them as highly potential tumor suppressors. Among these 8 genes, the function of CTXN3 in cancers is unknown. Moreover, we identified the CWH43 gene as the major signature of KIRC. In addition, we found different genes as signatures of KIRC tumor stages and grades.

Conclusions

Our results may shed light on identifying KIRC pathogenesis and developing effective therapeutic targets for renal cancers, mainly KIRC.

Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer

Aberrant alternative splicing (AS) is a prominent hallmark of cancer. AS can perturb protein-protein interactions (PPIs) by adding or removing interface regions encoded by individual exons. Identifying prognostic exon-exon interactions (EEIs) from PPI interfaces can help discover AS-affected cancer-driving PPIs that can serve as potential drug targets. Here, we assessed the prognostic significance of EEIs across 15 cancer types by integrating RNA-seq data with three-dimensional (3D) structures of protein complexes. By analyzing the resulting EEI network we identified patient-specific perturbed EEIs (i.e., EEIs present in healthy samples but absent from the paired cancer samples or vice versa) that were significantly associated with survival. We provide the first evidence that EEIs can be used as prognostic biomarkers for cancer patient survival. Our findings provide mechanistic insights into AS-affected PPI interfaces. Given the ongoing expansion of available RNA-seq data and the number of 3D structurally-resolved (or confidently predicted) protein complexes, our computational framework will help accelerate the discovery of clinically important cancer-promoting AS events.

Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts

Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling. However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. Our approach selected predictors regardless of their pathway membership and optimally combined their RPPA measurements into a weighted risk score. Performance was assessed and compared to that of the published signatures using two unbiased approaches: 1) 10 iterations of threefold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan-Meier method) between predictor-defined high and low risk groups; and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Here, we demonstrate strong stratification of 445 renal clear cell carcinoma tumors from The Cancer Genome Atlas (TCGA) into high and low risk groups using LASSO regression on RPPA data. Median cross-validated difference in 5-year overall survival was 32.8%, compared to 25.2% using a published receptor tyrosine kinase (RTK) prognostic signature (median hazard ratios of 3.3 and 2.4, respectively). Applicability and performance of our approach was demonstrated in three additional TCGA cohorts: ovarian serous cystadenocarcinoma (OVCA), sarcoma (SARC), and cutaneous melanoma (SKCM). The data-driven LASSO-based approach is versatile and well-suited for discovery of new protein/disease associations.

The Prediction of a 3-Protein-Based Model on the Prognosis of Head and Neck Squamous Cell Carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the commonest malignant tumors. Using high-throughput genomic methods, RNA-based diagnostic and prognostic models for HNSCC with potential clinical value have been developed. However, the clinical utility and reproducibility of these models are uncertain. Because the complex regulatory processes occurring after mRNA is transcribed, the abundance of proteins in a cell can never be fully predicted or explained by their corresponding mRNA expression. We aimed to assume and verify a novel protein signature for checking the HNSCC patients' prognosis.

Methods

The functional proteomic data of 332 HNSCC cases were collected from The Cancer Proteome Atlas (TCPA), and the related follow-up and clinical data were acquired from The Cancer Genome Atlas (TCGA). This study adopted multivariate and univariate Cox regression analysis, Akaike Information Criterion, receiver operating characteristic (ROC) analysis, and Kaplan-Meier method.

Results

Patients' clinical features in both sets were comparable (all, P > 0.05). The area under the ROC curve (AUC) for the 3-protein signature (X4EBP1_pT37T46, HER3_pY1289, and NF2) in the test set was 0.655 and in the combined cohort (all 332 patients combined) was 0.699. In addition, the 3-protein signature exhibited better predictive value for the survival of HNSCC patients as in comparison with conventional clinical factors like age, gender, tumor stage, and smoking history (TNM stage).

Conclusion

The 3-protein signature developed in this study exhibits good performance in predicting the overall survival of with HNSCC patients. The 3-protein signature exhibited better predictive value for survival than conventional clinical factors just like gender, TNM stage, smoking history, and age.

A pan-cancer analysis of GINS complex subunit 4 to identify its potential role as a biomarker in multiple human cancers

This study was initiated to explore the expression variation, clinical significance, and biological importance of the GINS complex subunit 4 (GINS4) in different human cancers as a shared biomarker via pan-cancer analysis through different platforms including UALCAN, Kaplan Meier (KM) plotter, TNMplot, GENT2, GEPIA, DriverDBv3, Human Protein Atlas (HPA), MEXPRESS, cBioportal, STRING, DAVID, MuTarge, Enrichr, TIMER, and CTD. Our findings have verified the up-regulation of GINS4 in 24 major subtypes of human cancers, and its overexpression was found to be substantially associated with poor overall survival (OS), relapse-free survival (RFs), and metastasis in ESCA, KIRC, LIHC, LUAD, and UCEC. This suggested that GINS4 plays a significant role in the development and progression of these five cancers. Furthermore, we noticed that GINS4 is also overexpressed in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics. Enrichment analysis revealed the involvement of GINS4 associated genes in a variety of diverse GO and KEGG terms. We also explored few significant correlations between GINS4 expression and promoter methylation, genetic alterations, CNVs, other mutant genes, tumor purity, and immune cells infiltration. In conclusion, our results elucidated that GINS4 can serve as a shared diagnostic, prognostic biomarker, and a potential therapeutic target in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics.

Playing the Devil's Advocate: Should We Give a Second Chance to mTOR Inhibition in Renal Clear Cell Carcinoma? - ie Strategies to Revert Resistance to mTOR Inhibitors

In the last decade, the inhibition of the mechanistic target of Rapamycin (mTOR) in renal clear cell carcinoma (RCC) has disappointed the clinician's expectations. Many clinical trials highlighted the low efficacy and unmanageable safety profile of first-generation mTOR inhibitors (Rapalogs), thus limiting their use in the clinical practice only to those patients who already failed several therapy lines. In this review, we analyze the major resistance mechanisms that undermine the efficacy of this class of drugs. Moreover, we describe some of the possible strategies to overcome the mechanisms of resistance and their clinical experimentation, with particular focus on novel mTOR inhibitors and the combinations of mTOR inhibitors and other anti-cancer drugs.

Bioinformatics analysis reveals biomarkers with cancer stem cell characteristics in kidney renal clear cell carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is a renal cortical tumor. KIRC is the most common subtype of kidney cancer, accounting for 70%-80% of kidney cancer. Early identification of the risk of KIRC patients can facilitate more accurate clinical treatment, but there is a lack of effective prognostic markers. We aimed to identify new prognostic biomarkers for KIRC on the basis of the cancer stem cell (CSC) theory.

Methods

RNA-sequencing (RNA-seq) data and related clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA) was used to identify significant modules and hub genes, and predictive hub genes were used to construct prognostic characteristics.

Results

The messenger RNA expression-based stemness index (mRNAsi) in tumor tissues of patients in the TCGA database is higher than that of the corresponding normal tissues. In addition, some clinical features and results are highly correlated with mRNAsi. WGCNA found that the green module is the most prominent module associated with mRNAsi; the genes in the green module are mainly concentration in Notch binding, endothelial cell development, Notch signaling pathway, and Rap 1 signaling pathway. A protein-protein interaction (PPI) network showed that the top 10 central genes were significantly associated with the transcriptional level. Moreover, the 10 hub genes were up-regulated in KIRC. Regarding survival analysis, the nomogram of the prognostic markers of the seven pivotal genes showed a higher predictive value. The classical receiver operating characteristic (ROC) curve analysis showed that risk score biomarkers had the highest accuracy and specificity with an area under the curve (AUC) value of 0.701.

Conclusions

mRNAsi-related genes may be good prognostic biomarkers for KIRC.

Prognostic significance and tumor-immune infiltration of mTOR in clear cell renal cell carcinoma

Mammalian target of rapamycin (mTOR), a serine/threonine kinase involved in cell proliferation, survival, metabolism and immunity, was reportedly activated in various cancers. However, the clinical role of mTOR in renal cell carcinoma (RCC) is controversial. Here we detected the expression and prognosis of total mTOR and phosphorylated mTOR (p-mTOR) in clear cell RCC (ccRCC) patients, and explored the interactions between mTOR and immune infiltrates in ccRCC. The protein level of mTOR and p-mTOR was determined by western blotting (WB), and their expression was evaluated in 145 ccRCC and 13 non-tumor specimens by immunohistochemistry (IHC). The relationship to immune infiltration of mTOR was further investigated using TIMER and TISIDB databases, respectively. WB demonstrated the ratio of p-mTOR to mTOR was higher in ccRCC than adjacent specimens (n = 3), and IHC analysis elucidated that p-mTOR expression was positively correlated with tumor size, stage and metastasis status, and negatively correlated with cancer-specific survival (CSS). In univariate analysis, high grade, large tumor, advanced stage, metastasis, and high p-mTOR expression were recognized as prognostic factors of poorer CSS, and multivariate survival analysis elucidated that tumor stage, p-mTOR and metastasis were of prognostic value for CSS in ccRCC patients. Further TIMER and TISIDB analyses uncovered that mTOR gene expression was significantly associated with numerous immune cells and immunoinhibitors in patients with ccRCC. Collectively, these findings revealed p-mTOR was identified as an independent predictor of poor survival, and mTOR was associated with tumor immune infiltrates in ccRCC patients, which validated mTOR could be implicated in the initiation and progression of ccRCC.

The Ambivalent Role of miRNAs in Carcinogenesis: Involvement in Renal Cell Carcinoma and Their Clinical Applications

The analysis of microRNA (miRNAs), small, non-coding endogenous RNA, plays a crucial role in oncology. These short regulatory sequences, acting on thousands of messenger RNAs (mRNAs), modulate gene expression at the transcriptional and post-transcriptional level leading to translational repression or degradation of target molecules. Although their function is required for several physiological processes, such as proliferation, apoptosis and cell differentiation, miRNAs are also responsible for development and/or progression of several cancers, since they may interact with classical tumor pathways. In this review, we highlight recent advances in deregulated miRNAs in cancer focusing on renal cell carcinoma (RCC) and provide an overview of the potential use of miRNA in their clinical settings, such as diagnostic and prognostic markers.

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.

Materials and Methods

Protein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.

Results

From the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.

Conclusion

We constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

Expression and prognostic significance of fatty acid synthase in clear cell renal cell carcinoma

Fatty acid synthase (FASN), a key enzyme essential for fatty acid (FA) synthesis, was reportedly implicated in the initiation and progression of various cancers. However, the clinical significance of FASN in renal cell carcinoma (RCC) has not been fully elucidated yet. Here we compare the expression profile and evaluate the prognostic significance of FASN in clear cell RCC (ccRCC) patients. FASN expression was examined in 3 pairs ccRCC and their adjacent nontumor tissues by western blotting (WB) analysis, and its expression was assessed in 145 ccRCC and 13 nontumor tissues by immunohistochemistry (IHC) analysis with tissue microarrays (TMAs). The prognosis of FASN was further investigated in large-scale database using LinkedOmics (n = 537) and The Cancer Protein Atlas (TCPA, n = 445), respectively. WB detected higher FASN expression in ccRCC than normal tissues, then IHC analysis revealed that FASN expression was positively associated with histological grade, pathological stage, tumor size and metastasis status, and negatively associated with cancer-specific survival (CSS). Univariate survival analysis demonstrated that high grade, advanced stage, large tumor, metastasis, and high FASN expression were significantly associated with a shorter CSS, and multivariate analysis revealed tumor grade, stage, metastasis and FASN were identified as independent predictors for CSS in patients with ccRCC. Further LinkedOmics and TCPA analyses confirmed that high FASN expression was correlated with a poorer overall survival (OS) of ccRCC. Collectively, these findings demonstrated FASN could be a poor prognostic factor in ccRCC patients, which indicated that FA synthesis might be implicated in the tumorigenesis and progression of ccRCC.

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is a potentially fatal urogenital disease. It is a major cause of renal cell carcinoma and is often associated with late diagnosis and poor treatment outcomes. More evidence is emerging that genetic models can be used to predict the prognosis of KIRC. This study aimed to develop a model for predicting the overall survival of KIRC patients.

Results

We identified 333 differentially expressed genes (DEGs) between KIRC and normal tissues from the Gene Expression Omnibus (GEO) database. We randomly divided 591 cases from The Cancer Genome Atlas (TCGA) into training and internal testing sets. In the training set, we used univariate Cox regression analysis to retrieve the survival-related DEGs and futher used multivariate Cox regression with the LASSO penalty to identify potential prognostic genes. A seven-gene signature was identified that included APOLD1, C9orf66, G6PC, PPP1R1A, CNN1G, TIMP1, and TUBB2B. The seven-gene signature was evaluated in the training set, internal testing set, and external validation using data from the ICGC database. The Kaplan-Meier analysis showed that the high risk group had a significantly shorter overall survival time than the low risk group in the training, testing, and ICGC datasets. ROC analysis showed that the model had a high performance with an AUC of 0.738 in the training set, 0.706 in the internal testing set, and 0.656 in the ICGC external validation set.

Conclusion

Our findings show that a seven-gene signature can serve as an independent biomarker for predicting prognosis in KIRC patients.

Proteomic approaches for characterizing renal cell carcinoma

Renal cell carcinoma is among the top 15 most commonly diagnosed cancers worldwide, comprising multiple sub-histologies with distinct genomic, proteomic, and clinicopathological features. Proteomic methodologies enable the detection and quantitation of protein profiles associated with the disease state and have been explored to delineate the dysregulated cellular processes associated with renal cell carcinoma. In this review we highlight the reports that employed proteomic technologies to characterize tissue, blood, and urine samples obtained from renal cell carcinoma patients. We describe the proteomic approaches utilized and relate the results of studies in the larger context of renal cell carcinoma biology. Moreover, we discuss some unmet clinical needs and how emerging proteomic approaches can seek to address them. There has been significant progress to characterize the molecular features of renal cell carcinoma; however, despite the large-scale studies that have characterized the genomic and transcriptomic profiles, curative treatments are still elusive. Proteomics facilitates a direct evaluation of the functional modules that drive pathobiology, and the resulting protein profiles would have applications in diagnostics, patient stratification, and identification of novel therapeutic interventions.

Prognostic significance of PI3K/AKT/ mTOR signaling pathway members in clear cell renal cell carcinoma

Background

Renal cell carcinoma (RCC) is a fatal disease, in which the PI3K/AKT/mTOR signaling pathway serves an important role in the tumorigenesis. Previous studies have reported the prognostic significance of PI3K/AKT/mTOR signaling pathway members in RCC; however, there is insufficient evidence to date to confirm this. Thus, the present study aimed to systematically investigate the prognostic roles of multiple PI3K/AKT/mTOR signaling proteins in clear cell RCC (ccRCC) using online large-scale databases.

Methods

The mRNA expression profiles of PI3K/AKT/mTOR signaling pathway proteins PTEN, PIK3CA, PIK3CB, PIK3CD, PIK3CG, AKT1, AKT2, AKT3 and mTOR were investigated using the Gene Expression Profiling Interactive Analysis (GEPIA) and Oncomine databases, and the protein expression levels of PI3K, AKT and mTOR were detected using western blotting (WB) analysis. In addition, the correlation between mRNA or protein expression levels and the prognostic significance was analyzed using the Kaplan-Meier (K-M) plotter (n = 530), the Human Protein Atlas (HPA; n = 528) and The Cancer Protein Atlas (TCPA; n = 445) databases.

Results

The GEPIA revealed that the mRNA expression of major PI3K/AKT/mTOR pathway members, including PTEN, PIK3CA, PIK3CB, AKT1, AKT2 and AKT3, were negatively correlated with ccRCC stages (P < 0.05), though most of their mRNA and protein expression levels were notsignificantly different between ccRCC and normal tissues using GEPIA, Oncomine and WB analyses (P < 0.05). Meanwhile, using the K-M plotter and HPA prognostic analysis, it was found that the mRNA expression levels of the majority of the PI3K/AKT/mTOR signaling pathway members, including PTEN, PIK3CA, PIK3CB, PIK3CG, AKT3 and mTOR were positively correlated with overall survival (OS), whereas PIK3CD mRNA expression was negatively correlated with OS (P < 0.05). Furthermore, TCPA prognostic analysis observed that several of the key molecules of the PI3K/AKT/mTOR signaling pathway [PTEN, p-AKT (S473) and p-mTOR (S2448)] were also positively correlated with OS in patients with ccRCC (P < 0.05). In conclusion, the present study suggested that several members of the PI3K/AKT/mTOR signaling pathway, especially PTEN, may be favorable prognostic factors in ccRCC, which indicated that the PI3K/AKT/mTOR signaling pathway may be implicated in ccRCC initiation and progression.

Over-Expression and Prognostic Significance of HHLA2, a New Immune Checkpoint Molecule, in Human Clear Cell Renal Cell Carcinoma

HHLA2, a newly identified B7 family member, regulates T cell functions. However, the expression and prognostic value of HHLA2 in solid tumors is ill defined. This study aimed to reveal the expression landscape of HHLA2 in various solid tumors, and to evaluate its prognostic value in kidney clear cell carcinoma (KIRC). Using The Cancer Genome Atlas (TCGA) database, we investigated the expression pattern of HHLA2 across 22 types of cancer. HHLA2 and CD8 protein expression was determined via immunohistochemistry (IHC). KIRC-specific findings were further analyzed with R software and the prognostic value was validated on tissue microarrays. HHLA2 was widely expressed in cancers at both the mRNA and protein levels. Among all tested tumors, KIRC showed the highest transcript level of HHLA2, and HHLA2 levels were significantly higher in tumor tissues than in matched normal samples, as evidenced by both TCGA and IHC data. HHLA2 was also positively correlated with survival rates in KIRC based on TCGA and clinical data. Receiver operating characteristic curves data showed the prognostic value of HHLA2 for patients with KIRC in TCGA. Moreover, HHLA2 was positively correlated with immune-related genes, while HHLA2 and CD8 expression exhibited a consistent trend in KIRC tumor samples. In conclusion, HHLA2 is highly expressed in KIRC and predicts a favorable survival outcome, highlighting that it may work as a potential target for KIRC therapy.

Utility of a Reverse Phase Protein Array to Evaluate Multiple Biomarkers in Diffuse Large B-Cell Lymphoma

Purpose

Diffuse large B‐cell lymphoma (DLBCL), the most common non‐Hodgkin lymphoma, is a heterogeneous lymphoma with different clinical manifestations and molecular alterations, and several markers are currently being measured routinely for its diagnosis, subtyping, or prognostication by immunohistochemistry (IHC). Here, the utility of a reverse‐phase‐protein‐array (RPPA) as a novel supportive tool to measure multiple biomarkers for DLBCL diagnosis is validated.

Experimental design

The expression of seven markers (CD5, CD10, BCL2, BCL6, MUM1, Ki‐67, and C‐MYC) is analyzed by RPPA and IHC using 37 DLBCL tissues, and the correlation between the two methods is determined. To normalize tumor content ratio in the tissues, the raw RPPA values of each marker are adjusted by that of CD20 or PAX‐5.

Results

The CD20‐adjusted data for CD5, MUM1, BCL2, Ki‐67, and C‐MYC has better correlation with IHC results than PAX‐5‐adjusted data. Receiver operating characteristic (ROC) analysis reveals that CD5, MUM1, BCL2, and C‐MYC exhibit a better sensitivity and specificity >0.750. Furthermore, the CD20‐adjusted C‐MYC value strongly correlates with that of IHC, and has a particularly high specificity (0.882).

Conclusions and clinical relevance

Although further investigation using a large number of DLBCL specimens needs to be conducted, these results suggest that RPPA could be applicable as a supportive tool for determining lymphoma prognosis.

The mRNA Expression Signature and Prognostic Analysis of Multiple Fatty Acid Metabolic Enzymes in Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a metabolic disease, and accumulating evidences indicate significant alterations in the cellular metabolism, especial aerobic glycolysis and glutamine metabolism, in RCC. However, fatty acid (FA) metabolism has received less attention, and the mRNA expression pattern and prognostic role of FA metabolic enzymes in clear cell RCC (ccRCC) have not been carefully examined. In the current study, we first investigated the mRNA expression profiles of multiple FA metabolic enzymes, i.e., ACLY, ACC, FASN, SCD, CPT1A, HADHA, HADHB, and ACAT1, in 42 ccRCC and 33 normal kidney tissues using the Oncomine database, validated their mRNA expression profiles using GEPIA resource, then evaluated and validated the prognostic significance of these metabolic enzymes in 530 ccRCC patients using Kaplan-Meier plotter and GEPIA analyses respectively. The Oncomine and GEPIA confirmed higher ACLY, SCD, and lower ACAT1 mRNA expression in ccRCC than normal tissues (P<0.05). And further prognostic analysis displayed that overexpression of the some FA anabolic enzymes (FASN) was correlated to poor overall survival (OS), while overexpression of the FA catabolic enzymes (CPT1A, HADHA, HADHB, and ACAT1) was correlated to favorable OS in ccRCC patients. In conclusion, multiple FA metabolic enzymes, such as FASN, HADHA, and ACAT1, were potential prognostic markers of ccRCC, which implied alterations in FA metabolism might be involved in ccRCC tumorigenesis and progression.

TissGDB: tissue-specific gene database in cancer

Tissue-specific gene expression is critical in understanding biological processes, physiological conditions, and disease. The identification and appropriate use of tissue-specific genes (TissGenes) will provide important insights into disease mechanisms and organ-specific therapeutic targets. To better understand the tissue-specific features for each cancer type and to advance the discovery of clinically relevant genes or mutations, we built TissGDB (Tissue specific Gene DataBase in cancer) available at http://zhaobioinfo.org/TissGDB. We collected and curated 2461 tissue specific genes (TissGenes) across 22 tissue types that matched the 28 cancer types of The Cancer Genome Atlas (TCGA) from three representative tissue-specific gene expression resources: The Human Protein Atlas (HPA), Tissue-specific Gene Expression and Regulation (TiGER), and Genotype-Tissue Expression (GTEx). For these 2461 TissGenes, we performed gene expression, somatic mutation, and prognostic marker-based analyses across 28 cancer types using TCGA data. Our analyses identified hundreds of TissGenes, including genes that universally kept or lost tissue-specific gene expression, with other features: cancer type-specific isoform expression, fusion with oncogenes or tumor suppressor genes, and markers for protective or risk prognosis. TissGDB provides seven categories of annotations: TissGeneSummary, TissGeneExp, TissGene-miRNA, TissGeneMut, TissGeneNet, TissGeneProg, TissGeneClin.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: summary and innovation in genomics

In this editorial, we first summarize the 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) that was held on December 8–10, 2016 in Houston, Texas, USA, and then briefly introduce the ten research articles included in this supplement issue. ICIBM 2016 included four workshops or tutorials, four keynote lectures, four conference invited talks, eight concurrent scientific sessions and a poster session for 53 accepted abstracts, covering current topics in bioinformatics, systems biology, intelligent computing, and biomedical informatics. Through our call for papers, a total of 77 original manuscripts were submitted to ICIBM 2016. After peer review, 11 articles were selected in this special issue, covering topics such as single cell RNA-seq analysis method, genome sequence and variation analysis, bioinformatics method for vaccine development, and cancer genomics.