Transcriptome-proteome integration of archival human renal cell carcinoma biopsies enables identification of molecular mechanisms

Bile Acid Metabolism Mediates Cholesterol Homeostasis and Promotes Tumorigenesis in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) incidence has risen steadily over the last decade. Elevated lipid uptake and storage is required for ccRCC cell viability. As stored cholesterol is the most abundant component in ccRCC intracellular lipid droplets, it may also play an important role in ccRCC cellular homeostasis. In support of this hypothesis, ccRCC cells acquire exogenous cholesterol through the high-density lipoprotein receptor SCARB1, inhibition or suppression of which induces apoptosis. Here, we showed that elevated expression of 3 beta-hydroxy steroid dehydrogenase type 7 (HSD3B7), which metabolizes cholesterol-derived oxysterols in the bile acid biosynthetic pathway, is also essential for ccRCC cell survival. Development of an HSD3B7 enzymatic assay and screening for small-molecule inhibitors uncovered the compound celastrol as a potent HSD3B7 inhibitor with low micromolar activity. Repressing HSD3B7 expression genetically or treating ccRCC cells with celastrol resulted in toxic oxysterol accumulation, impaired proliferation, and increased apoptosis in vitro and in vivo. These data demonstrate that bile acid synthesis regulates cholesterol homeostasis in ccRCC and identifies HSD3B7 as a plausible therapeutic target.

SIGNIFICANCE

The bile acid biosynthetic enzyme HSD3B7 is essential for ccRCC cell survival and can be targeted to induce accumulation of cholesterol-derived oxysterols and apoptotic cell death.

Deciphering Urogenital Cancers through Proteomic Biomarkers: A Systematic Review and Meta-Analysis

Urogenital cancers, which include prostate, bladder, and kidney malignancies, exert a substantial impact on global cancer-related morbidity and mortality. Proteomic biomarkers, emerging as valuable tools, aim to enhance early detection, prognostic accuracy, and the development of personalized therapeutic strategies. This study undertook a comprehensive systematic review and meta-analysis of the existing literature investigating the role and potential of proteomic biomarkers in plasma, tissue, and urine samples in urogenital cancers. Our extensive search across several databases identified 1879 differentially expressed proteins from 37 studies, signifying their potential as unique biomarkers for these cancers. A meta-analysis of the significantly differentially expressed proteins was executed, accentuating the findings through visually intuitive volcano plots. A functional enrichment analysis unveiled their significant involvement in diverse biological processes, including signal transduction, immune response, cell communication, and cell growth. A pathway analysis highlighted the participation of key pathways such as the nectin adhesion pathway, TRAIL signaling pathway, and integrin signaling pathways. These findings not only pave the way for future investigations into early detection and targeted therapeutic approaches but also underscore the fundamental role of proteomics in advancing our understanding of the molecular mechanisms underpinning urogenital cancer pathogenesis. Ultimately, these findings hold remarkable potential to significantly enhance patient care and improve clinical outcomes.

Differential Activation of NRF2 Signaling Pathway in Renal-Cell Carcinoma Caki Cell Lines

Renal-cell carcinoma (RCC) is a heterogeneous disease consisting of several subtypes based on specific genomic profiles and histological and clinical characteristics. The subtype with the highest prevalence is clear-cell RCC (ccRCC), next is papillary RCC (pRCC), and then chromophobe RCC (chRCC). The ccRCC cell lines are further subdivided into prognostic expression-based subtypes ccA or ccB. This heterogeneity necessitates the development, availability, and utilization of cell line models with the correct disease phenotypic characteristics for RCC research. In this study, we focused on characterizing proteomic differences between the Caki-1 and Caki-2 cell lines that are commonly used in ccRCC research. Both cells are primarily defined as human ccRCC cell lines. Caki-1 cell lines are metastatic, harboring wild-type VHL, whereas Caki-2 are considered as the primary ccRCC cell lines expressing wild-type von Hippel–Lindau protein (pVHL). Here, we performed a comprehensive comparative proteomic analysis of Caki-1 and Caki-2 cells using tandem mass-tag reagents together with liquid chromatography mass spectrometry (LC/MS) for the identification and quantitation of proteins in the two cell lines. Differential regulation of a subset of the proteins identified was validated using orthogonal methods including western blot, q-PCR, and immunofluorescence assays. Integrative bioinformatic analysis identifies the activation/inhibition of specific molecular pathways, upstream regulators, and causal networks that are uniquely regulated and associated with the two cell lines and RCC subtypes, and potentially the disease stage. Altogether, we have identified multiple molecular pathways, including NRF2 signaling, which is the most significantly activated pathway in Caki-2 versus Caki-1 cells. Some of the differentially regulated molecules and signaling pathways could serve as potential diagnostic and prognostic biomarkers and therapeutic targets amongst ccRCC subtypes.

Prognostic value of Talin-1 in renal cell carcinoma and its association with B7-H3

METHODS: Talin-1 protein was demonstrated as a potential prognostic marker in renal cell carcinoma (RCC) using bioinformatics analysis. We, therefore, examined the protein expression levels and prognostic significance of Talin-1 with a clinical follow-up in a total of 269 tissue specimens from three important subtypes of RCC and 30 adjacent normal samples using immunohistochemistry. Then, we used combined analysis with B7-H3 to investigate higher prognostic values. RESULTS: The results showed that high membranous and cytoplasmic expression of Talin-1 was significantly associated with advanced nucleolar grade, microvascular invasion, histological tumor necrosis, and invasion to Gerota’s fascia in clear cell RCC (ccRCC). In addition, high membranous and cytoplasmic expression of Talin-1 was found to be associated with significantly poorer disease-specific survival (DSS) and progression-free survival (PFS). Moreover, increased cytoplasmic expression of Talin-1High/B7-H3High compared to the other phenotypes was associated with tumor aggressiveness and progression of the disease, and predicted a worse clinical outcome, which may be an effective biomarker to identify ccRCC patients at high risk of recurrence and metastasis. CONCLUSIONS: Collectively, these observations indicate that Talin-1 is an important molecule involved in the spread and progression of ccRCC when expressed particularly in the cytoplasm and may serve as a novel prognostic biomarker in this subtype. Furthermore, a combined analysis of Talin-1/B7-H3 indicated an effective biomarker to predict the progression of disease and prognosis in ccRCC.

Optimal cutoff values for physical function tests in elderly patients with heart failure

Six-minute walk distance (6MWD) of 300 and 400 m are important targets of functional capacity. The present study was performed to determine cutoff values of physical function associated with 6MWD < 300 m and < 400 m in elderly patients with heart failure (HF). 6MWD, handgrip strength, quadriceps isometric strength (QIS), one-leg standing time (OLST), and 5-times sit-to-stand (5STS) before hospital discharge were evaluated in 1001 patients > 65 years (median age, 75: interquartile range, 71-80, 607 men) with HF. 6MWD < 300 and < 400 m were seen in 323 patients (32.3%) and 658 patients (65.7%), respectively. Handgrip strength, QIS, OLST, and 5STS were associated with 6MWD < 300 and < 400 m, respectively (P < 0.001). The cutoff values of handgrip strength, QIS, OLST, and 5STS were 18.9 kg, 35.0% body mass (BM), 9.1 s, and 9.5 s for 6MWD < 300 m, and 21.9 kg, 40.0% BM, 12.0 s, and 8.8 s for < 400 m, respectively. The cutoff values of physical function could be used to set cardiac rehabilitation goals and limiting determinants of reduced functional capacity in a clinical setting in elderly patients with HF.

Quantitative proteomic analysis reveals sophisticated metabolic alteration and identifies FMNL1 as a prognostic marker in clear cell renal cell carcinoma

Purpose: In this study, we have undertaken the whole proteomic analysis and got a better understanding of biological processes involved in the development and progression of ccRCC. We hope promising biomarkers can be uncovered to facilitate early diagnosis, predict the prognosis and progression, more importantly, to be applied as potential therapeutic targets.

Experimental design: Fresh frozen tissue samples were surgically resected from patients with local or locally advanced ccRCC. Trypsin digested proteins were analyzed using TMT-based LC-MS/MS proteomic approach, followed by bioinformatic analysis. A potential prognostic marker FMNL1 was chosen to be validated in TCGA_KIRC datasets (n=525 and 72), further validation sets (n=10 and 10) and expanded validation sets (n=81 and 16). The effects of FMNL1 on proliferation, migration and invasion were determined by colony formation, wound healing, and transwell assays in 786-O and Caki-1 cells in vitro study.

Results: A total of 657 differentially expressed proteins were identified and quantified between ccRCC and adjacent normal tissues (p-value<0.05, FC>2 or<1/2), of which 186 proteins were up-regulated and 471 proteins were down-regulated. Bioinformatic analysis showed enriched metabolic biological processes and pathways. Univariate and multivariate analysis defined FMNL1 as an independent negative prognostic marker in the TCGA datasets. High expression of FMNL1 correlated significantly with tumor stage and distant metastasis (P<0.05) both in the TCGA-KIRC datasets and expanded validation sets. Kaplan-Meier survival curve illustrated that the patients with high FMNL1 protein level had shorter OS time in the expanded validation sets (p=0.0273). In vitro experiments presented the functional effects of FMNL1 knockdown on the inhibition of proliferation, migration and invasion in cancer cell lines.

Conclusion and clinical relevance: The proteomic results uncovered sophisticated metabolic reprogramming of ccRCC and indicated that the upregulation of rate-limiting enzymes in glycolysis and mitochondrial impairment may be the cause of metabolic reprogramming in ccRCC. Moreover, FMNL1 has been identified as a promising prognostic marker, and knockdown of FMNL1 could inhibit ccRCC cell proliferation, migration and invasion, which might be used as a new effective therapeutic strategy to inhibit the progression of ccRCC.

Identification-Free Control over the Precursor Isotopic Mass Misassignment in Orbitrap-Based Proteomics

Selection of a precursor ion from a peptide isotopic cluster to obtain a fragmentation mass spectrum is a crucial step in data-dependent proteome analysis. However, the monoisotopic mass assignment performed in this step is often an issue confronted by the data acquisition software of hybrid Orbitrap FTMS that is most widely used in proteomics. To address the problem, many data processing tools, such as raw data converters and search engines, have optional accounting for the precursor mass shift due to the isotopic error. These solutions require additional data preprocessing steps and lead to an increase in the search space, thus making the analysis longer and/or less reliable. In this work, we processed 100 Orbitrap-based LC-MS/MS runs from 10 publicly available data sets to examine the rate of precursor isotope misassignment. The effect from taking the isotope error into account during the search on the number of identified peptides varied in a wide range from 0 to 33%. Thus, it may be tempting to spend extra time before or during a search to account for the mass assignment issue. Alternatively, this effect can be predicted a priori using an identification-free metric, which can be a part of data quality control software. Based on the results obtained in this work, we propose such a metric be further added into the visual and intuitive quality control software, viQC, developed previously and available at https://github.com/lisavetasol/viQC. It takes about a minute to calculate and plot nine quality metrics, including the proposed one for typical proteome analysis.

Fibrosis and cancer: shared features and mechanisms suggest common targeted therapeutic approaches

Epidemiological studies support a strong link between organ fibrosis and epithelial cancers. Moreover, clinical and experimental investigations consistently indicate that these diseases intertwine and share strikingly overlapping features. As a deregulated response to injury occurring in all body tissues, fibrosis is characterized by activation of fibroblasts and immune cells, contributing to progressive deposition of extracellular matrix (ECM) and inflammation. Cancers are driven by genetic alterations resulting in dysregulated cell survival, proliferation and dissemination. However, non-cancerous components of tumour tissues including fibroblasts, inflammatory cells and ECM play key roles in oncogenesis and cancer progression by providing a pro-mutagenic environment where cancer cells can develop, favouring their survival, expansion and invasiveness. Additional commonalities of fibrosis and cancer are also represented by overproduction of growth factors, like transforming growth factor β, epithelial-to-mesenchymal transition, high oxidative stress, Hippo pathway dysfunctions and enhanced cellular senescence. Here, we review advances in the analysis of cellular and molecular mechanisms involved in the pathogenesis of both organ fibrosis and cancer, with particular reference to chronic kidney diseases and renal cell cancers. Most importantly, improved understanding of common features is contributing to the development of innovative treatment strategies targeting shared mechanisms.

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.

Quadriceps Strength and Mortality in Older Patients With Heart Failure

BACKGROUND

This study was performed to test the hypothesis that low quadriceps isometric strength (QIS) is associated with greater risk of mortality and has the additive prognostic significance to the severity of heart failure (HF) and gait speed in older patients with HF.

METHODS

A retrospective cohort study was performed in 1273 patients ≥ 60 years of age with HF (mean age 75 ± 8 years, 59.1% men); all of whom were evaluated during hospitalization for usual gait speed and maximal QIS. The QIS was expressed relative to body mass (% BM). The endpoint was all-cause mortality.

RESULTS

Over a median follow-up period of 1.59 years (interquartile range, 0.58 to 3.42 years), 224 patients died. The cutoff value based on the Youden index for the QIS discriminating those at high risk of mortality was 36.2% BM for overall, and we defined less than this cutoff point of QIS as low QIS. After adjustment for the HF risk score, the hazard ratio in low QIS was 1.55 for overall (95% confidence interval [CI], 1.17-2.06). The addition of low QIS to the HF risk score and gait speed was associated with significant increases in both net reclassification improvement (NRI, 0.239 for overall; 95% CI, 0.096-0.381) and integrated discrimination improvement (IDI, 0.004 for overall; 95% CI, 0.001-0.009) for all-cause mortality.

CONCLUSION

Low QIS was strongly associated with poor prognosis and showed complementary prognostic predictive capability to the HF risk score and gait speed in older patients with HF.

Prognostic significance of proteomics and multi-omics studies in renal carcinoma

INTRODUCTION

Renal carcinoma, and in particular its most common variant, the clear cell subtype, is often diagnosed incidentally through abdominal imaging and frequently, the tumor is discovered at an early stage. However, 20% to 40% of patients undergoing nephrectomy for clinically localized renal cancer, even after accurate histological and clinical classification, will develop metastasis or recurrence, justifying the associated mortality rate. Therefore, even if renal carcinoma is not among the most frequent nor deadly cancers, a better prognostication is needed.

AREAS COVERED

Recently proteomics or other omics combinations have been applied to both cancer tissues, on the neoplasia itself and surrounding microenvironment, cultured cells, and biological fluids (so-called liquid biopsy) generating a list of prognostic molecular tools that will be reviewed in the present paper.

EXPERT OPINION

Although promising, none of the approaches listed above has been yet translated in clinics. This is likely due to the peculiar genetic and phenotypic heterogeneity of this cancer, which makes nearly each tumor different from all the others. Attempts to overcome this issue will be also revised. In particular, we will discuss how the application of omics-integrated approaches could provide the determinants of response to the different targeted drugs.

Impact of Gait Speed on the Obesity Paradox in Older Patients With Cardiovascular Disease

PURPOSE

The purpose of this study was to determine whether gait speed affects the obesity paradox in older patients with cardiovascular disease.

METHODS

The study population consisted of 2224 patients ≥60 years old with cardiovascular disease admitted to hospital between May 1, 2006, and January 31, 2018. Body mass index (BMI) and gait speed before hospital discharge were determined, and patients were divided into two groups: slow and preserved gait speed (≤0.8 and >0.8 m/s, respectively), according to the algorithm for sarcopenia diagnosis. The slow and preserved gait speed groups were also further subdivided according to BMI: <18.5 kg/m², 18.5-24.9 kg/m², and BMI ≥25.0 kg/m². The study endpoint was all-cause mortality.

RESULTS

The study population (male: 66.7%) had a mean age of 73.1 ± 7.6 years. Over a median follow-up period of 1.69 years (interquartile range 0.67-3.67 years), 283 patients died. Higher BMI was associated with favorable prognosis in the group with preserved gait speed but not in the group with slow gait speed after adjusting for other prognostic factors. Adding BMI to the clinical model significantly increased the area under the receiver operating characteristic curve in the group with preserved gait speed (0.744 vs 0.726, P = 0.028) but not in the group with slow gait speed (0.716 vs 0.716, P = 0.789).

CONCLUSIONS

Higher BMI was consistently associated with favorable prognosis in patients with cardiovascular disease and preserved gait speed but not in those with slow gait speed. These findings indicated that physical frailty influences the obesity paradox in older patients with cardiovascular disease.

Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks, to proteomics and histology imaging datasets generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) from clear cell renal cell carcinoma patients. We report robust correlations between a set of diagnostic proteins and predictions generated by an imaging-based classification model. Proteins significantly correlated with the histology-based predictions are significantly implicated in immune responses, extracellular matrix reorganization, and metabolism. Moreover, we showed that the genes encoding these proteins also reliably recapitulate the biological associations with imaging-derived predictions based on strong gene–protein expression correlations. Our findings offer novel insights into the integrative modeling of histology and omics data through machine learning, as well as the methodological basis for new research opportunities in this and other cancer types.