Prognostic and predictive value of VHL gene alteration in renal cell carcinoma: a meta-analysis and review

Diagnostic, predictive and prognostic molecular biomarkers in clear cell renal cell carcinoma: A retrospective study

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive subtype of kidney cancer. Many patients are diagnosed at advanced stages, making early detection crucial. Unfortunately, there are currently no noninvasive tests for ccRCC, emphasizing the need for new biomarkers. Additionally, ccRCC often develops resistance to treatments like radiotherapy and chemotherapy. Identifying biomarkers that predict treatment outcomes is vital for personalized care. The integration of artificial intelligence (AI), multi-omics analysis, and computational biology holds promise in bolstering detection precision and resilience, opening avenues for future investigations. The amalgamation of radiogenomics and biomaterial-basedimmunomodulation signifies a revolutionary breakthrough in diagnostic medicine. This review summarizes existing literature and highlights emerging biomarkers that enhance diagnostic, predictive, and prognostic capabilities for ccRCC, setting the stage for future clinical research.

Identification of ALDOB as a novel prognostic biomarker in kidney clear cell renal cell carcinoma

Kidney clear cell renal cell carcinoma (KIRC) is also the most lethal subtype among all kidney cancer subtypes, posing a severe threat to public health. Therefore, it is crucial to identify new, reliable biomarkers in KIRC. Therefore, it is crucial to identify novel, reliable biomarkers associated with KIRC. We analyzed RNA sequence results from TCGA and several GEO datasets. The commonly deregulated gene, ALDOB, was found in multiple data and confirmed its important prognostic value. Subsequently, we explored the specific mechanism by which ALDOB regulates anti-tumor immunity through in vivo and in vitro experiments. We found that ALDOB may play a role in regulating tumor growth by regulating CD8+ T cell infiltration. This is consistent with the results of our immune infiltration-related analysis. In addition, we have also discovered the effect of ALDOB in previous studies on other cancer types. Finally, we concluded that ALDOB may have potential reference value for immunotherapy and can also be used as an independent predictor of prognosis in KIRC.

Belzutifan: a novel therapeutic for the management of von Hippel-Lindau disease and beyond

The identification of the VHL gene and its role in regulating the hypoxia-inducible factor signaling pathway has helped to revolutionize the treatment of renal cell carcinoma (RCC). Belzutifan is a novel small-molecule inhibitor of hypoxia-inducible factor 2α which has demonstrated efficacy in treating von Hippel-Lindau (VHL) disease, earning regulatory approvals for this indication. There is also early evidence for efficacy in sporadic RCC. Belzutifan has a favorable safety profile. Several clinical trials are currently ongoing, which should help in identifying this promising drug's role in RCC and beyond. This review summarizes the history, pharmacology and clinical evidence for belzutifan use to date, and also explores unanswered questions as they relate to this novel therapeutic agent.

Predictive and prognostic biomarkers in urological tumours

Advancements in cutting-edge molecular profiling techniques, such as next-generation sequencing and bioinformatic analytic tools, have allowed researchers to examine tumour biology in detail and stratify patients based on factors linked with clinical outcome and response to therapy. This manuscript highlights the most relevant prognostic and predictive biomarkers in kidney, bladder, prostate and testicular cancers with recognised impact in clinical practice. In bladder and prostate cancer, new genetic acquisitions concerning the biology of tumours have modified the therapeutic scenario and led to the approval of target directed therapies, increasing the quality of patient care. Thus, it has become of paramount importance to choose adequate molecular tests, i.e., FGFR screening for urothelial cancer and BRCA1-2 alterations for prostate cancer, to guide the treatment plan for patients. While no tissue or blood-based biomarkers are currently used in routine clinical practice for renal cell carcinoma and testicular cancers, the field is quickly expanding. In kidney tumours, gene expression signatures might be the key to identify patients who will respond better to immunotherapy or anti-angiogenic drugs. In testicular germ cell tumours, the use of microRNA has outperformed conventional serum biomarkers in the diagnosis of primary tumours, prediction of chemoresistance, follow-up monitoring, and relapse prediction.

Ferroptosis-Related lncRNA to Predict the Clinical Outcomes and Molecular Characteristics of Kidney Renal Papillary Cell Carcinoma

Kidney renal papillary cell carcinoma (KIRP) is a highly heterogeneous type of kidney cancer, resulting in limited effective prognostic targets for KIRP patients. Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in the regulation of ferroptosis and iron metabolism, making them potential targets for the treatment and prognosis of KIRP. In this study, we constructed a ferroptosis-related lncRNA risk score model (FRM) based on the TCGA-KIRP dataset, which represents a novel subtype of KIRP not previously reported. The model demonstrated promising diagnostic accuracy and holds potential for clinical translation. We observed significant differences in metabolic activities, immune microenvironment, mutation landscape, ferroptosis sensitivity, and drug sensitivity between different risk groups. The high-risk groups exhibit significantly higher fractions of cancer-associated fibroblasts (CAFs), hematopoietic stem cells (HSC), and pericytes. Drugs (IC50) analysis provided a range of medication options based on different FRM typing. Additionally, we employed single-cell transcriptomics to further analyze the impact of immune invasion on the occurrence and development of KIRP. Overall, we have developed an accurate prognostic model based on the expression patterns of ferroptosis-related lncRNAs for KIRP. This model has the potential to contribute to the evaluation of patient prognosis, molecular characteristics, and treatment modalities, and can be further translated into clinical applications.

Management of Renal Cell Carcinoma: Promising Biomarkers and the Challenges to Reach the Clinic

The incidence of renal cell carcinoma (RCC) is increasing worldwide, yet research within this field is lagging behind other cancers. Despite increased detection of early disease as a consequence of the widespread use of diagnostic CT scans, 25% of patients have disseminated disease at diagnosis. Similarly, around 25% progress to metastatic disease following curatively intended surgery. Surgery is the cornerstone in the treatment of RCC; however, when the disease is disseminated, immunotherapy or immunotherapy in combination with a tyrosine kinase inhibitor is the patient's best option. Immunotherapy is a potent treatment, with durable treatment responses and potential to cure the patient, but only half of the patients benefit from the administered treatment, and there are currently no methods that can identify which patients will respond to immunotherapy. Moreover, there is a need to identify the patients in greatest risk of relapsing after surgery for localized disease and direct adjuvant treatment there. Even though several molecular biomarkers have been published to date, we are still lacking routinely used biomarkers to guide optimal clinical management. The purpose of this review is to highlight some of the most promising biomarkers, discuss the efforts made within this field to date, and describe the barriers needed to be overcome to have reliable and robust predictive and prognostic biomarkers in the clinic for renal cancer.

von Hippel-Lindau disease-related neoplasia with an emphasis on renal manifestations

von Hippel-Lindau (VHL) disease is characterized by biallelic inactivation of the VHL gene leading to abnormal or absent VHL protein function, and constitutive activation of hypoxia-inducible factors (HIF) that leads to pro-tumorigenic signaling. Individuals with VHL disease develop numerous cysts and tumors involving multiple organs including the kidneys, central nervous system, endolymphatic sac, lungs, pancreatobiliary system, adrenal glands, epididymis, and/or broad ligament. On histologic examination, these lesions show morphologic overlap as they are frequently characterized by cells with clear cytoplasm and prominent vascularity. In addition to distinguishing non-renal tumors from metastatic clear cell renal cell carcinoma, understanding site-specific histopathologic and immunophenotypic features of these tumors has several applications. This includes distinguishing VHL-related tumors from those that arise sporadically and lack VHL gene alterations, guiding further genetic workup, and helping distinguish between different genetic predisposition syndromes. In this context, immunohistochemical studies for markers such as paired box 8 (PAX-8), carbonic anhydrase 9 (CA9), and glucose transporter 1 (GLUT-1) have an important role in routine clinical practice and represent cost-effective diagnostic tools. The recent development of targeted therapeutics directed against HIF-mediated signaling represents a significant milestone in the management of VHL disease and highlights the importance of accurately diagnosing and characterizing the wide spectrum of VHL disease-associated lesions.

Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know

Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies. Because of the central role of imaging in cancer diagnosis and therapy, radiologists need to be familiar with the basic concepts of genomics, which are now becoming the new norm in oncologic clinical practice. By incorporating these concepts into clinical practice, radiologists can make their imaging interpretations more meaningful and specific, facilitate multidisciplinary clinical dialogue and interventions, and provide better patient-centric care. This review article highlights basic concepts of genomics and epigenomics, reviews the most common genetic alterations in cancer, and discusses the implications of these concepts on imaging by organ system in a case-based manner. This information will help stimulate new innovations in imaging research, accelerate the development and validation of new imaging biomarkers, and motivate efforts to bring new molecular and functional imaging methods to clinical radiology. Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Markers Supplemental material is available for this article. © RSNA, 2023.

Genomic Profiling and Molecular Characterization of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) treatment has undergone three major paradigm shifts in recent years, first with the introduction of molecular targeted therapies, then with immune checkpoint inhibitors, and, more recently, with immune-based combinations. However, to date, molecular predictors of response to targeted agents have not been identified for ccRCC. The WHO 2022 classification of renal neoplasms introduced the molecularly defined RCC class, which is a first step in the direction of a better molecular profiling of RCC. We reviewed the literature data on known genomic alterations of clinical interest in ccRCC, discussing their prognostic and predictive role. In particular, we explored the role of VHL, mTOR, chromatin modulators, DNA repair genes, cyclin-dependent kinases, and tumor mutation burden. RCC is a tumor whose pivotal genomic alterations have pleiotropic effects, and the interplay of these effects determines the tumor phenotype and its clinical behavior. Therefore, it is difficult to find a single genomic predictive factor, but it is more likely to identify a signature of gene alterations that could impact prognosis and response to specific treatment. To accomplish this task, the interpolation of large amounts of clinical and genomic data is needed. Nevertheless, genomic profiling has the potential to change real-world clinical practice settings.

Multi-center validation of an immune-related lncRNA signature for predicting survival and immune status of patients with renal cell carcinoma: an integrating machine learning-derived study

BACKGROUND

Long noncoding RNAs (lncRNAs) have been reported to play an important role in tumor immune modification. Nonetheless, the clinical implication of immune-associated lncRNAs in renal cell carcinoma (RCC) remains to be further explored.

METHODS

76 combinations of machine learning algorithms were integrated to develop and validate a machine learning-derived immune-related lncRNA signature (MDILS) in five independent cohorts (n = 801). We collected 28 published signatures and collated clinical variables for comparison with MDILS to verify its efficacy. Subsequently, molecular mechanisms, immune status, mutation landscape, and pharmacological profile were further investigated in different stratified patients.

RESULTS

Patients with high MDILS displayed worse overall survival than those with low MDILS. The MDILS could independently predict overall survival and convey robust performance across five cohorts. MDILS has a significantly better performance compared with traditional clinical variables and 28 published signatures. Patients with low MDILS exhibited more abundant immune infiltration and higher potency of immunotherapeutic response, while patients with high MDILS might be more sensitive to multiple chemotherapeutic drugs (e.g., sunitinib and axitinib).

CONCLUSION

MDILS is a robust and promising tool to facilitate clinical decision-making and precision treatment of RCC.

An Overview of Systemic Targeted Therapy in Renal Cell Carcinoma, with a Focus on Metastatic Renal Cell Carcinoma and Brain Metastases

The most commonly diagnosed malignancy of the urinary system is represented by renal cell carcinoma. Various subvariants of RCC were described, with a clear-cell type prevailing in about 85% of all RCC tumors. Patients with metastases from renal cell carcinoma did not have many effective therapies until the end of the 1980s, as long as hormonal therapy and chemotherapy were the only options available. The outcomes were unsatisfactory due to the poor effectiveness of the available therapeutic options, but then interferon-alpha and interleukin-2 showed treatment effectiveness, providing benefits but only for less than half of the patients. However, it was not until 2004 that targeted therapies emerged, prolonging the survival rate. Currently, new technologies and strategies are being developed to improve the actual efficacy of available treatments and their prognostic aspects. This article summarizes the mechanisms of action, importance, benefits, adverse events of special interest, and efficacy of immunotherapy in metastatic renal cell carcinoma, with a focus on brain metastases.

Sporadic renal cell carcinoma with widespread metastasis in young patient: A rare case report

Renal cell carcinoma occurrence is increasing from time to time and known as one of the most common cancers worldwide. RCC usually found in older age and common acquired risk factors for RCC including obesity, hypertension, diabetes, smoking and long-term use of NSAIDs. As for genetic risk, it is noted that Von Hippel-Lindau gene involved in the pathogenesis of RCC. Many treatment strategies were developed for RCC with various outcome. Here, we present a sporadic clear cell renal carcinoma in young male without VHL gene mutation and survive for long term period despite progressivity of treatment.

Relationship between body composition and PBRM1 mutations in clear cell renal cell carcinoma: a propensity score matching analysis

OBJECTIVE

This study aimed to examine the relationship between body muscle and adipose tissue composition in clear cell renal cell carcinoma patients with PBRM1 gene mutation.

METHODS

Cancer Genome Atlas Kidney clear cell renal cell carcinoma and Clinical Proteomic Tumor Analysis Consortium clear cell renal cell carcinoma collections were retrieved from the Cancer Imaging Archive. A total of 291 clear cell renal cell carcinoma patients were included in the study retrospectively. Patients' characteristics were obtained from Cancer Imaging Archive. Body composition was assessed with abdominal computed tomography using the automated artificial intelligence software (AID-U™, iAID Inc., Seoul, Korea). Body composition parameters of the patients were calculated. To investigate the net effect of body composition, the propensity score matching procedure was applied over age, gender, and T-stage parameters.

RESULTS

 Of the patients, 184 were males and 107 were females. Mutations in the PBRM1 gene were detected in 77 of the patients. While there was no difference in adipose tissue areas between the PBRM1 mutation group and those without PBRM1 mutation, statistically significant differences were found in normal attenuated muscle area parameters.

CONCLUSION

 This study shows that there was no difference between adipose tissue areas in patients with PBMR1 mutation, but normal attenuated muscle area was found to be higher in PBRM1 patients.

Ferroptosis-related gene signature predicts prognosis in kidney renal papillary cell carcinoma

Ferroptosis, an iron-dependent form of selective cell death, is involved in the development of many cancers. However, the role of ferroptosis-related genes (FRGs) in kidney renal papillary cell carcinoma (KIRP) is unclear. In this study, we examined the mRNA expression profiles and clinical data of patients with KIRP from the TCGA cohort. Consequently, 41 differentially-expressed FRGs were screened using the limma package, and 17 prognostic-related FRGs were identified by survival analysis and univariate Cox regression analyses. Thereafter, a ferroptosis-related gene prognostic index (FRGPI) was constructed based on five FRGs (AKR1C3, SAT1, FANCD2, HSBP1 and SQLE), using lasso Cox and multivariate Cox regression analyses. KIRP patients with high FRGPI scores displayed worse outcomes. Furthermore, the FRGPI was shown to be a reliable independent prognostic factor in both the training and testing cohorts. Comprehensive analysis also showed that the FRGPI can distinguish gene mutation, functional enrichment of immune cells and molecular function-related pathways. Interestingly, low FRGPI score could be more benefit from immune checkpoint inhibitors (ICIs) therapy. Then, the two hub prognostic genes (AKR1C3 and FANCD2) as a risk gene for KIRP were identified based on the FRGPI module, and the expression profiles of these two genes were validated using human KIRP cells, besides, we furthermore discovered that Fancd2 is significantly up-regulated in most cancers and is associated with prognosis. In conclusion, these findings showed that FRGPI can accurately predict the prognosis of patients with KIRP, suggesting that this risk model is a promising prognostic biomarker for these patients. Moreover, targeting ferroptosis (FANCD2) could be a potential therapeutic alternative for various cancers.

Overexpressed lncRNA FTX promotes the cell viability, proliferation, migration and invasion of renal cell carcinoma via FTX/miR‑4429/UBE2C axis

The present study aimed to explore the role of long non‑coding (lnc)RNA FTX and ubiquitin‑conjugating enzyme E2C (UBE2C) in promoting the progression of renal cell carcinoma (RCC) and the underlying regulatory mechanism. Relative levels of lncRNA FTX, UBE2C, AKT, CDK1 and CDK6 in RCC cell lines were detected by reverse transcription‑quantitative (RT‑q). Expression levels of UBE2C, phosphorylated (p)‑AKT/AKT, p‑CDK1/CDK1 and p‑CDK6/CDK6 in RCC and paracancerous specimens and RCC cells were measured by western blot or immunohistochemistry assay. In addition, the proliferative rate, cell viability, cell cycle progression, migratory rate and invasive rate of RCC cells overexpressing lncRNA FTX by lentivirus transfection were determined by a series of functional experiments, including the colony formation assay, MTT assay, flow cytometry, Transwell assay and wound healing assay. The targeted binding relationship in the lncRNA FTX/miR‑4429/UBE2C axis was validated by dual‑luciferase reporter assay. By intervening microRNA (miR)‑4492 and UBE2C by the transfection of miR‑4429‑mimics or short interfering UBE2C‑2, the regulatory effect of lncRNA FTX/miR‑4429/UBE2C axis on the progression of RCC was evaluated. Finally, a xenograft model of RCC in nude mice was established by subcutaneous implantation, thus evaluating the in vivo function of lncRNA FTX in the progression of RCC. The results showed that lncRNA FTX and UBE2C were upregulated in RCC specimens and cell lines. The overexpression of lncRNA FTX in RCC cells upregulated UBE2C. In addition, the overexpression of lncRNA FTX promoted the cell viability and proliferative, migratory and invasive capacities of RCC cells and accelerated the cell cycle progression. A dual‑luciferase reporter assay validated that lncRNA FTX exerted the miRNA sponge effect on miR‑4429, which was bound to UBE2C 3'UTR. Knockdown of UBE2C effectively reversed the regulatory effects of overexpressed lncRNA FTX on the abovementioned phenotypes of RCC cells. In the xenograft model of RCC, the mice implanted with RCC cells overexpressing lncRNA FTX showed a larger tumor size and higher tumor weight than those of controls, while the in vivo knockdown of UBE2C significantly reduced the size of RCC lesions, indicating the reversed cancer‑promoting effect of lncRNA FTX. Overall, the present study showed that lncRNA FTX was upregulated in RCC and could significantly promote the proliferative, migratory and invasive capacities, enhancing the viability and accelerating the cell cycle progression of RCC cells by exerting the miRNA sponge effect on miR‑4429 and thus upregulating UBE2C. lncRNA FTX and UBE2C are potential molecular biomarkers and therapeutic targets of RCC.

Targeting hypoxia-inducible factor pathways in sporadic and Von Hippel-Lindau syndrome-related kidney cancers

Hereditary and sporadic renal cell carcinomas (RCCs) are often associated with Von Hippel-Lindau (VHL)-gene inactivation. Patients with VHL disease have an increased risk of RCC, leading to bilateral nephrectomy and dialysis. In patients with advanced RCC, no standard second-lines are available after progression to immune checkpoint inhibitors (ICIs), and new agents are required to manage progression. HIFs have emerged as a promising target for metastatic RCC patients who have progressed to ICI-based combinations, as well as for those with RCC and VHL syndrome where the goal is to delay surgery and/or and preserve kidney function and avoid dialysis. This review describes the available evidence supporting the use of the small-molecule HIF-2 alpha inhibitor, belzutifan (MK-6482), as well as other new anti-HIF molecules that have demonstrated significant efficacy in VHL disease-related RCCs as well as for sporadic RCC that has progressed after the use of ICI-based combinations.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Radiogenomics in Clear Cell Renal Cell Carcinoma: A Review of the Current Status and Future Directions

Radiogenomics is a field of translational radiology that aims to associate a disease's radiologic phenotype with its underlying genotype, thus offering a novel class of non-invasive biomarkers with diagnostic, prognostic, and therapeutic potential. We herein review current radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the most common renal malignancy. A literature review was performed by querying PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases, identifying all relevant articles using the following search terms: "radiogenomics", "renal cell carcinoma", and "clear cell renal cell carcinoma". Articles included were limited to the English language and published between 2009-2021. Of 141 retrieved articles, 16 fit our inclusion criteria. Most studies used computed tomography (CT) images from open-source and institutional databases to extract radiomic features that were then modeled against common genomic mutations in ccRCC using a variety of machine learning algorithms. In more recent studies, we noted a shift towards the prediction of transcriptomic and/or epigenetic disease profiles, as well as downstream clinical outcomes. Radiogenomics offers a platform for the development of non-invasive biomarkers for ccRCC, with promising results in small-scale retrospective studies. However, more research is needed to identify and validate robust radiogenomic biomarkers before integration into clinical practice.

A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images

OBJECTIVES

We tried to realize accurate pathological classification, assessment of prognosis, and genomic molecular typing of renal cell carcinoma by CT texture feature analysis. To determine whether CT texture features can perform accurate pathological classification and evaluation of prognosis and genomic characteristics in renal cell carcinoma.

METHODS

Patients with renal cell carcinoma from five open-source cohorts were analyzed retrospectively in this study. These data were randomly split to train and test machine learning algorithms to segment the lesion, predict the histological subtype, tumor stage, and pathological grade. Dice coefficient and performance metrics such as accuracy and AUC were calculated to evaluate the segmentation and classification model. Quantitative decomposition of the predictive model was conducted to explore the contribution of each feature. Besides, survival analysis and the statistical correlation between CT texture features, pathological, and genomic signatures were investigated.

RESULTS

A total of 569 enhanced CT images of 443 patients (mean age 59.4, 278 males) were included in the analysis. In the segmentation task, the mean dice coefficient was 0.96 for the kidney and 0.88 for the cancer region. For classification of histologic subtype, tumor stage, and pathological grade, the model was on a par with radiologists and the AUC was 0.83 [Formula: see text] 0.1, 0.80 [Formula: see text] 0.1, and 0.77 [Formula: see text] 0.1 at 95% confidence intervals, respectively. Moreover, specific quantitative CT features related to clinical prognosis were identified. A strong statistical correlation (R² = 0.83) between the feature crosses and genomic characteristics was shown. The structural equation modeling confirmed significant associations between CT features, pathological (β =  - 0.75), and molecular subtype (β =  - 0.30).

CONCLUSIONS

The framework illustrates high performance in the pathological classification of renal cell carcinoma. Prognosis and genomic characteristics can be inferred by quantitative image analysis.

KEY POINTS

• The analytical framework exhibits high-performance pathological classification of renal cell carcinoma and is on a par with human radiologists. • Quantitative decomposition of the predictive model shows that specific texture features contribute to histologic subtype and tumor stage classification. • Structural equation modeling shows the associations of genomic characteristics to CT texture features. Overall survival and molecular characteristics can be inferred by quantitative CT texture analysis in renal cell carcinoma.

Clinicopathological and Molecular Features of Secondary Cancer (Metastasis) to the Thyroid and Advances in Management

Secondary tumours to the thyroid gland are uncommon and often incidentally discovered on imaging. Symptomatic patients often present with a neck mass. Collision tumours of secondary tumours and primary thyroid neoplasms do occur. Ultrasound-guided fine-needle aspiration, core-needle biopsy, and surgical resection with histological and immunohistochemical analysis are employed to confirm diagnosis as well as for applying molecular studies to identify candidates for targeted therapy. Biopsy at the metastatic site can identify mutations (such as EGFR, K-Ras, VHL) and translocations (such as EML4-ALK fusion) important in planning target therapies. Patients with advanced-stage primary cancers, widespread dissemination, or unknown primary origin often have a poor prognosis. Those with isolated metastasis to the thyroid have better survival outcomes and are more likely to undergo thyroid resection. Systemic therapies, such as chemotherapy and hormonal therapy, are often used as adjuvant treatment post-operatively or in patients with disseminated disease. New targeted therapies, such as tyrosine kinase inhibitors and immune checkpoint inhibitors, have shown success in reported cases. A tailored treatment plan based on primary tumour features, overall cancer burden, and co-morbidities is imperative. To conclude, secondary cancer to the thyroid is uncommon, and awareness of the updates on diagnosis and management is needed.

Biomarkers in renal cell carcinoma: Are we there yet?

Management of kidney cancer has undergone a paradigm shift with the approval of new therapies over the last two decades. Although these drugs have improved clinical outcomes in patients with kidney cancer, there are still a large number of patients who do not show objective responses. A multitude of investigators, including those for The Cancer Genome Atlas have biologically characterized and sub-classified kidney cancer. However, we have not been able to identify molecular targets to effectively treat patients with kidney cancer. As we familiarize ourselves with newer drugs for patients with kidney cancer, it is important to understand that these drugs may not work in every patient and instead may expose patients to unnecessary toxic effects along with burdening society with the financial impact. As we head toward the era of "precision medicine", validated biomarkers are being utilized to guide treatment choices and help identify pathways of resistance in other tumor types. The current review aims at evaluating the progress made so far in this realm for patients with kidney cancer.

Regulation of Immunity in Clear Cell Renal Carcinoma: Role of PD-1, PD-L1, and PD-L2

Regulation of immunity is a unique oncogenic mechanism that differs in different cancers. VHL deficient clear cell renal cell carcinomas (ccRCC) trigger the immune response resulting in cancer progression. This study aimed to investigate PD-1, PD-L1, and PD-L2 expression in ccRCC primary cancers and metastatic tissues associated with the p-VHL content, transcriptional, and growth factors expression. Methods: A total of 62 patients with RCC were enrolled in the study. Investigation of mRNA level was performed by PCR in real-time. Western blotting analysis was used for detecting the p-VHL protein content in tissues. Results: The PD-L2 prevalence in metastatic cancers is crucial in tumor progression. The VHL expression and p-VHL content determined the aggressive cancer behavior and elevated in disseminated tumors. The cancer dissemination was accompanied by an increase in both mRNA and VHL content. Conclusion: We present a new instrument targeting pathologies with p-VHL/HIF altered function that impact the PD-L2 expression through the change in transcriptional, growth factors, and AKT/mTOR modulation.

Can Targeting Hypoxia-Mediated Acidification of the Bone Marrow Microenvironment Kill Myeloma Tumor Cells?

Multiple myeloma (MM) is an incurable cancer arising from malignant plasma cells that engraft in the bone marrow (BM). The physiology of these cancer cells within the BM microenvironment (TME) plays a critical role in MM development. These processes may be similar to what has been observed in the TME of other (non-hematological) solid tumors. It has been long reported that within the BM, vascular endothelial growth factor (VEGF), increased angiogenesis and microvessel density, and activation of hypoxia-induced transcription factors (HIF) are correlated with MM progression but despite a great deal of effort and some modest preclinical success the overall clinical efficacy of using anti-angiogenic and hypoxia-targeting strategies, has been limited. This review will explore the hypothesis that the TME of MM engrafted in the BM is distinctly different from non-hematological-derived solid tumors calling into question how effective these strategies may be against MM. We further identify other hypoxia-mediated effectors, such as hypoxia-mediated acidification of the TME, oxygen-dependent metabolic changes, and the generation of reactive oxygen species (ROS), that may prove to be more effective targets against MM.

Background, applications and challenges of radiogenomics in genitourinary tumor

Genitourinary tumors are groups of tumors with high complexity and heterogeneity. For long-term monitoring, biomarkers that can be used in detection, grading and treatment response assessment are needed. With rapid development in imaging technology and cancer genomics, radiogenomics, the combination of "radiology" and "genomics", has emerged as a powerful tool in oncology practice in recent years because imaging can provide some information that genomic test cannot as gene expression and mutation status are usually evaluated on a small portion of the tumor and are usually not powerful enough to reflect tumor heterogeneity. Radiogenomics investigates the correlations between imaging features and gene expression of a disease, especially in oncologic diseases. It aims to detect the disease's mutation status and supplement genomic analysis based on imaging analysis, providing additional findings for diagnosis, treatment decisions, evaluation of treatment response and prognosis prediction of the disease. Recent years have seen an increase in the number of studies investigating the application of radiogenomics in genitourinary tumors. Many studies have shown promising results. However, there still exist limitations and challenges. In this review, we will summarize recent applications of radiogenomics in genitourinary tumors and discuss limitiations, challenges and future directions of radiogenomics.

Review of Prognostic Expression Markers for Clear Cell Renal Cell Carcinoma

Context: The number of prognostic markers for clear cell renal cell carcinoma (ccRCC) has been increasing regularly over the last 15 years, without being integrated and compared.

Objective: Our goal was to perform a review of prognostic markers for ccRCC to lay the ground for their use in the clinics.

Evidence Acquisition: PubMed database was searched to identify RNA and protein markers whose expression level was reported as associated with survival of ccRCC patients. Relevant studies were selected through cross-reading by two readers.

Evidence Synthesis: We selected 249 studies reporting an association with prognostic of either single markers or multiple-marker models. Altogether, these studies were based on a total of 341 distinct markers and 13 multiple-marker models. Twenty percent of these markers were involved in four biological pathways altered in ccRCC: cell cycle, angiogenesis, hypoxia, and immune response. The main genes (VHL, PBRM1, BAP1, and SETD2) involved in ccRCC carcinogenesis are not the most relevant for assessing survival.

Conclusion: Among single markers, the most validated markers were KI67, BIRC5, TP53, CXCR4, and CA9. Of the multiple-marker models, the most famous model, ClearCode34, has been highly validated on several independent datasets, but its clinical utility has not yet been investigated.

Patient Summary: Over the years, the prognosis studies have evolved from single markers to multiple-marker models. Our review highlights the highly validated prognostic markers and multiple-marker models and discusses their clinical utility for better therapeutic care.

Tissue Based Biomarkers for Metastatic Clear Cell Renal Carcinoma: A Systematic Review

Background: Treatments for metastatic clear cell renal carcinoma (mccRCC) are evolving with multiple targeted and immune therapy drugs currently approved by regulatory agencies as single agents or in combination. Developing predictive biomarkers to determine which patients derive a differential benefit from a particular treatment is an area of ongoing clinical research. Objective: We sought to systematically evaluate the role of tumour tissue-based biomarkers that assist in selection of therapy for mccRCC. Methods: Literature addressing the role of biomarkers in mccRCC was identified through a search of the electronic databases MEDLINE, Embase, and the Web of Science and a hand search of major conference abstracts (from Jan 2010 –Sep 2020). Abstracts were screened to identify papers meriting full-text review. Studies with a comparison arm were included to assess biomarker relevance. A narrative review of studies was performed. Results: The literature search yielded 6784 potentially relevant articles. 133 articles met criteria for full text review, and 10 articles were identified by scanning bibliographies of relevant studies. A total of 33 articles (involving 13 studies) were selected for data extraction and subsequent review. Conclusions: Predictive biomarkers for immediate use in the clinic are lacking, and embedding their evaluation and validation in future clinical trials is needed to refine practice and patient selection.

Biomarker Development for Metastatic Renal Cell Carcinoma: Omics, Antigens, T-cells, and Beyond

The treatment of metastatic renal cell carcinoma has evolved quickly over the last few years from a disease managed primarily with sequential oral tyrosine kinase inhibitors (TKIs) targeting the vascular endothelial growth factor (VEGF) pathway, to now with a combination of therapies incorporating immune checkpoint blockade (ICB). Patient outcomes have improved with these innovations, however, controversy persists regarding optimal sequence and patient selection amongst the available combinations. Ideally, predictive biomarkers would aid in guiding treatment decisions and personalizing care. However, clinically-actionable biomarkers have remained elusive. We aim to review the available evidence regarding biomarkers for both TKIs and ICB and will present where the field may be headed in the years to come.

Genomic profiling in renal cell carcinoma

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

Von Hippel-Lindau Disease: Current Challenges and Future Prospects

Understanding of molecular mechanisms of tumor growth has an increasing impact on the development of diagnostics and targeted therapy of human neoplasia. In this review, we summarize the current knowledge on molecular mechanisms and their clinical implications in von Hippel-Lindau (VHL) disease. This autosomal dominant tumor syndrome usually manifests in young adulthood and predisposes affected patients to the development of benign and malignant tumors of different organ systems mainly including the nervous system and internal organs. A consequent screening and timely preventive treatment of lesions are crucial for patients affected by VHL disease. Surgical indications and treatment have been evaluated and optimized over many years. In the last decade, pharmacological therapies have been evolving, but are largely still at an experimental stage. Effective pharmacological therapy as well as detection of biomarkers is based on the understanding of the molecular basis of disease. The molecular basis of von Hippel-Lindau disease is the loss of function of the VHL protein and subsequent accumulation of hypoxia-inducible factor with downstream effects on cellular metabolism and differentiation. Organs affected by VHL disease may develop frank tumors. More characteristically, however, they reveal multiple separate microscopic foci of neoplastic cell proliferation. The exact mechanisms of tumorigenesis in VHL disease are, however, still not entirely understood and knowledge on biomarkers and targeted therapy is scarce.

Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas

BACKGROUND

BRCA1-associated protein 1 (BAP1) mutation is an unfavorable factor for overall survival in patients with clear cell renal cell carcinoma (ccRCC). Radiomics literature about BAP1 mutation lacks papers that consider the reliability of texture features in their workflow.

PURPOSE

Using texture features with a high inter-observer agreement, we aimed to develop and internally validate a machine learning-based radiomic model for predicting the BAP1 mutation status of ccRCCs.

MATERIAL AND METHODS

For this retrospective study, 65 ccRCCs were included from a public database. Texture features were extracted from unenhanced computed tomography (CT) images, using two-dimensional manual segmentation. Dimension reduction was done in three steps: (i) inter-observer agreement analysis; (ii) collinearity analysis; and (iii) feature selection. The machine learning classifier was random forest. The model was validated using 10-fold nested cross-validation. The reference standard was the BAP1 mutation status.

RESULTS

Out of 744 features, 468 had an excellent inter-observer agreement. After the collinearity analysis, the number of features decreased to 17. Finally, the wrapper-based algorithm selected six features. Using selected features, the random forest correctly classified 84.6% of the labelled slices regarding BAP1 mutation status with an area under the receiver operating characteristic curve of 0.897. For predicting ccRCCs with BAP1 mutation, the sensitivity, specificity, and precision were 90.4%, 78.8%, and 81%, respectively. For predicting ccRCCs without BAP1 mutation, the sensitivity, specificity, and precision were 78.8%, 90.4%, and 89.1%, respectively.

CONCLUSION

Machine learning-based unenhanced CT texture analysis might be a potential method for predicting the BAP1 mutation status of ccRCCs.

Anti-angiogenesis and Immunotherapy: Novel Paradigms to Envision Tailored Approaches in Renal Cell-Carcinoma

Although decision making strategy based on clinico-histopathological criteria is well established, renal cell carcinoma (RCC) represents a spectrum of biological ecosystems characterized by distinct genetic and molecular alterations, diverse clinical courses and potential specific therapeutic vulnerabilities. Given the plethora of drugs available, the subtype-tailored treatment to RCC subtype holds the potential to improve patient outcome, shrinking treatment-related morbidity and cost. The emerging knowledge of the molecular taxonomy of RCC is evolving, whilst the antiangiogenic and immunotherapy landscape maintains and reinforces their potential. Although several prognostic factors of survival in patients with RCC have been described, no reliable predictive biomarkers of treatment individual sensitivity or resistance have been identified. In this review, we summarize the available evidence able to prompt more precise and individualized patient selection in well-designed clinical trials, covering the unmet need of medical choices in the era of next-generation anti-angiogenesis and immunotherapy.

Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings

To evaluate the potential application of computed tomography (CT) radiomics in the prediction of BRCA1-associated protein 1 (BAP1) mutation status in patients with clear-cell renal cell carcinoma (ccRCC). In this retrospective study, clinical and CT imaging data of 54 patients were retrieved from The Cancer Genome Atlas–Kidney Renal Clear Cell Carcinoma database. Among these, 45 patients had wild-type BAP1 and nine patients had BAP1 mutation. The texture features of tumor images were extracted using the Matlab-based IBEX package. To produce class-balanced data and improve the stability of prediction, we performed data augmentation for the BAP1 mutation group during cross validation. A model to predict BAP1 mutation status was constructed using Random Forest Classification algorithms, and was evaluated using leave-one-out-cross-validation. Random Forest model of predict BAP1 mutation status had an accuracy of 0.83, sensitivity of 0.72, specificity of 0.87, precision of 0.65, AUC of 0.77, F-score of 0.68. CT radiomics is a potential and feasible method for predicting BAP1 mutation status in patients with ccRCC.

Combining molecular and imaging metrics in cancer: radiogenomics

Background

Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing.

Main body

In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis.

Conclusion

Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.

A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis

Kidney renal cell carcinoma (KIRC), which is the most common subtype of kidney cancer, has a poor prognosis and a high mortality rate. In this study, a multi-omics analysis is performed to build a multi-gene prognosis signature for KIRC. A combination of a DNA methylation analysis and a gene expression data analysis revealed 863 methylated differentially expressed genes (MDEGs). Seven MDEGs (BID, CCNF, DLX4, FAM72D, PYCR1, RUNX1, and TRIP13) were further screened using LASSO Cox regression and integrated into a prognostic risk score model. Then, KIRC patients were divided into high- and low-risk groups. A univariate cox regression analysis revealed a significant association between the high-risk group and a poor prognosis. The time-dependent receiver operating characteristic (ROC) curve shows that the risk group performs well in predicting overall survival. Furthermore, the risk group is contained in the best multivariate model that was obtained by a multivariate stepwise analysis, which further confirms that the risk group can be used as a potential prognostic biomarker. In addition, a nomogram was established for the best multivariate model and shown to perform well in predicting the survival of KIRC patients. In summary, a seven-MDEG signature is a powerful prognosis factor for KIRC patients and may provide useful suggestions for their personalized therapy.

VHL Expression in Kidney Cancer: Relation to Metastasis Development, Transcription and Growth Factors and Component of Akt/m-TOR Signaling Pathway

Von Hippel-Lindau protein (VHL) is associated with the development and progression of kidney cancer. An increase in VHL expression was found in patients with the disseminated form of the disease compared to the localized cancer, which was combined with a uniform distribution of decreased (<1.0) and increased (>1.0) VHL mRNA levels in renal cancer patients depending on the dissemination of the process. The increase in VHL expression was accompanied an increase in the level of mRNA for NF-κB p65 and kinases PDK1 and Akt. The revealed data indicate the importance of molecular biological parameters in oncogenesis.

Radiogenomics in renal cell carcinoma

Radiogenomics, a field of radiology investigating the association between the imaging features of a disease and its gene expression pattern, has expanded considerably in the last few years. Recent advances in whole-genome sequencing of clear cell renal cell carcinoma (ccRCC) and the identification of mutations with prognostic significance have led to increased interest in the relationship between imaging and genomic data. ccRCC is particularly suitable for radiogenomic analysis as the relative paucity of mutated genes allows for more straightforward genomic-imaging associations. The ultimate aim of radiogenomics of ccRCC is to retrieve additional data for accurate diagnosis, prognostic stratification, and optimization of therapy. In this review article, we will present the state-of-the-art of radiogenomics of ccRCC, and after briefly reviewing updates in genomics, we will discuss imaging-genomic associations for diagnosis and staging, prognosis, and for assessment of optimal therapy in ccRCC.

Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status

OBJECTIVE

The purpose of this study is to evaluate the potential value of machine learning (ML)-based high-dimensional quantitative CT texture analysis in predicting the mutation status of the gene encoding the protein polybromo-1 (PBRM1) in patients with clear cell renal cell carcinoma (RCC).

MATERIALS AND METHODS

In this retrospective study, 45 patients with clear cell RCC (29 without the PBRM1 mutation and 16 with the PBRM1 mutation) were identified in The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma database. To create stable ML models and balanced classes, the data were augmented to a total of 161 labeled segmentations (87 without the PBRM1 mutation and 74 with the PBRM1 mutation) by obtaining three to five different samples per patient. Texture features were extracted from corticomedullary phase contrast-enhanced CT images with the use of an open-source software package for the extraction of radiomic data from medical images. Reproducibility analysis (intraclass correlation) was performed by two radiologists. Attribute selection and model optimization were done using a wrapper-based classifier-specific algorithm with nested cross-validation. ML classifiers were an artificial neural network (ANN) algorithm and a random forest (RF) algorithm. The models were validated using 10-fold cross-validation. The reference standard was the PBRM1 mutation status. The main performance metric was the AUC value.

RESULTS

Of 828 extracted texture features, 759 had excellent reproducibility. Using 10 selected features, the ANN algorithm correctly classified 88.2% (142 of 161) of the clear cell RCCs in terms of PBRM1 mutation status (AUC value, 0.925). Using five selected features, the RF algorithm correctly classified 95.0% (153 of 161) of the clear cell RCCs (AUC value, 0.987). Overall, the RF algorithm performed better than the ANN algorithm (z score = -2.677; p = 0.007).

CONCLUSION

ML-based high-dimensional quantitative CT texture analysis might be a feasible and potential method for predicting PBRM1 mutation status in patients with clear cell RCC.

Clinicopathologic Significance of VHL Gene Alteration in Clear-Cell Renal Cell Carcinoma: An Updated Meta-Analysis and Review

The von Hippel-Lindau (VHL) gene is inactivated frequently in sporadic clear-cell renal cell carcinomas (ccRCCs) by genetic alteration (mutation, loss of heterozygosity, or promoter hypermethylation). However, the pathological or prognostic significance of VHL gene alteration has not been well defined. We conducted this meta-analysis to evaluate the association between VHL alteration and clinopathologic findings in ccRCCs. We performed a systematic computerized search of online databases, including PubMed, EMBASE, Web of Science, and Google Scholar (up to July 2018). From ten studies, 1,082 patients were included in the pooled analyses of odds ratios (ORs) with 95% confidence intervals (CIs) for pathological features (nuclear grade and disease stage) or hazard ratios (HRs) with 95% CIs for overall survival (OS). VHL alteration was not significantly associated with nuclear grade (OR = 0.79, 95% CI: 0.59–1.06, p = 0.12) or disease stage (OR = 1.07, 95% CI: 0.79–1.46, p = 0.65). There was also no significant correlation between VHL alteration and OS (HR = 0.75, 95% CI: 0.43–1.29, p = 0.30). When we pooled HRs for OS according to the VHL alteration types, the combined HRs were 0.72 (95% CI: 0.47–1.11, p = 0.14) for VHL mutations and 1.32 (95% CI: 0.70–2.47, p = 0.39) for methylation. In conclusion, this meta-analysis indicates that VHL gene alteration is not significantly associated with the pathological features and survival in patients with ccRCC.

VHL loss predicts response to Aurora kinase A inhibitor in renal cell carcinoma cells

The majority of molecular targets of anticancer agents are limited to a subset of patients, and therefore identification of more specific biomarkers that can be used to improve clinical outcomes is of increasing interest. The present study showed that von Hippel‑Lindau tumor suppressor (VHL) tumor‑suppressor activity may influence the therapeutic response to Aurora kinase A (AURKA) inhibitors in human renal cell carcinoma (RCC). VHL protein (pVHL) expression was evaluated by immunoblotting in the human RCC cell lines CAKI, ACHN, 786‑O, 769‑P and A498. The anti‑tumor activities of alisertib, an AURKA‑specific chemical inhibitor, were detected by Cell Counting Kit‑8 assay in vitro and mouse xenograft model in vivo. Additionally, the VHL‑mediated anti‑tumor activity was assessed in 769‑P and CAKI cells via the loss or gain of VHL. The results revealed that VHL‑deficient 786‑O, 769‑P and A498 cells were sensitive to alisertib. By contrast, alisertib‑resistant CAKI and ACHN cells expressed the wild type VHL gene. In addition, rescue or knockdown of VHL was observed to increase or decrease alisertib anti‑proliferation activity, respectively, in RCC cells. The inverse correlation between the VHL gene expression profile and alisertib sensitivity was further confirmed in human cancer xenografts models. Taken together, these results suggested that VHL loss could potentially serve as a biomarker for predicting the efficacy of AURKA inhibitors.

Polymorphisms in the Von Hippel-Lindau Gene Are Associated With Overall Survival in Metastatic Clear-Cell Renal-Cell Carcinoma Patients Treated With VEGFR Tyrosine Kinase Inhibitors

BACKGROUND

Clear-cell renal-cell carcinoma (ccRCC) is characterized by loss of a functional Von Hippel-Lindau (VHL) protein. We investigated the potential of 3 single nucleotide polymorphisms (SNPs) in VHL as biomarkers in metastatic ccRCC (m-ccRCC) patients treated with vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKIs).

PATIENTS AND METHODS

We genotyped 3 VHL SNPs in 199 m-ccRCC patients: rs1642742 T > C, rs1642743 A > G, and rs1678607 C > A. Primary end points were response rate (RR), progression-free survival (PFS), and overall survival (OS) after start of first-line TKI. RR was compared with Fisher's exact test, and PFS and OS with Kaplan-Meier analysis and multivariable Cox regression. Secondary end points were association with VHL promotor hypermethylation, VHL mutation status, VHL loss of heterozygosity, ≥ 25% sarcomatoid dedifferentiation, and expression of genes implicated in angiogenesis and immunoresponse (Fisher's exact test and unpaired t tests).

RESULTS

The minor alleles of rs1642742 and rs1642743, known to be in close linkage disequilibrium, were associated with poor outcome, following a recessive pattern. For the rs1642742 CC versus TT/TC genotype, OS was 11 versus 26 months (hazard ratio = 2.3; 95% confidence interval, 1.2-6.6; P = .015). For the rs1642743 GG versus AA/AG genotype, OS was 15 versus 28 months (hazard ratio = 2.6; 95% confidence interval, 1.4-5.0; P = .004). After multivariable analysis, both remained linked with poor OS (P = .018 and P = .009, respectively). There was a trend toward shorter PFS and poorer RR. Both SNPs were associated with ≥ 25% sarcomatoid dedifferentiation (P = .037 and .006, respectively). No significant results were found for rs1678607.

CONCLUSION

rs1642742 and rs1642743 are candidate biomarkers for poor OS in m-ccRCC patients receiving first-line VEGFR-TKI. They are associated with higher levels of sarcomatoid dedifferentiation.

Von Hippel-Lindau disease and Sturge-Weber syndrome

The primary neurologic involvement in both von Hippel-Lindau (VHL) disease and Sturge-Weber syndrome (SWS) is vascular tumor/vascular malformation, but molecular pathogenesis, long-term symptom evolution, and treatment are quite different. VHL is caused by dominant inherited or de novo germline mutations, while SWS is caused by somatic mosaicism. A diagnosis of VHL carries substantial cancer risk, while the clinical issues in SWS are primarily related to the consequences of the intracranial vascular abnormalities.

Survival prediction of kidney renal papillary cell carcinoma by comprehensive LncRNA characterization

Kidney renal papillary cell carcinoma (KIRP) accounts for 10%-15% of renal cell carcinoma (RCC), patients with KIRP tend to have a poor prognosis, and there was a lack of effective prognostic indicators for this type of cancer. Currently, owing to the availability of The Cancer Genome Atlas (TCGA), long non-coding RNAs (LncRNAs) have been discovered to indicate a prognostic value in some tumors. In that regard, we analyzed lncRNA-sequencing data of KIRP in TCGA, and among 780 differentially-expressed lncRNAs, we selected 37 lncRNAs which were able to assist the prognosis. In addition, by using the multivariate cox regression analysis, the prognosis index (PI) that consisted of 7 lncRNAs (including AFAP1-AS1, GAS6-AS1, RP11-1C8.7, RP11-21L19.1, RP11-503C24.1, RP11-536I6.2, and RP11-63A11.1) could predict the progression and outcomes of KIRP with accuracy. More importantly, the PI was considered an independent indicator for prognostication of KIRP. Moreover, having categorized patients with KIRP into cohorts of high risk and low risk, according to the PI, we found that the key genes and pathways varied in these two groups. Overall, these LncRNAs, especially the PI, may be conceived as biomarkers and helpful for determining the different pathological stages for KIRP patients. However, their biological functions need to be further confirmed.