Multi-Omics Analysis of the Expression and Prognosis for FKBP Gene Family in Renal Cancer

FKBP10 promotes clear cell renal cell carcinoma progression and regulates sensitivity to the HIF2α blockade by facilitating LDHA phosphorylation

Renal cell carcinoma (RCC) is one of the three major malignant tumors of the urinary system and originates from proximal tubular epithelial cells. Clear cell renal cell carcinoma (ccRCC) accounts for approximately 80% of RCC cases and is recognized as a metabolic disease driven by genetic mutations and epigenetic alterations. Through bioinformatic analysis, we found that FK506 binding protein 10 (FKBP10) may play an essential role in hypoxia and glycolysis pathways in ccRCC progression. Functionally, FKBP10 promotes the proliferation and metastasis of ccRCC in vivo and in vitro depending on its peptidyl-prolyl cis-trans isomerase (PPIase) domains. Mechanistically, FKBP10 binds directly to lactate dehydrogenase A (LDHA) through its C-terminal region, the key regulator of glycolysis, and enhances the LDHA-Y10 phosphorylation, which results in a hyperactive Warburg effect and the accumulation of histone lactylation. Moreover, HIFα negatively regulates the expression of FKBP10, and inhibition of FKBP10 enhances the antitumor effect of the HIF2α inhibitor PT2385. Therefore, our study demonstrates that FKBP10 promotes clear cell renal cell carcinoma progression and regulates sensitivity to HIF2α blockade by facilitating LDHA phosphorylation, which may be exploited for anticancer therapy.

Proline Isomerization: From the Chemistry and Biology to Therapeutic Opportunities

Proline isomerization, the process of interconversion between the cis- and trans-forms of proline, is an important and unique post-translational modification that can affect protein folding and conformations, and ultimately regulate protein functions and biological pathways. Although impactful, the importance and prevalence of proline isomerization as a regulation mechanism in biological systems have not been fully understood or recognized. Aiming to fill gaps and bring new awareness, we attempt to provide a wholistic review on proline isomerization that firstly covers what proline isomerization is and the basic chemistry behind it. In this section, we vividly show that the cause of the unique ability of proline to adopt both cis- and trans-conformations in significant abundance is rooted from the steric hindrance of these two forms being similar, which is different from that in linear residues. We then discuss how proline isomerization was discovered historically followed by an introduction to all three types of proline isomerases and how proline isomerization plays a role in various cellular responses, such as cell cycle regulation, DNA damage repair, T-cell activation, and ion channel gating. We then explore various human diseases that have been linked to the dysregulation of proline isomerization. Finally, we wrap up with the current stage of various inhibitors developed to target proline isomerases as a strategy for therapeutic development.

Subcellular Expression Patterns of FKBP Prolyl Isomerase 10 (FKBP10) in Colorectal Cancer and Its Clinical Significance

FKBP10, a member of the FK506-binding protein (FKBP) family, has been implicated in cancer development, although its prognostic function remains controversial. In this study, we analyzed the expression of FKBP10 in tumor tissues using online databases (TCGA) as well as our CRC cohort, and investigated the relationship between its subcellular expression pattern and patient outcomes. Cox regression analysis was used to determine the associations between different subcellular expression patterns of FKBP10 and clinical features of patients. We also discussed the expression level of FKBP10 based on different subcellular expression patterns. Our results showed that FKBP10 was significantly elevated in CRC tissues and exhibited three different subcellular expression patterns which were defined as 'FKBP10-C' (concentrated), 'FKBP10-T' (transitional) and 'FKBP10-D' (dispersive). The FKBP10-D expression pattern was only found in tumor tissues and was associated with unfavorable disease-free survival in CRC patients. High expression levels of FKBP10-C predicted an unfavorable prognosis of recurrence of CRC, while FKBP10-D did not. Our findings suggest that the subcellular expression patterns and expression level of FKBP10 play crucial prognostic roles in CRC, which revealed that FKBP10 may be a viable prognostic and therapeutic target for the diagnosis and treatment of CRC.

FKBP11 upregulation promotes proliferation and migration in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer related deaths world over. Early diagnosis and effective treatment monitoring significantly improves patients' outcomes. FKBP11 gene is highly expressed in HCC and could play a role in its development, early diagnosis and treatment.

OBJECTIVE

This study aimed to evaluate the expression of FKBP11 in HCC, its correlation with patients' clinical characteristics and potential role in HCC development.

METHODS

Expression was determined by bioinformatics analysis, quantitative real-time PCR, western blot, and immunohistochemistry. CCK-8, Transwell and wound healing assays were used to investigate involvement in HCC development.

RESULTS

FKBP11 was significantly upregulated in HCC cells, tissues and blood (all p< 0.001). Its receiver operator characteristic (ROC) curve had an AUC of 0.864 (95% CI: 0.823-0.904), at a sensitivity of 0.86 and specificity of 0.78 indicating a good diagnostic potential in HCC. Its expression was markedly reduced after surgery (p< 0.0001), indicating a potential application in HCC treatment follow-up. Knockdown of FKBP11 in HCC cells attenuated proliferation and migration, suggesting a possible role in HCC pathogenesis.

CONCLUSION

This study thus found that FKBP11 is upregulated in HCC, and the upregulation promotes HCC development. FKBP11 levels are significantly reduced post-surgery and could be a potential diagnostic and prognostic marker for HCC.

Multimodal AI for prediction of distant metastasis in carcinoma patients

Metastasis of cancer is directly related to death in almost all cases, however a lot is yet to be understood about this process. Despite advancements in the available radiological investigation techniques, not all cases of Distant Metastasis (DM) are diagnosed at initial clinical presentation. Also, there are currently no standard biomarkers of metastasis. Early, accurate diagnosis of DM is however crucial for clinical decision making, and planning of appropriate management strategies. Previous works have achieved little success in attempts to predict DM from either clinical, genomic, radiology, or histopathology data. In this work we attempt a multimodal approach to predict the presence of DM in cancer patients by combining gene expression data, clinical data and histopathology images. We tested a novel combination of Random Forest (RF) algorithm with an optimization technique for gene selection, and investigated if gene expression pattern in the primary tissues of three cancer types (Bladder Carcinoma, Pancreatic Adenocarcinoma, and Head and Neck Squamous Carcinoma) with DM are similar or different. Gene expression biomarkers of DM identified by our proposed method outperformed Differentially Expressed Genes (DEGs) identified by the DESeq2 software package in the task of predicting presence or absence of DM. Genes involved in DM tend to be more cancer type specific rather than general across all cancers. Our results also indicate that multimodal data is more predictive of metastasis than either of the three unimodal data tested, and genomic data provides the highest contribution by a wide margin. The results re-emphasize the importance for availability of sufficient image data when a weakly supervised training technique is used. Code is made available at: https://github.com/rit-cui-lab/Multimodal-AI-for-Prediction-of-Distant-Metastasis-in-Carcinoma-Patients.

Penetrating Exploration of Prognostic Correlations of the FKBP Gene Family with Lung Adenocarcinoma

The complexity of lung adenocarcinoma (LUAD), the development of which involves many interacting biological processes, makes it difficult to find therapeutic biomarkers for treatment. FK506-binding proteins (FKBPs) are composed of 12 members classified as conservative intracellular immunophilin family proteins, which are often connected to cyclophilin structures by tetratricopeptide repeat domains and have peptidyl prolyl isomerase activity that catalyzes proline from residues and turns the trans form into the cis form. Since FKBPs belong to chaperone molecules and promote protein folding, previous studies demonstrated that FKBP family members significantly contribute to the degradation of damaged, misfolded, abnormal, and foreign proteins. However, transcript expressions of this gene family in LUAD still need to be more fully investigated. In this research, we adopted high-throughput bioinformatics technology to analyze FKBP family genes in LUAD to provide credible information to clinicians and promote the development of novel cancer target drugs in the future. The current data revealed that the messenger (m)RNA levels of FKBP2, FKBP3, FKBP4, FKBP10, FKBP11, and FKBP14 were overexpressed in LUAD, and FKBP10 had connections to poor prognoses among LUAD patients in an overall survival (OS) analysis. Based on the above results, we selected FKBP10 to further conduct a comprehensive analysis of the downstream pathway and network. Through a DAVID analysis, we found that FKBP10 was involved in mitochondrial electron transport, NADH to ubiquinone transport, mitochondrial respiratory chain complex I assembly, etc. The MetaCore pathway analysis also indicated that FKBP10 was involved in "Ubiquinone metabolism", "Translation_(L)-selenoaminoacid incorporation in proteins during translation", and "Transcription_Negative regulation of HIF1A function". Collectively, this study revealed that FKBP family members are both significant prognostic biomarkers for lung cancer progression and promising clinical therapeutic targets, thus providing new targets for treating LUAD patients.

Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set

BACKGROUND

Mesenchymal stem cells (MSCs) play a crucial role in osteosarcoma (OS) growth and progression. This study conducted a bioinformatics analysis of a single-cell ribonucleic acid sequencing data set and explored the MSC-specific differentially expressed genes (DEGs) in advanced OS.

METHODS

MSC-specific DEGs from GSE152048 was extracted using Seurat R package. These DEGs were then subjected to the functional analysis, and several key genes were further identified and underwent a prognosis analysis.

RESULTS

A total of 234 upregulated and 280 downregulated DEGs were identified between the MSCs and other cells, and a total of 188 upregulated and 158 downregulated DEGs were identified between the MSCs and osteoblastic cells. The Gene Ontology (GO) functional analysis showed that the specific DEGs between the MSCs and osteoblastic cells were enriched in GO terms such as "collagen catabolic process", "positive regulation of pathway-restricted SMAD protein phosphorylation", "osteoblast differentiation", "regulation of release of cytochrome c from mitochondria" and "interleukin-1 production". The specific DEGs between the MSCs and osteoblastic cells were subjected to a protein-protein interaction network analysis. Further, a survival analysis of 20 genes with combined scores >0.94 revealed that the low expression of ANXA1 (annexin A1) and TPM1 (tropomyosin 1) was associated with the shorter overall survival of OS patients, while the high expression of FDPS (farnesyl pyrophosphate synthase), IFITM5 (interferon-induced transmembrane protein 5), FKBP11 (FKBP prolyl isomerase 11), SP7, and SQLE (squalene epoxidase) was associated with the shorter overall survival of OS patients. In a further analysis, we compared the expression of ANXA1, FDPS, IFITM5, FKBP11, SP7, SQLE, and TPM1 between the MSCs and high-grade OS cells. Further validation studies using the GSE42352 data set revealed that ANXA1, FKBP11, SP7, and TPM1 were more upregulated in the MSCs than the high-grade OS cells, while FDPS, IFITM5, and SQLE were more downregulated in the MSCs than the high-grade OS cells.

CONCLUSIONS

Our bioinformatics analysis revealed 7 hub genes derived from the specific DEGs between the MSCs and osteoblastic cells. The 7 hub genes may serve as potential prognostic biomarkers for patients with OS.

A Novel Biomarker, FKBP10, for Poor Prognosis Prediction in Patients with Clear Cell Renal Cell Carcinoma

Objective

To screen genes associated with poor prognosis of clear cell renal cell carcinoma (CcRCC) from the public databases HPA (Human Protein Atlas), UALCAN, and GEPIA (Gene Expression Profiling Interactive Analysis) and to investigate the expression of FKBP10 in CcRCC and the effect on prognosis of the patients and the biological behavior of CcRCC cells.

Methods

The tumor tissues and adjacent noncancerous tissues of 42 patients with CcRCC diagnosed and treated in our hospital were collected, and the general information of the patients was recorded. FKBP10 expression in the tissues was determined by qRT-PCR and western blot, and its relationship with general information and prognosis of patients was analyzed. Knockdown or overexpression experiments were carried out with the human proximal tubule epithelial cell line HK-2 and CcRCC cell lines Caki-1, 786-O, ACHN, and A498 to verify the relationship between FKBP10 expression and cell proliferation and adhesion ability using MTT assay and fibronectin adhesion assay, respectively. Western blot was utilized to examine the protein expression level of c-Myc, cyclin D1, and Bcl-2 in the cells.

Results

FKBP10 was highly expressed in CcRCC tissues and cells and was correlated with poor prognosis. In addition, FKBP10 expression was positively correlated with CcRCC tumor size and staging and negatively correlated with tumor differentiation. Moreover, knockdown of FKBP10 significantly inhibited the proliferation of CcRCC cells, notably declined the protein expression of c-Myc, cyclin D1, and Bcl-2, and promoted cell adhesion.

Conclusion

FKBP10 is highly expressed in CcRCC tissues and cells and is associated with poor prognosis in patients. FKBP10 participated in the occurrence and development of CcRCC by promoting cell proliferation and inhibiting apoptosis and adhesion.

Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Carcinoma

Aim

The action of immune checkpoint inhibition (ICI) largely depends on antibody-dependent cellular phagocytosis (ADCP). We thus aim to develop ADCP-based ccRCC risk stratification as both prognostic and therapeutic markers of ICI.

Method

Genomic data from multiple public datasets (TCGA, etc.) were integrated. A cancer-intrinsic ADCP gene set for ccRCC tailored from a recent report was constructed based on the association with prognosis, immune infiltrates, and response to ICI. Therapeutic potential was profiled using genome-drug sensitivity datasets.

Results

ADCP genes were selected from a recent CRISPR/Cas9 screen report. Following a four-module panel based on clinical traits, we generated a six-gene signature (ARPC3, PHF19, FKBP11, MS4A14, KDELR3, and CD1C), which showed a strong correlation with advanced grade and stage and worsened prognosis, with a nomogram showing predictive efficacies of 0.911, 0.845, and 0.867 (AUC) at 1, 3, and 5 years, respectively. Signatures were further dichotomized, and groups with a higher risk score showed a positive correlation with tumor mutation burden, higher expressions of inhibitory checkpoint molecules, and increased antitumor immune infiltrates and were enriched for antitumor immune pathways. The high risk-score group showed better response to ICI and could benefit from TKIs of axitinib, tivozanib, or sorafenib, preferentially in combination, whereas sunitinib and pazopanib would better fit the low risk-score group.

Conclusion

Here we showed a six-gene ADCP signature that correlated with prognosis and immune modulation in ccRCC. The signature-based risk stratification was associated with response to both ICI and tyrosine kinase inhibition in ccRCC.

Susceptibility Genes and Chromosomal Regions Associated With Non-Syndromic Familial Non-Medullary Thyroid Carcinoma: Some Pathogenetic and Diagnostic Keys

Thyroid cancer is the malignant tumor that is increasing most rapidly in the world, mainly at the expense of sporadic papillary thyroid carcinoma. The somatic alterations involved in the pathogenesis of sporadic follicular cell derived tumors are well recognized, while the predisposing alterations implicated in hereditary follicular tumors are less well known. Since the genetic background of syndromic familial non-medullary carcinoma has been well established, here we review the pathogenesis of non-syndromic familial non-medullary carcinoma emphasizing those aspects that may be useful in clinical and pathological diagnosis. Non-syndromic familial non-medullary carcinoma has a complex and heterogeneous genetic basis involving several genes and loci with a monogenic or polygenic inheritance model. Most cases are papillary thyroid carcinoma (classic and follicular variant), usually accompanied by benign thyroid nodules (follicular thyroid adenoma and/or multinodular goiter). The possible diagnostic and prognostic usefulness of the changes in the expression and/or translocation of various proteins secondary to several mutations reported in this setting requires further confirmation. Given that non-syndromic familial non-medullary carcinoma and sporadic non-medullary thyroid carcinoma share the same morphology and somatic mutations, the same targeted therapies could be used at present, if necessary, until more specific targeted treatments become available.