A Novel Prognostic Signature for Survival Prediction and Immune Implication Based on SARS-CoV-2–Related Genes in Kidney Renal Clear Cell Carcinoma

The signature of SARS-CoV-2-related genes predicts the immune therapeutic response and prognosis in breast cancer

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an exceptionally contagious single-stranded RNA virus with a strong positive contagion. The COVID-19 pandemic refers to the swift worldwide dissemination of SARS-CoV-2 infection, which began in late 2019. The COVID-19 epidemic has disrupted many cancer treatments. A few reports indicate that the prevalence of SARS-CoV-2 has disrupted the treatment of breast cancer patients (BCs). However, the role of SARS-CoV-2 in the occurrence and prognosis of BC has not been elucidated. Here, we applied bioinformatics to construct a prognostic signature of SARS-CoV-2-related genes (SCRGs). Specifically, weighted gene co-expression network analysis (WGCNA) was utilized to extract co-expressed genes of differentially expressed genes (DEGs) in breast cancer and SCRGs. Then, we utilized the least absolute shrinkage and selection operator (LASSO) algorithm and univariate regression analysis to screen out three hub genes (DCTPP1, CLIP4 and ANO6) and constructed a risk score model. We further analyzed tumor immune invasion, HLA-related genes, immune checkpoint inhibitors (ICIs), and sensitivity to anticancer drugs in different SARS-CoV-2 related risk subgroups. In addition, we have developed a nomination map to expand clinical applicability. The results of our study indicate that BCs with a high-risk score are linked to negative outcomes, lower immune scores, and reduced responsiveness to anticancer medications. This suggests that the SARS-CoV-2 related signature could be used to guide prognosis assessment and treatment decisions for BCs.

Methylation modifications in tRNA and associated disorders: Current research and potential therapeutic targets

High-throughput sequencing has sparked increased research interest in RNA modifications, particularly tRNA methylation, and its connection to various diseases. However, the precise mechanisms underpinning the development of these diseases remain largely elusive. This review sheds light on the roles of several tRNA methylations (m1A, m3C, m5C, m1G, m2G, m7G, m5U, and Nm) in diverse biological functions, including metabolic processing, stability, protein interactions, and mitochondrial activities. It further outlines diseases linked to aberrant tRNA modifications, related enzymes, and potential underlying mechanisms. Moreover, disruptions in tRNA regulation and abnormalities in tRNA-derived small RNAs (tsRNAs) contribute to disease pathogenesis, highlighting their potential as biomarkers for disease diagnosis. The review also delves into the exploration of drugs development targeting tRNA methylation enzymes, emphasizing the therapeutic prospects of modulating these processes. Continued research is imperative for a comprehensive comprehension and integration of these molecular mechanisms in disease diagnosis and treatment.

Genetic variants affecting mitochondrial function provide further insights for kidney disease

BACKGROUND

Chronic kidney disease (CKD) is a complex disorder that has become a high prevalence global health problem, with diabetes being its predominant pathophysiologic driver. Autosomal genetic variation only explains some of the predisposition to kidney disease. Variations in the mitochondrial genome (mtDNA) and nuclear-encoded mitochondrial genes (NEMG) are implicated in susceptibility to kidney disease and CKD progression, but they have not been thoroughly explored. Our aim was to investigate the association of variation in both mtDNA and NEMG with CKD (and related traits), with a particular focus on diabetes.

METHODS

We used the UK Biobank (UKB) and UK-ROI, an independent collection of individuals with type 1 diabetes mellitus (T1DM) patients.

RESULTS

Fourteen mitochondrial variants were associated with estimated glomerular filtration rate (eGFR) in UKB. Mitochondrial variants and haplogroups U, H and J were associated with eGFR and serum variables. Mitochondrial haplogroup H was associated with all the serum variables regardless of the presence of diabetes. Mitochondrial haplogroup X was associated with end-stage kidney disease (ESKD) in UKB. We confirmed the influence of several known NEMG on kidney disease and function and found novel associations for SLC39A13, CFL1, ACP2 or ATP5G1 with serum variables and kidney damage, and for SLC4A1, NUP210 and MYH14 with ESKD. The G allele of TBC1D32-rs113987180 was associated with higher risk of ESKD in patients with diabetes (OR:9.879; CI95%:4.440-21.980; P = 2.0E-08). In UK-ROI, AGXT2-rs71615838 and SURF1-rs183853102 were associated with diabetic nephropathies, and TFB1M-rs869120 with eGFR.

CONCLUSIONS

We identified novel variants both in mtDNA and NEMG which may explain some of the missing heritability for CKD and kidney phenotypes. We confirmed the role of MT-ND5 and mitochondrial haplogroup H on renal disease (serum variables), and identified the MT-ND5-rs41535848G variant, along with mitochondrial haplogroup X, associated with higher risk of ESKD. Despite most of the associations were independent of diabetes, we also showed potential roles for NEMG in T1DM.

Low expression of fatty acid oxidation related gene ACADM indicates poor prognosis of renal clear cell carcinoma and is related to tumor immune infiltration

This research aims to identify the key fatty acid beta-oxidation (FAO) genes that are altered in kidney renal clear cell carcinoma (KIRC) and to analyze the role of these genes in KIRC. The Gene Expression Omnibus (GEO) and FAO datasets were used to identify these key genes. Wilcoxon rank sum test was used to assess the levels of acyl-CoA dehydrogenase medium chain (ACADM) between KIRC and non-cancer samples. The logistic regression and Wilcoxon rank sum test were used to explore the association between ACADM and clinical features. The diagnostic performance of ACADM for KIRC was assessed using a diagnostic receiver operating characteristic (ROC) curve. The co-expressed genes of ACADM were identified in LinkedOmics database, and their function and pathway enrichment were analyzed. The correlation between ACADM expression level and immune infiltration was analyzed by Gene Set Variation Analysis (GSVA) method. Additionally, the proliferation, migration, and invasion abilities of KIRC cells were assessed after overexpressing ACADM. Following differential analysis and intersection, we identified six hub genes, including ACADM. We found that the expression level of ACADM was decreased in KIRC tissues and had a better diagnostic effect (AUC = 0.916). Survival analysis suggested that patients with decreased ACADM expression had a worse prognosis. According to correlation analysis, a variety of clinical features were associated with the expression level of ACADM. By analyzing the infiltration level of immune cells, we found that ACADM may be related to the enrichment of immune cells. Finally, ACADM overexpression inhibited proliferation, migration, and invasion of KIRC cells. In conclusion, our findings suggest that reduced ACADM expression in KIRC patients is indicative of poor prognosis. These results imply that ACADM may be a diagnostic and prognostic marker for individuals with KIRC, offering a reference for clinicians in diagnosis and treatment.

New Insights into the Link between SARS-CoV-2 Infection and Renal Cancer

Cancer has been described as a risk factor for greater susceptibility to SARS-CoV-2 infection and severe COVID-19, mainly for patients with metastatic disease. Conversely, to that reported for most solid and hematological malignancies, the few available clinical studies reported that the infection did not increase the risk of death in renal cancer patients. The expression on proximal tubular renal cells of the key players in cellular viral uptake, ACE2, TMPRSS2, and NRP1, seems to be the mechanism for the direct kidney injury seen in patients with COVID-19. Interestingly, data from The Cancer Genome Atlas and experimental analyses on various renal cancer cell lines demonstrated that the above-reported receptors/cofactors are maintained by renal cancer cells. However, whether SARS-CoV-2 infection directly kills renal cancer cells or generates enhanced immunogenicity is a question worth investigating. In addition, some researchers have further addressed the topic by studying the expression and prognostic significance of gene signatures related to SARS-CoV-2 infection in renal cancer patients. The emerging data highlights the importance of better understanding the existence of a link between renal cancer and COVID-19 since it could lead to the identification of new prognostic factors and the development of new therapeutic targets in the management of renal cancer patients.

Single Nucleus RNA Sequencing of Remnant Kidney Biopsies and Urine Cell RNA Sequencing Reveal Cell Specific Markers of Covid-19 Acute Kidney Injury

Acute kidney injury (AKI) in COVID-19 patients is associated with high mortality and morbidity. Critically ill COVID-19 patients are at twice the risk of in-hospital mortality compared to non-COVID AKI patients. We know little about the cell-specific mechanism in the kidney that contributes to worse clinical outcomes in these patients. New generation single cell technologies have the potential to provide insights into physiological states and molecular mechanisms in COVID-AKI. One of the key limitations is that these patients are severely ill posing significant risks in procuring additional biopsy tissue. We recently generated single nucleus RNA-sequencing data using COVID-AKI patient biopsy tissue as part of the human kidney atlas. Here we describe this approach in detail and report deeper comparative analysis of snRNAseq of 4 COVID-AKI, 4 reference, and 6 non-COVID-AKI biopsies. We also generated and analyzed urine transcriptomics data to find overlapping COVID-AKI-enriched genes and their corresponding cell types in the kidney from snRNA-seq data. We identified all major and minor cell types and states by using by using less than a few cubic millimeters of leftover tissue after pathological workup in our approach. Differential expression analysis of COVID-AKI biopsies showed pathways enriched in viral response, WNT signaling, kidney development, and cytokines in several nephron epithelial cells. COVID-AKI profiles showed a much higher proportion of altered TAL cells than non-COVID AKI and the reference samples. In addition to kidney injury and fibrosis markers indicating robust remodeling we found that, 17 genes overlap between urine cell COVID-AKI transcriptome and the snRNA-seq data from COVID-AKI biopsies. A key feature was that several of the distal nephron and collecting system cell types express these markers. Some of these markers have been previously observed in COVID-19 studies suggesting a common mechanism of injury and potentially the kidney as one of the sources of soluble factors with a potential role in disease progression.

Translational Statement

The manuscript describes innovation, application and discovery that impact clinical care in kidney disease. First, the approach to maximize use of remnant frozen clinical biopsies to inform on clinically relevant molecular features can augment existing pathological workflow for any frozen tissue without much change in the protocol. Second, this approach is transformational in medical crises such as pandemics where mechanistic insights are needed to evaluate organ injury, targets for drug therapy and diagnostic and prognostic markers. Third, the cell type specific and soluble markers identified and validated can be used for diagnoses or prognoses in AKI due to different etiologies and in multiorgan injury.

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.

Calreticulin as a prognostic biomarker and correlated with immune infiltrate in kidney renal clear cell carcinoma

Background: Calreticulin (CALR) has been investigated in several malignant diseases and is associated with immune-cell infiltration. However, the prognostic value of CALR in kidney renal clear cell carcinoma (KIRC) is still unknown. Methods: Based on the computational analysis, data from 530 KIRC cases and 72 normal kidney samples from The Cancer Genome Atlas (TGCA-KIRC) database were analyzed in this study. The expression of CALR mRNA in pan-cancer and immune infiltrates was analyzed using the Tumor Immune Estimation Resource (TIMER) database. The CALR protein expression was obtained from the UALCAN and Human Protein Atlas (HPA) databases. Survival, functional, and statistical analyses were conducted using R software. Results: The CALR expression was higher in KIRC cases than in normal kidneys. A high CALR expression was correlated with TNM stage, pathological stage, and histological grade. Kaplan-Meier survival analysis showed that a high CALR expression was associated with poor overall survival, disease-specific survival, and progression-free interval. Gene set enrichment analysis (GSEA) indicated that CALR was enriched in IL-6 and IL-2 signaling, interferon signaling, TNF signaling, inflammatory response, apoptosis, and the p53 pathway. CALR is correlated with immune-infiltrating cells. A significant correlation was observed between CALR expression and immunomodulators. Conclusion: We identified CALR as a prognostic biomarker of KIRC. Meanwhile, the CALR expression associated with immune infiltration indicated that CALR might be a potential immunotherapy target for patients with KIRC.

Comprehensive analysis of immune ferroptosis gene in renal clear cell carcinoma: prognosis and influence of tumor microenvironment

OBJECTIVE

We conducted an in-depth study of the immune system and ferroptosis to identify prognostic biomarkers and therapeutic targets for renal clear cell carcinoma.

METHODS

Immune ferroptosis-related differentially expressed genes (IFR-DEGs) were selected from The Cancer Genome Atlas (TCGA). A lasso-Cox risk scoring model was established; its prognostic value was determined using prognostic analysis and single multivariate Cox analysis. Model genes were subjected to subcellular fluorescence localization, mRNA and protein expression analyses, and single-cell RNA sequencing localization analysis. Risk score was analyzed using the immune score, immune infiltrating cell correlation, immune checkpoint, TIDE, and drug sensitivity.

RESULTS

A total of 103 IFR-DEGs were identified; a risk model comprising ACADSB, CHAC1, LURAP1L, and PLA2G6 was established. The survival curve, single multivariate Cox regression, and receiver operating characteristic (ROC) curve analysis showed that the model had good predictive ability (p < 0.05). It was also validated using the validation set and total cohort. Subcellular fluorescence localization revealed that ACADSB, CHAC1, and PLA2G6 were distributed in the cytoplasm and LURAP1L in the nucleus. The mRNA and protein expression trends were consistent. Single-cell RNA sequencing mapping revealed that ACADSB was enriched in distal tubule cell clusters. In the Kidney renal clear cell carcinoma (KIRC) mutation correlation analysis, 1.56% of the patients were found to have genetic alterations; The Spearman correlation analysis of model gene mutations showed that ACADSB was positively correlated with LURAP1L, which may have a synergistic effect; it was negatively correlated with CHAC1 and PLA2G6, and CHAC1 was negatively correlated with LURAP1L, which may have an antagonistic effect. Model and immune correlation analyses found that high-risk patients had significantly higher levels of CD8+ T cells, regulatory T cells (Tregs), immune checkpoints, immune scores, and immune escape than those in low-risk patients. High-risk patients had a higher susceptibility to small-molecule drugs.

CONCLUSION

A novel prognostic model of immune ferroptosis-related genes (ACADSB, CHAC1, LURAP1L, and PLA2G6), which plays an important role in immune infiltration, microenvironment, and immune escape, was constructed. It effectively predicts the survival of patients with KIRC.

Systematic analysis of prognostic and immunologic characteristics associated with coronavirus disease 2019 regulators in acute myeloid leukemia

The coronavirus disease 2019 (COVID-19) pandemic has so far damaged the health of millions and has made the treatment of cancer patients more complicated, and so did acute myeloid leukemia (AML). The current problem is the lack of understanding of their interactions and suggestions of evidence-based guidelines or historical experience for the treatment of such patients. Here, we first identified the COVID-19-related differentially expressed genes (C-DEGs) in AML patients by analyzing RNA-seq from public databases and explored their enrichment pathways and candidate drugs. A total of 76 C-DEGs associated with the progress of AML and COVID-19 infection were ultimately identified, and the functional analysis suggested that there are some shared links between them. Their protein–protein interactions (PPIs) and protein–drug interactions were then recognized by multiple bioinformatics algorithms. Moreover, a COVID-19 gene-associated prognostic model (C-GPM) with riskScore was constructed, patients with a high riskScore had poor survival and apparently immune-activated phenotypes, such as stronger monocyte and neutrophil cell infiltrations and higher immunosuppressants targeting expressions, meaning which may be one of the common denominators between COVID-19 and AML and the reason what complicates the treatment of the latter. Among the study’s drawbacks is that these results relied heavily on publicly available datasets rather than being clinically confirmed. Yet, these findings visualized those C-DEGs’ enrichment pathways and inner associations, and the C-GPM based on them could accurately predict survival outcomes in AML patients, which will be helpful for further optimizing therapies for AML patients with COVID-19 infections.

Construction of an endoplasmic reticulum stress-related gene model for predicting prognosis and immune features in kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is one of the most lethal malignant tumors with a propensity for poor prognosis and difficult treatment. Endoplasmic reticulum (ER) stress served as a pivotal role in the progression of the tumor. However, the implications of ER stress on the clinical outcome and immune features of KIRC patients still need elucidation.

Methods: We identified differentially expressed ER stress-related genes between KIRC specimens and normal specimens with TCGA dataset. Then, we explored the biological function and genetic mutation of ER stress-related differentially expressed genes (DEGs) by multiple bioinformatics analysis. Subsequently, LASSO analysis and univariate Cox regression analysis were applied to construct a novel prognostic model based on ER stress-related DEGs. Next, we confirmed the predictive performance of this model with the GEO dataset and explored the potential biological functions by functional enrichment analysis. Finally, KIRC patients stratified by the prognostic model were assessed for tumor microenvironment (TME), immune infiltration, and immune checkpoints through single-sample Gene Set Enrichment Analysis (ssGSEA) and ESTIMATE analysis.

Results: We constructed a novel prognostic model, including eight ER stress-related DEGs, which could stratify two risk groups in KIRC. The prognostic model and a model-based nomogram could accurately predict the prognosis of KIRC patients. Functional enrichment analysis indicated several biological functions related to the progression of KIRC. The high-risk group showed higher levels of tumor infiltration by immune cells and higher immune scores.

Conclusion: In this study, we constructed a novel prognostic model based on eight ER stress-related genes for KIRC patients, which would help predict the prognosis of KIRC and provide a new orientation to further research studies on personalized immunotherapy in KIRC.

Identification of Ferroptosis-Related Prognostic Signature and Subtypes Related to the Immune Microenvironment for Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

Purpose

To identify molecular clusters associated with ferroptosis and to develop a ferroptosis-related signature for providing novel potential targets for the recurrence-free survival and treatment of breast cancer.

Methods

Ferroptosis-related gene (FRG) signature was constructed by univariate and multivariate Cox regression and least absolute shrinkage and selection operator (LASSO). Receiver operating characteristic curves, Kaplan-Meier survival analysis, principal component analysis, and univariate and multivariate Cox regression analyses in the training and test cohorts were used to evaluate the application of this signature. Quantitative reverse transcriptase-PCR (qRT-PCR) was employed to detect the expression of FRGs in the model. Furthermore, the correlations between the signature and immune microenvironment, somatic mutation, and chemotherapeutic drugs sensitivity were explored.

Results

Internal and external validations affirmed that relapse-free survival differed significantly between the high-risk and low-risk groups. Univariate and multivariate Cox regression analyses indicated that the riskScore was an independent prognostic factor for BRCA. The areas under the curve (AUCs) for predicting 1-, 2-, and 3-year survival in the training and test cohorts were satisfactory. Significant differences were also found in the immune microenvironment and IC50 of chemotherapeutic drugs between different risk groups. Furthermore, we divided patients into three clusters based on 18 FRGs to ameliorate the situation of immunotherapy failure in BRCA.

Conclusions

The FRG signature functions as a robust prognostic predictor of the immune microenvironment and therapeutic response, with great potential to guide individualized treatment strategies in the future.