Onkopipe: A Snakemake Based DNA-Sequencing Pipeline for Clinical Variant Analysis in Precision Medicine.

NGS is increasingly used in precision medicine, but an automated sequencing pipeline that can detect different types of variants (single nucleotide - SNV, copy number - CNV, structural - SV) and does not rely on normal samples as germline comparison is needed. To address this, we developed Onkopipe, a Snakemake-based pipeline that integrates quality control, read alignments, BAM pre-processing, and variant calling tools to detect SNV, CNV, and SV in a unified VCF format without matched normal samples. Onkopipe is containerized and provides features such as reproducibility, parallelization, and easy customization, enabling the analysis of genomic data in precision medicine. Our validation and evaluation demonstrate high accuracy and concordance, making Onkopipe a valuable open-source resource for molecular tumor boards. Onkopipe is being shared as an open source project and is available at https://gitlab.gwdg.de/MedBioinf/mtb/onkopipe.

Removal of false positives in metagenomics-based taxonomy profiling via targeting Type IIB restriction sites.

Accurate species identification and abundance estimation are critical for the interpretation of whole metagenome sequencing (WMS) data. Yet, existing metagenomic profilers suffer from false-positive identifications, which can account for more than 90% of total identified species. Here, by leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, we present a metagenomic profiler, MAP2B (MetAgenomic Profiler based on type IIB restriction sites), to resolve those issues. We first illustrate the pitfalls of using relative abundance as the only feature in determining false positives. We then propose a feature set to distinguish false positives from true positives, and using simulated metagenomes from CAMI2, we establish a false-positive recognition model. By benchmarking the performance in metagenomic profiling using a simulation dataset with varying sequencing depth and species richness, we illustrate the superior performance of MAP2B over existing metagenomic profilers in species identification. We further test the performance of MAP2B using real WMS data from an ATCC mock community, confirming its superior precision against sequencing depth. Finally, by leveraging WMS data from an IBD cohort, we demonstrate the taxonomic features generated by MAP2B can better discriminate IBD and predict metabolomic profiles.

An efficient cloning method to expand vector and restriction site compatibility of Golden Gate Assembly.

Golden Gate Assembly is an efficient and rapid cloning method but requires dedicated vectors. Here, we modified Golden Gate to expand its compatibility to a broader range of destination vectors while maintaining its strengths. Our Expanded Golden Gate (ExGG) assembly adds to the insert(s) type IIS restriction sites that generate protruding ends compatible with traditional type IIP sites on the recipient vector. The ligated product cannot be cleaved again, owing to a single-base change near the junction. This allows the reaction to proceed in a single tube without an intermediate purification step. ExGG can be used to introduce multiple fragments into a vector simultaneously, including shorter fragments (<100 bp) and fragments with shared sequences, which can be difficult to assemble with other fast cloning strategies. Thus, ExGG extends the convenience of Golden Gate to a much larger space of pre-existing vectors designed for conventional cloning.

A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing.

Carcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. CUP patients typically have poor prognosis but therapy targeting the original cancer tissue can significantly improve patients' prognosis. Thus, it's critical to develop accurate computational methods to infer cancer TOO. While qPCR or microarray-based methods are effective in inferring TOO for most cancer types, the overall prediction accuracy is yet to be improved. In this study, we propose a cross-cohort computational framework to trace TOO of 32 cancer types based on RNA sequencing (RNA-seq). Specifically, we employed logistic regression models to select 80 genes for each cancer type to create a combined 1356-gene set, based on transcriptomic data from 9911 tissue samples covering the 32 cancer types with known TOO from the Cancer Genome Atlas (TCGA). The selected genes are enriched in both tissue-specific and tissue-general functions. The cross-validation accuracy of our framework reaches 97.50% across all cancer types. Furthermore, we tested the performance of our model on the TCGA metastatic dataset and International Cancer Genome Consortium (ICGC) dataset, achieving an accuracy of 91.09% and 82.67%, respectively, despite the differences in experiment procedures and pipelines. In conclusion, we developed an accurate yet robust computational framework for identifying TOO, which holds promise for clinical applications. Our code is available at http://github.com/wangbo00129/classifybysklearn .

M6ATMR: identifying N6-methyladenosine sites through RNA sequence similarity matrix reconstruction guided by Transformer.

Numerous studies have focused on the classification of N6-methyladenosine (m6A) modification sites in RNA sequences, treating it as a multi-feature extraction task. In these studies, the incorporation of physicochemical properties of nucleotides has been applied to enhance recognition efficacy. However, the introduction of excessive supplementary information may introduce noise to the RNA sequence features, and the utilization of sequence similarity information remains underexplored. In this research, we present a novel method for RNA m6A modification site recognition called M6ATMR. Our approach relies solely on sequence information, leveraging Transformer to guide the reconstruction of the sequence similarity matrix, thereby enhancing feature representation. Initially, M6ATMR encodes RNA sequences using 3-mers to generate the sequence similarity matrix. Meanwhile, Transformer is applied to extract sequence structure graphs for each RNA sequence. Subsequently, to capture low-dimensional representations of similarity matrices and structure graphs, we introduce a graph self-correlation convolution block. These representations are then fused and reconstructed through the local-global fusion block. Notably, we adopt iteratively updated sequence structure graphs to continuously optimize the similarity matrix, thereby constraining the end-to-end feature extraction process. Finally, we employ the random forest (RF) algorithm for identifying m6A modification sites based on the reconstructed features. Experimental results demonstrate that M6ATMR achieves promising performance by solely utilizing RNA sequences for m6A modification site identification. Our proposed method can be considered an effective complement to existing RNA m6A modification site recognition approaches.

Identification and validation of biomarkers for epithelial-mesenchymal transition-related cells to estimate the prognosis and immune microenvironment in primary gastric cancer by the integrated analysis of single-cell and bulk RNA sequencing data.

BACKGROUND: The epithelial-mesenchymal transition (EMT) is associated with gastric cancer (GC) progression and immune microenvironment. To better understand the heterogeneity underlying EMT, we integrated single-cell RNA-sequencing (scRNA-seq) data and bulk sequencing data from GC patients to evaluate the prognostic utility of biomarkers for EMT-related cells (ERCs), namely, cancer-associated fibroblasts (CAFs) and epithelial cells (ECs). METHODS: scRNA-seq data from primary GC tumor samples were obtained from the Gene Expression Omnibus (GEO) database to identify ERC marker genes. Bulk GC datasets from the Cancer Genome Atlas (TCGA) and GEO were used as training and validation sets, respectively. Differentially expressed markers were identified from the TCGA database. Univariate Cox, least-absolute shrinkage, and selection operator regression analyses were performed to identify EMT-related cell-prognostic genes (ERCPGs). Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses were adopted to evaluate the prognostic utility of the ERCPG signature. An ERCPG-based nomogram was constructed by integrating independent prognostic factors. Finally, we evaluated the correlations between the ERCPG signature and immune-cell infiltration and verified the expression of ERCPG prognostic signature genes by in vitro cellular assays. RESULTS: The ERCPG signature was comprised of seven genes (COL4A1, F2R, MMP11, CAV1, VCAN, FKBP10, and APOD). Patients were divided into high- and low-risk groups based on the ERCPG risk scores. Patients in the high-risk group showed a poor prognosis. ROC and calibration curves suggested that the ERCPG signature and nomogram had a good prognostic utility. An immune cell-infiltration analysis suggested that the abnormal expression of ERCPGs induced the formation of an unfavorable tumor immune microenvironment. In vitro cellular assays showed that ERCPGs were more abundantly expressed in GC cell lines compared to normal gastric tissue cell lines. CONCLUSIONS: We constructed and validated an ERCPG signature using scRNA-seq and bulk sequencing data from ERCs of GC patients. Our findings support the estimation of patient prognosis and tumor treatment in future clinical practice.

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer.

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted "ComBat" to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package "Seurat" in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan-Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

Repetitive DNA sequence detection and its role in the human genome.

Repetitive DNA sequences playing critical roles in driving evolution, inducing variation, and regulating gene expression. In this review, we summarized the definition, arrangement, and structural characteristics of repeats. Besides, we introduced diverse biological functions of repeats and reviewed existing methods for automatic repeat detection, classification, and masking. Finally, we analyzed the type, structure, and regulation of repeats in the human genome and their role in the induction of complex diseases. We believe that this review will facilitate a comprehensive understanding of repeats and provide guidance for repeat annotation and in-depth exploration of its association with human diseases.

Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM.

BACKGROUND: Quantifying cell-type abundance in bulk tissue RNA-sequencing enables researchers to better understand complex systems. Newer deconvolution methodologies, such as MuSiC, use cell-type signatures derived from single-cell RNA-sequencing (scRNA-seq) data to make these calculations. Single-nuclei RNA-sequencing (snRNA-seq) reference data can be used instead of scRNA-seq data for tissues such as human brain where single-cell data are difficult to obtain, but accuracy suffers due to sequencing differences between the technologies. RESULTS: We propose a modification to MuSiC entitled 'DeTREM' which compensates for sequencing differences between the cell-type signature and bulk RNA-seq datasets in order to better predict cell-type fractions. We show DeTREM to be more accurate than MuSiC in simulated and real human brain bulk RNA-sequencing datasets with various cell-type abundance estimates. We also compare DeTREM to SCDC and CIBERSORTx, two recent deconvolution methods that use scRNA-seq cell-type signatures. We find that they perform well in simulated data but produce less accurate results than DeTREM when used to deconvolute human brain data. CONCLUSION: DeTREM improves the deconvolution accuracy of MuSiC and outperforms other deconvolution methods when applied to snRNA-seq data. DeTREM enables accurate cell-type deconvolution in situations where scRNA-seq data are not available. This modification improves characterization cell-type specific effects in brain tissue and identification of cell-type abundance differences under various conditions.

Quantification of Genetic Heterogeneity Using Long-Read Targeted Individual DNA Molecule Sequencing.

Understanding genetic heterogeneity is of paramount importance in unraveling the intricate functioning of biological systems, as it contributes to the diversity of phenotypes of gene-environment interactions. We have developed a method termed targeted Individual DNA Molecule Sequencing (IDMseq) to accurately quantify genetic heterogeneity within cell populations, even those with rare variants present at low frequencies. IDMseq ensures that each original DNA molecule is distinctively represented by one unique molecule identifier (UMI) group, preventing false UMI groups and enabling precise quantification of allele frequency within the original population. IDMseq is a versatile sequencing technique that combines error correction and long-read sequencing, enabling sensitive detection of various genetic variants, including single nucleotide variants and large structural variants in both basic and clinical research settings. This protocol provides a comprehensive, step-by-step guide to preparing samples and performing IDMseq to determine genetic variations. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: UMI labeling and amplification of DNA Support Protocol 1: AMPure XP beads cleanup Support Protocol 2: Suggested data analysis pipeline.

Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling.

Introduction: Neoantigen-based immunotherapy has emerged as a promising strategy for improving the life expectancy of cancer patients. This therapeutic approach heavily relies on accurate identification of cancer mutations using DNA sequencing (DNAseq) data. However, current workflows tend to provide a large number of neoantigen candidates, of which only a limited number elicit efficient and immunogenic T-cell responses suitable for downstream clinical evaluation. To overcome this limitation and increase the number of high-quality immunogenic neoantigens, we propose integrating RNA sequencing (RNAseq) data into the mutation identification step in the neoantigen prediction workflow. Methods: In this study, we characterize the mutation profiles identified from DNAseq and/or RNAseq data in tumor tissues of 25 patients with colorectal cancer (CRC). Immunogenicity was then validated by ELISpot assay using long synthesis peptides (sLP). Results: We detected only 22.4% of variants shared between the two methods. In contrast, RNAseq-derived variants displayed unique features of affinity and immunogenicity. We further established that neoantigen candidates identified by RNAseq data significantly increased the number of highly immunogenic neoantigens (confirmed by ELISpot) that would otherwise be overlooked if relying solely on DNAseq data. Discussion: This integrative approach holds great potential for improving the selection of neoantigens for personalized cancer immunotherapy, ultimately leading to enhanced treatment outcomes and improved survival rates for cancer patients.

Epigenetic inheritance is unfaithful at intermediately methylated CpG sites.

DNA methylation at the CpG dinucleotide is considered a stable epigenetic mark due to its presumed long-term inheritance through clonal expansion. Here, we perform high-throughput bisulfite sequencing on clonally derived somatic cell lines to quantitatively measure methylation inheritance at the nucleotide level. We find that although DNA methylation is generally faithfully maintained at hypo- and hypermethylated sites, this is not the case at intermediately methylated CpGs. Low fidelity intermediate methylation is interspersed throughout the genome and within genes with no or low transcriptional activity, and is not coordinately maintained between neighbouring sites. We determine that the probabilistic changes that occur at intermediately methylated sites are likely due to DNMT1 rather than DNMT3A/3B activity. The observed lack of clonal inheritance at intermediately methylated sites challenges the current epigenetic inheritance model and has direct implications for both the functional relevance and general interpretability of DNA methylation as a stable epigenetic mark.

Cloning, subcellular localization and antibacterial functional analysis of NK-lysin in yellow drum (Nibea albiflora).

Vibrio harveyi is the primary pathogenic bacteria affecting Nibea albiflora aquaculture. In a previous phase, our laboratory intentionally exposed N. albiflora to V. harveyi and analyzed the outcomes using a combination of genome-wide association study (GWAS) and RNA-seq. The results revealed that the antimicrobial peptide NK-lysin (YdNkl-1) was a candidate gene for resistance to V. harveyi disease in N. albiflora. To investigate the role of the antimicrobial peptide NK-lysin in N. albiflora's antimicrobial immunity, we screened the YdNkl-1 gene from the transcriptome database. The full-length cDNA of YdNkl-1 gene is 508 bp, with an open reading frame (ORF) of 477 bp, encoding 158 amino acids. The deduced amino acid sequence of YdNkl-1 contains a signal peptide (1st-22nd amino acids) and a Saposin B domain (50th-124th amino acids), akin to mammalian NK-lysin. Phylogenetic tree analysis confirmed that the NK-lysin of teleost fish clustered into a single species, and YdNkl-1 was most closely related to Larimichthys crocea. Subcellular localization showed that YdNkl-1 was distributed in cytoplasm and nucleus of yellow drum kidney cells. Furthermore, YdNkl-1 mRNA transcripts were significantly up-regulated in the skin, gill, intestine, head-kidney, liver, and spleen after V. harveyi infection, suggesting a critical role in N. albiflora's defense against V. harveyi infection. Additionally, we purified and observed the YdNkl-1 protein, which exhibited a potent membrane-disrupting effect on V. harveyi, Pseudomonas plecoglossicida, Vibrio parahaemolyticus, Escherichia coli and Bacillus subtilis. These findings underscore the significance of NK-lysin in N. albiflora's resistance to V. harveyi infection and provide new insights into the crucial role of NK-lysin in the innate immunity of teleost fishes.

Interaction of a Dimeric Single-Stranded DNA-Binding Protein (G5P) with DNA Hairpins. A Molecular Beacon Study.

Gene-V protein (G5P/GVP) is a single-stranded (ss)DNA-binding protein (SBP) of bacteriophage f1 that is required for DNA synthesis and repair. In solution, it exists as a dimer that binds two antiparallel ssDNA strands with high affinity in a cooperative manner, forming a left-handed helical protein-DNA filament. Here, we report on fluorescence studies of the interaction of G5P with different DNA oligonucleotides having a hairpin structure (molecular beacon, MB) with a seven base-pair stem (dT24-stem7, dT18-stem7), as well as with DNA oligonucleotides (dT38, dT24) without a defined secondary structure. All oligonucleotides were end-labeled with a Cy3-fluorophore and a BHQ2-quencher. In the case of DNA oligonucleotides without a secondary structure, an almost complete quenching of their strong fluorescence (with about 5% residual intensity) was observed upon the binding of G5P. This implies an exact alignment of the ends of the DNA strand(s) in the saturated complex. The interaction of the DNA hairpins with G5P led to the unzipping of the base-paired stem, as revealed by fluorescence measurements, fluorescence microfluidic mixing experiments, and electrophoretic mobility shift assay data. Importantly, the disruption of ssDNA's secondary structure agrees with the behavior of other single-stranded DNA-binding proteins (SBPs). In addition, substantial protein-induced fluorescence enhancement (PIFE) of the Cy3-fluorescence was observed.

TRPM8 as a Potential Biomarker and Therapeutic Target for Gastric Cancer Identified by a Combination of Text Mining and RNA Sequencing.

INTRODUCTION: Gastric cancer is a well-known malignant tumor that causes millions of deaths worldwide every year. Due to the lack of a specific biomarker for gastric cancer, most patients are diagnosed at an advanced stage of the disease which results in a poor prognosis and a higher death rate. Therefore, novel biomarkers are urgently needed for early diagnosis and to improve the survival rate. METHODS: In this study, we conducted RNA sequencing of tumor samples from 21 patients with gastric cancer. A total of 3192 differentially expressed genes (1589 up-regulated and 1603 down-regulated) were identified. Subsequently, we applied a text-mining algorithm for further analysis of these data and selected 30 representative genes to investigate as candidates for novel biomarkers in gastric cancer. RESULTS: Among these genes, we confirmed transient receptor potential melastatin 8 channels (TRPM8) as a novel biomarker based on Western blot and immunochemistry validation performed on 134 samples. Compared to normal gastric tissue, the tumor tissues exhibited a significantly higher expression level of TRPM8. CONCLUSION: This study provides insights into the underlying role of TRPM8 in cell proliferation. In addition, TRPM8 may be used as a potential therapeutic target for patients with gastric cancer.

Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm.

Chronological age prediction from DNA methylation sheds light on human aging, health, and lifespan. Current clocks are mostly based on linear models and rely upon hundreds of sites across the genome. Here, we present GP-age, an epigenetic non-linear cohort-based clock for blood, based upon 11,910 methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ∼2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with implications for using fragment-level analysis of sequencing data in aging research. By training three independent clocks, we show enrichment of donors with consistent deviation between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.

Investigation of Tumor Heterogeneity Using Integrated Single-Cell RNA Sequence Analysis to Focus on Genes Related to Breast Cancer-, EMT-, CSC-, and Metastasis-Related Markers in Patients with HER2-Positive Breast Cancer.

Human epidermal growth factor receptor 2 (HER2) protein, which is characterized by the amplification of ERBB2, is a molecular target for HER2-overexpressing breast cancer. Many targeted HER2 strategies have been well developed thus far. Furthermore, intratumoral heterogeneity in HER2 cases has been observed with immunohistochemical staining and has been considered one of the reasons for drug resistance. Therefore, we conducted an integrated analysis of the breast cancer single-cell gene expression data for HER2-positive breast cancer cases from both scRNA-seq data from public datasets and data from our cohort and compared them with those for luminal breast cancer datasets. In our results, heterogeneous distribution of the expression of breast cancer-related genes (ESR1, PGR, ERBB2, and MKI67) was observed. Various gene expression levels differed at the single-cell level between the ERBB2-high group and ERBB2-low group. Moreover, molecular functions and ERBB2 expression levels differed between estrogen receptor (ER)-positive and ER-negative HER2 cases. Additionally, the gene expression levels of typical breast cancer-, CSC-, EMT-, and metastasis-related markers were also different across each patient. These results suggest that diversity in gene expression could occur not only in the presence of ERBB2 expression and ER status but also in the molecular characteristics of each patient.

Identification of Long Non-Coding RNA Profiles and Potential Therapeutic Agents for Fibrolamellar Carcinoma Based on RNA-Sequencing Data.

BACKGROUND: Fibrolamellar carcinoma (FLC) is a rare type of liver cancer that primarily affects adolescents and young adults without prior liver disease or viral infections. Patients with FLC generally have non-specific symptoms, are often diagnosed at a later stage, and experience a higher frequency of metastases compared to patients with other liver cancers. A fusion transcript of DNAJB1 and PRKACA, which can lead to increased activity of PKA and cellular proliferation, has been identified in all FLC patients, but the exact mechanism through which FLC develops remains unclear. In this study, we investigated common lncRNA profiles in various FLC samples using bioinformatics analyses. METHODS: We analyzed differentially expressed (DE) lncRNAs from three RNA sequencing datasets. Using lncRNAs and DE mRNAs, we predicted potential lncRNA target genes and performed Gene Ontology (GO) and KEGG analyses with the DE lncRNA target genes. Moreover, we screened for small-molecule compounds that could act as therapeutic targets for FLC. RESULTS: We identified 308 DE lncRNAs from the RNA sequencing datasets. In addition, we performed a trans-target prediction analysis and identified 454 co-expressed pairs in FLC. The GO analysis showed that the lncRNA-related up-regulated mRNAs were enriched in the regulation of protein kinase C signaling and cAMP catabolic processes, while lncRNA-related down-regulated mRNAs were enriched in steroid, retinol, cholesterol, and xenobiotic metabolic processes. The analysis of small-molecule compounds for FLC treatment identified vitexin, chlorthalidone, triamterene, and amiloride, among other compounds. CONCLUSIONS: We identified potential therapeutic targets for FLC, including lncRNA target genes as well as small-molecule compounds that could potentially be used as treatments. Our findings could contribute to furthering our understanding of FLC and providing potential avenues for diagnosis and treatment.

Establishment of a Seven-Gene Signature Associated with CD8+ T Cells through the Utilization of Both Single-Cell and Bulk RNA-Sequencing Techniques in Clear Cell Renal Cell Carcinoma.

Tumor immune microenvironment constituents, such as CD8+ T cells, have emerged as crucial focal points for cancer immunotherapy. Given the absence of reliable biomarkers for clear cell renal cell carcinoma (ccRCC), we aimed to ascertain a molecular signature that could potentially be linked to CD8+ T cells. The differentially expressed genes (DEGs) linked to CD8+ T cells were identified through an analysis of single-cell RNA sequencing (scRNA-seq) data obtained from the Gene Expression Omnibus (GEO) database. Subsequently, immune-associated genes were obtained from the InnateDB and ImmPort datasets and were cross-referenced with CD8+ T-cell-associated DEGs to generate a series of DEGs linked to immune response and CD8+ T cells. Patients with ccRCC from the Cancer Genome Atlas (TCGA) were randomly allocated into testing and training groups. A gene signature was established by conducting LASSO-Cox analysis and subsequently confirmed using both the testing and complete groups. The efficacy of this signature in evaluating immunotherapy response was assessed on the IMvigor210 cohort. Finally, we employed various techniques, including CIBERSORT, ESTIMATE, ssGSEA, and qRT-PCR, to examine the immunological characteristics, drug responses, and expression of the signature genes in ccRCC. Our findings revealed 206 DEGs linked to immune response and CD8+ T cells, among which 65 genes were correlated with overall survival (OS) in ccRCC. A risk assessment was created utilizing a set of seven genes: RARRES2, SOCS3, TNFSF14, XCL1, GRN, CLDN4, and RBP7. The group with a lower risk showed increased expression of CD274 (PD-L1), suggesting a more favorable response to anti-PD-L1 treatment. The seven-gene signature demonstrated accurate prognostic prediction for ccRCC and holds potential as a clinical reference for treatment decisions.

Integrating data from asymmetric multi-models can identify drought-resistant groundnut genotypes for drought hot-spot locations.

Water/drought stress experiments are frequently conducted under imposed stress or rainout shelters, while natural drought hot-spot investigations are rare. The "drought hot spot" in Anantapur, Andhra Pradesh, India, is appropriate for drought stress evaluation due to its hot, arid environment, limited rainfall, with over 50% rainfall variability. According to reports, 30 out of 200 groundnut cultivars in India are supposed to possess drought-tolerant characteristics. However, these cultivars are yet to be evaluated in areas that are prone to drought. This study tested these drought-tolerant genotypes in naturally drought-prone areas of Anantapur under rainfed conditions from Kharif 2017 to 2019. Pod yield and rainfall-use-efficiency (RUE) were measured for these genotypes. Genotype and genotype*environment interactions affected pod yield and RUE (GEI). The AMMI model exhibits significant season-to-season variability within the same area with environmental vectors > 90° angles. GGE biplot suggested the 2018 wet season for drought-resistant cultivar identification. Kadiri5 and GPBD5 were the most drought-tolerant cultivars for cultivation in Anantapur and adjacent regions. These types could also be used to generate drought-tolerant groundnut variants for drought-prone regions.