The blackish cicada (Cryptotympana atrata) exhibits unique characteristics and is one of the model cicadas found in the Korean Peninsula. It is a species of southern origin, prefers high temperatures, and is listed as a climate-sensitive indicator species in South Korea. Therefore, this species can be utilized to study the impact of climate change on the genetic diversity and structure of populations. However, research on the genome of C. atrata is limited. We sequenced the genome of an individual collected from South Korea and constructed a draft genome. Additionally, we collected ten specimens from each of the five regions in South Korea and identified single nucleotide variants (SNVs) for population genetic analysis. The sequencing library was constructed using the MGIEasy DNA Library Prep Kit and sequenced using the MGISEQ-2000 platform with 150-bp paired-end reads. The draft genome of C. atrata was approximately 5.0 Gb or 5.2 Gb, making it one of the largest genomes among insects. Population genetic analysis, which was conducted on four populations in South Korea, including both previously distributed and newly expanded regions, showed that Jeju Island, a remote southern island with the highest average temperature, formed an independent genetic group. However, there were no notable genetic differences among the inland populations selected based on varying average temperatures, indicating that the current population genetic composition on the Korean Peninsula is more reflective of biogeographic history rather than climate-induced genetic structures. Additionally, we unexpectedly observed that most individuals of C. atrata collected in a specific locality were infected with microbes not commonly found in insects, necessitating further research on the pathogens within C. atrata. This study introduces the draft genome of C. atrata, a climate-sensitive indicator species in South Korea. Population analysis results indicate that the current genetic structure of C. atrata is driven by biogeographic history rather than just climate. The prevalence of widespread pathogen infections raises concerns about their impact on C. atrata. Considering the scarcity of publicly available genomic resources related to the family Cicadidae, this draft genome and population data of C. atrata are expected to serve as a valuable resource for various studies utilizing cicada genomes.

IntroductionOsteonecrosis of the Femoral Head (ONFH) is one of the common refractory diseases. However, the role of cuproptosis in ONFH pathogenesis remains unexplored. This study aimed to investigate the potential relationship between cuproptosis and ONFH.MethodsONFH-related datasets were obtained from the Gene Expression Omnibus (GEO) database, and cuproptosis-related genes in the GSE123568 dataset were identified through differential expression analysis. To further discover potential cuproptosis-related biomarkers, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and Support Vector Machine (SVM) analysis were conducted. The Receiver Operating Characteristic (ROC) curve analysis was used to explore the diagnostic value of cuproptosis-related biomarkers. The summary Statistics-based Mendelian Randomization (SMR) algorithm was used to investigate the causal relationship between the related genes and ONFH. The immune infiltration analysis was conducted to assess the effect of immune cells on ONFH. Subsequently, the GSE74089 and GSE89587 datasets were used to validate gene expression levels and predict the lncRNA-miRNA-mRNA network. Finally, quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) was employed to validate the expression of these genes.ResultsThe study showed that the upregulation of PDHB, a cuproptosis-related biomarker, may contribute to the development of ONFH. Additionally, immune cells were found to play a crucial role in ONFH, and PDHB showed a significant association with various immune cells. Furthermore, the study identified the existence of the MIR22HG/let-7c-5p/PDHB regulatory pathway, which may play a critical role in ONFH through cuproptosis.DiscussionThis study discovered a cuproptosis-related regulating pathway, MIR22HG/let-7c-5p/PDHB. This can provide new insights into the treatment of ONFH. However, further experimental validation is needed.ConclusionPDHB, identified as a cuproptosis-related biomarker, can induce ONFH through cuproptosis. PDHB also contributes to the pathogenesis and progression of ONFH by influencing immune cell function. This is most likely mediated through the regulatory interaction between MIR22HG, let-7c-5p, and PDHB.

Precision medicine is a landmark strategy that has been changing the future of health care through matching treatment plans with each individual patient’s needs and requirements. It permits the discovery of certain genetic abnormalities that cause tumors in cancer research, resulting in tailored medicines and better outcomes. The new drug development process is facilitated by precision medicine, focusing on biomarkers and patient classification because they allow for faster identification of new treatments. Emerging trends in omics technologies and Artificial Intelligence for data processing have patient-centered telemedicine applications. Ethical and privacy issues are addressed, focusing on data security and informed consent. The additional development of precision medicine offers hope for bridging gaps in healthcare delivery systems, addressing rare disease challenges, and promoting global healthcare initiatives. The revolutionizing nature of healthcare and improved patient outcomes can only be fully realized through acceptance and support of precision medicine to its fullest extent. This review evaluates various applications of precision medicine with an emphasis on how it could potentially change the paradigm of cancer research.

Neurodegenerative diseases, including Alzheimer’s and Parkinson’s disease, are characterized by the pathological aggregation of proteins such as amyloid-β, tau, and alpha-synuclein. These hallmark proteins play central roles in disease progression and represent promising targets for therapeutic intervention. Advances in precision medicine, driven by genomic technologies such as CRISPR-Cas systems, RNA-based therapies, and high-throughput sequencing, have enabled the development of tailored strategies to modulate these pathological pathways. This review examines the integration of genomic approaches in targeting amyloid-β, tau, and alpha-synuclein, emphasizing their potential to mitigate disease progression and improve patient outcomes. We highlight current progress in preclinical and clinical studies, discuss challenges associated with translating these therapies into clinical practice, and explore future directions for achieving therapeutic precision in neurodegenerative disorders. By examining the interplay of genetic, molecular, and therapeutic innovations, this review underscores the transformative potential of genomic medicine in addressing the unmet needs of neurodegenerative disease treatment.

Targeted therapies have improved the clinical outcomes of most patients with cancer. However, the heterogeneity of gastric cancer remains a major hurdle for precision treatment. Further investigations into tumor microenvironment heterogeneity are required to resolve these problems. In this study, bioinformatic analyses, including metabolism analysis, pathway enrichment, differentiation trajectory inference, regulatory network construction, and survival analysis, were applied to gain a comprehensive understanding of tumor microenvironment biology within gastric cancer using single-cell RNA-seq and public datasets and experiments were carried out to confirm the conclusions of these analyses. We profiled heterogeneous single-cell atlases and identified eight cell populations with differential expression patterns. We identified two cancer-associated fibroblasts (CAFs) subtypes, with particular emphasis on the role of inflammatory cancer-associated fibroblasts (iCAFs) in EMT and lipid metabolic crosstalk within the tumor microenvironment. Notably, we detected two differentiation states of iCAFs that existed in different tissues with discrepant expression of genes involved in immuno-inflammation or ECM remodeling. Moreover, investigation of tumor-infiltrating myeloid cells has revealed the functional diversity of myeloid cell lineages in gastric cancer. Of which a proliferative cell lineage named C1QC+MKI67+TAMs was recognized with high immunosuppressive capacities, suggesting it has immune suppression and cell proliferation functions in the tumor niche. Finally, we explored regulatory networks based on ligand-receptor pairs and found crucial pro-tumor crosstalk between CAFs and myeloid cells in the tumor microenvironment (TME). These findings provide insights for future cancer treatments and drug discovery.

Understanding the genetics of susceptibility to classical Hodgkin lymphoma (cHL) is considerably limited compared to other cancers due to the rare Hodgkin and Reed-Sternberg (HRS) tumor cells, which coexist with the predominant non-malignant microenvironment. This article offers insights into genetic abnormalities in cHL, as well as nucleotide variants and their associated target genes, elucidated through recent technological advancements. Oncogenomes in HRS cells highlight the survival and proliferation of these cells through hyperactive signaling in specific pathways (e.g., NF-kB) and their interplay with microenvironmental cells (e.g., CD4+ T cells). In contrast, the susceptibility genes identified from genome-wide association studies and expression quantitative trait locus analyses only vaguely implicate their potential roles in susceptibility to more general cancers. To pave the way for the era of precision oncology, more intensive efforts are imperative, employing the following strategies: exploring genetic heterogeneity by gender and cHL subtype, investigating colocalization with various types of expression quantitative trait loci, and leveraging single-cell analysis. These approaches provide valuable perspectives for unraveling the genetic complexities of cHL.

Fungal infections are considered a great threat to human life and are associated with high mortality and morbidity, especially in immunocompromised individuals. Fungal pathogens employ various defense mechanisms to evade the host immune system, which causes severe infections. The available repertoire of drugs for the treatment of fungal infections includes azoles, allylamines, polyenes, echinocandins, and antimetabolites. However, the development of multidrug and pandrug resistance to available antimycotic drugs increases the need to develop better treatment approaches. In this new era of -omics, bioinformatics has expanded options for treating fungal infections. This review emphasizes how bioinformatics complements the emerging strategies, including advancements in drug delivery systems, combination therapies, drug repurposing, epitope-based vaccine design, RNA-based therapeutics, and the role of gut-microbiome interactions to combat anti-fungal resistance. In particular, we focused on computational methods that can be useful to obtain potent hits, and that too in a short period.

Popular gene set enrichment analysis approaches assumed that genes in the gene set contributed to the statistics equally. However, the genes in the transcription factors (TFs) derived gene sets, or gene sets constructed by TF targets identified by the ChIP-Seq experiment, have a rank attribute, as each of these genes have been assigned with a p-value which indicates the true or false possibilities of the ownerships of the genes belong to the gene sets. Ignoring the rank information during the enrichment analysis will lead to improper statistical inference. We address this issue by developing of new method to test the significance of ranked gene sets in genome-wide transcriptome profiling data. A method was proposed by first creating ranked gene sets and gene lists and then applying weighted Kendall's tau rank correlation statistics to the test. After introducing top-down weights to the genes in the gene set, a new software called "Flaver" was developed. Theoretical properties of the proposed method were established, and its differences over the GSEA approach were demonstrated when analyzing the transcriptome profiling data across 55 human tissues and 176 human cell-lines. The results indicated that the TFs identified by our method have higher tendency to be differentially expressed across the tissues analyzed than its competitors. It significantly outperforms the well-known gene set enrichment analyzing tools, GOStats (9%) and GSEA (17%), in analyzing well-documented human RNA transcriptome datasets. The method is outstanding in detecting gene sets of which the gene ranks were correlated with the expression levels of the genes in the transcriptome data.

IntroductionColorectal cancers are the world’s third most commonly diagnosed type of cancer. Currently, there are several diagnostic and treatment options to combat it. However, a delay in detection of the disease is life-threatening. Additionally, a thorough analysis of the exomes of cancers reveals potential variation data that can be used for early disease prognosis.MethodsBy utilizing a comprehensive computational investigation, the present study aimed to reveal mutations that could potentially predispose to colorectal cancer. Ten colorectal cancer exomes were retrieved. Quality control assessments were performed using FastQC and MultiQC, gapped alignment to the human reference genome (hg19) using Bowtie2 and calling the germline variants using Haplotype caller in the GATK pipeline. The variants were filtered and annotated using SIFT and PolyPhen2 successfully categorized the mutations into synonymous, non-synonymous, start loss and stop gain mutations as well as marked them as possibly damaging, probably damaging and benign. This mutational profile helped in shortlisting frequently occurring mutations and associated genes, for which the downstream multi-dimensional expression analyses were carried out.ResultsOur work involved prioritizing the non-synonymous, deleterious SNPs since these polymorphisms bring about a functional alteration to the phenotype. The top variations associated with their genes with the highest frequency of occurrence included LGALS8, CTSB, RAD17, CPNE1, OPRM1, SEMA4D, MUC4, PDE4DIP, ELN and ADRA1A. An in-depth multi-dimensional downstream analysis of all these genes in terms of gene expression profiling and analysis and differential gene expression with regard to various cancer types revealed CTSB and CPNE1 as highly expressed and overregulated genes in colorectal cancer.ConclusionOur work provides insights into the various alterations that might possibly lead to colorectal cancer and suggests the possibility of utilizing the most important genes identified for wet-lab experimentation.

BackgroundCalebin-A is a minor phytoconstituent of turmeric known for its activity against inflammation, oxidative stress, cancerous, and metabolic disorders like Non-alcoholic fatty liver disease(NAFLD). Based on bioinformatic tools. Subsequently, the details of the interaction of critical proteins with Calebin-A were investigated using the molecular docking technique.MethodsWe first probed the intersection of genes/ proteins between NAFLD and Calebin-A through online databases. Besides, we performed an enrichment analysis using the ClueGO plugin to investigate signaling pathways and gene ontology. Next, we evaluate the possible interaction of Calebin-A with significant hub proteins involved in NAFLD through a molecular docking study.ResultsWe identified 87 intersection genes Calebin-A targets associated with NAFLD. PPI network analysis introduced 10 hub genes (TP53, TNF, STAT3, HSP90AA1, PTGS2, HDAC6, ABCB1, CCT2, NR1I2, and GUSB). In KEGG enrichment, most were associated with Sphingolipid, vascular endothelial growth factor A (VEGFA), C-type lectin receptor, and mitogen-activated protein kinase (MAPK) signaling pathways. The biological processes described in 87 intersection genes are mostly concerned with regulating the apoptotic process, cytokine production, and intracellular signal transduction. Molecular docking results also directed that Calebin-A had a high affinity to bind hub proteins linked to NAFLD.ConclusionHere, we showed that Calebin-A, through its effect on several critical genes/ proteins and pathways, might repress the progression of NAFLD.