Gene Selection Research Articles

Development of a robust and generalizable algorithm "gQuant" for accurate normalizer gene selection in qRT-PCR analysis

The emergent role of nucleic acid-based biomarkers—microRNAs(miRNAs), long non-coding RNAs(lncRNAs), and messenger RNAs(mRNAs), is becoming increasingly prominent in disease diagnostics and risk assessment. qRT-PCR is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, an absolute requirement for accurate quantification. Current tools normalizer selection are fraught with statistical limitations and suboptimal graphical user interface for data visualisation. These deficiencies underscore the necessity for a balanced tool tailored to handle qRT-PCR datasets. Addressing the identified challenges, we have developed 'gQuant' tool crafted to address these limitations. We employed voting classifiers that combine predictions from multiple statistical methods. Tool's efficacy was validated through different available and in house data derived from urinary exosomal miRNAs datasets. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas 'gQuant' consistently yielded rankings characterised by lower standard-deviation, reduced covariance, and enhanced kernel density estimation values. Given 'gQuant's' promising performance, normalizer gene identification will be greatly improved, improving precision of gene expression quantification in a variety of research scenarios. The gQuant tool developed for this study is available for public use and can be accessed at [https://github.com/ABHAYHBB/gQuant-Tool]."

UNVEILING THERAPEUTIC TARGETS THROUGH PATHWAY ANALYSIS AND IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES IN ULCERATIVE COLITIS

Objective: This study utilizes integrated bioinformatics to investigate Differentially Expressed Genes (DEGs) and pathways related to ulcerative colitis (UC). Material and Method: Differentially Expressed Genes were identified from UC patients' colonic mucosal samples and controls using GSE13367 and GSE134025 datasets. Differentially Expressed Genes selection utilized GEO2R and Venn diagrams, followed by functional annotation, pathway analysis, PPI determination via the STRING database, and GO/KEGG enrichment analysis using Metascape. Result and Discussion: Analysis unveiled 197 DEGs, with 76 up-regulated and 121 down-regulated genes. Up-regulated genes were enriched in humoral immune response, peptidoglycan binding, and NADPH oxidase complex, while down-regulated genes were linked to inorganic anion transport, transmitter-gated ion channel activity, and integral plasma membrane components. In the PPI network, up-regulated DEGs formed a dense network (75 nodes, 190 edges), indicating significant interactions, whereas down-regulated DEGs formed a less dense network (114 nodes, 63 edges). Five hub genes (CXCR4, CXCL13, CXCL1, MMP3) were identified among the 197 DEGs. These findings provide new insights into UC's causes and offer promise for more effective therapeutic approaches.

Novel Insights into Post-Myocardial Infarction Cardiac Remodeling through Algorithmic Detection of Cell-Type Composition Shifts.

Recent advances in single cell sequencing have led to an increased focus on the role of cell-type composition in phenotypic presentation and disease progression. Cell-type composition research in the heart is challenging due to large, frequently multinucleated cardiomyocytes that preclude most single cell approaches from obtaining accurate measurements of cell composition. Our in silico studies reveal that ignoring cell type composition when calculating differentially expressed genes (DEGs) can have significant consequences. For example, a relatively small change in cell abundance of only 10% can result in over 25% of DEGs being false positives. We have implemented an algorithmic approach that uses snRNAseq datasets as a reference to accurately calculate cell type compositions from bulk RNAseq datasets through robust data cleaning, gene selection, and multi-sample cross-subject and cross-cell-type deconvolution. We applied our approach to cardiomyocyte-specific α1A adrenergic receptor (CM-α1A-AR) knockout mice. 8-12 week-old mice (either WT or CM-α1A-KO) were subjected to permanent left coronary artery (LCA) ligation or sham surgery (n=4 per group). Transcriptomes from the infarct border zones were collected 3 days later and analyzed using our algorithm to determine cell-type abundances, corrected differential expression calculations using DESeq2, and validated these findings using RNAscope. Uncorrected DEGs for the CM-α1A-KO X LCA interaction term featured many cell-type specific genes such as Timp4 (fibroblasts) and Aplnr (cardiomyocytes) and overall GO enrichment for terms pertaining to cardiomyocyte differentiation (P=3.1E-4). Using our algorithm, we observe a striking loss of cardiomyocytes and gain in fibroblasts in the α1A-KO + LCA mice that was not recapitulated in WT + LCA animals, although we did observe a similar increase in macrophage abundance in both conditions. This recapitulates prior results that showed a much more severe heart failure phenotype in CM-α1A-KO + LCA mice. Following correction for cell-type, our DEGs now highlight a novel set of genes enriched for GO terms such as cardiac contraction (P=3.7E-5) and actin filament organization (P=6.3E-5). Our algorithm identifies and corrects for cell-type abundance in bulk RNAseq datasets opening new avenues for research on novel genes and pathways as well as an improved understanding of the role of cardiac cell types in cardiovascular disease.

Positive selection in cilia-related genes may facilitate deep-sea adaptation of Thermocollonia jamsteci

Deep-sea hydrothermal vents are characterized by high hydrostatic pressure, hypoxia, darkness and toxic substances. However, how organisms adapt to such extreme marine ecosystems remain poorly understood. We hypothesize that adaptive evolution plays an essential role in generating novelty for evolutionary adaptation to the deep-sea environment because adaptive evolution has been found to be critical for species origin and evolution. In this project, the chromosome-level genome of the deep-sea hydrothermal vent gastropod T. jamsteci was constructed for the first time to examine molecular mechanisms of its adaptation to the deep-sea environment. The genome size was large (2.54 Gb), ranking at the top of all species in the Vetigastropoda subclass, driven primarily by the bursts of transposable elements (TEs). The transposition of TEs may also trigger chromosomal changes including both inter-chromosomal fusions and intra-chromosomal activities involving chromosome inversions, rearrangements and fusions, as revealed by comparing the genomes of T. jamsteci and its closely related shallow-sea species Gibbula magus. Innovative changes including the expansion of the ABC transporter gene family that may facilitate detoxification, duplication of genes related to endocytosis, immunity, apoptosis, and anti-apoptotic domains that may help T. jamsteci fight against microbial pathogens, were identified. Furthermore, comparative analysis identified positive selection signals in a large number of genes including the hypoxia up-regulated protein 1, which is a chaperone that may promote adaptation of the T. jamsteci to hypoxic deepsea environments, hox2, Rx2, Pax6 and cilia-related genes BBS1, BBS2, BBS9 and RFX4. Notably, because of the critical importance of cilia and IFT in development, positive selection in cilia-related genes may play a critical role in facilitating T. jamsteci to adapt to the high-pressure deep-sea ecosystem. Results from this study thus revealed important molecular clues that may facilitate further research on the adaptation of molluscs to deep-sea hydrothermal vents.

Loss of Lkb1 cooperates with BrafV600E and ultraviolet radiation, increasing melanoma multiplicity and neural-like dedifferentiation.

The mechanisms that work alongside BRAFV600E oncogene in melanoma development, in addition to ultraviolet (UV) radiation (UVR), are of great interest. Analysis of human melanoma tumors [data from The Cancer Genome Atlas (TCGA)] revealed that 50% or more of the samples expressed no or low amounts of serine/threonine protein kinase STK11 (also known as LKB1) protein. Here, we report that, in a mouse model, concomitant neonatal BrafV600E activation and Lkb1 tumor suppressor ablation in melanocytes led to full melanoma development. A single postnatal dose of UVB radiation had no effect on melanoma onset in Lkb1-depleted mice compared with BrafV600E-irradiated mice, but increased tumor multiplicity. In concordance with these findings and previous reports, Lkb1-null irradiated mice exhibited deficient DNA damage repair (DDR). Histologically, tumors lacking Lkb1 were enriched in neural-like tumor morphology. Genetic profiling and gene set enrichment analyses of tumor sample mutated genes indicated that loss of Lkb1 promoted the selection of altered genes associated with neural differentiation processes. Thus, these results suggest that the loss of Lkb1 cooperates with BrafV600E and UVR, impairing the DDR and increasing melanoma multiplicity and neural-like dedifferentiation.

Distinct Genes with Similar Functions Underlie Convergent Evolution in Myotis Bat Ecomorphs.

Convergence offers an opportunity to explore to what extent evolution can be predictable when genomic composition and environmental triggers are similar. Here, we present an emergent model system to study convergent evolution in nature in a mammalian group, the bat genus Myotis. Three foraging strategies-gleaning, trawling, and aerial hawking, each characterized by different sets of phenotypic features-have evolved independently multiple times in different biogeographic regions in isolation for millions of years. To investigate the genomic basis of convergence and explore the functional genomic changes linked to ecomorphological convergence, we sequenced and annotated 17 new genomes and screened 16,426 genes for positive selection and associations between relative evolutionary rates and foraging strategies across 30 bat species representing all Myotis ecomorphs across geographic regions as well as among sister groups. We identify genomic changes that describe both phylogenetic and ecomorphological trends. We infer that colonization of new environments may have first required changes in genes linked to hearing sensory perception, followed by changes linked to fecundity and development, metabolism of carbohydrates, and heme degradation. These changes may be linked to prey acquisition and digestion and match phylogenetic trends. Our findings also suggest that the repeated evolution of ecomorphs does not always involve changes in the same genes but rather in genes with the same molecular functions such as developmental and cellular processes.

Optimization of Gene Selection for Cancer Classification in High-Dimensional Data Using an Improved African Vultures Algorithm

This study presents a novel method, termed RBAVO-DE (Relief Binary African Vultures Optimization based on Differential Evolution), aimed at addressing the Gene Selection (GS) challenge in high-dimensional RNA-Seq data, specifically the rnaseqv2 lluminaHiSeq rnaseqv2 un edu Level 3 RSEM genes normalized dataset, which contains over 20,000 genes. RNA Sequencing (RNA-Seq) is a transformative approach that enables the comprehensive quantification and characterization of gene expressions, surpassing the capabilities of micro-array technologies by offering a more detailed view of RNA-Seq gene expression data. Quantitative gene expression analysis can be pivotal in identifying genes that differentiate normal from malignant tissues. However, managing these high-dimensional dense matrix data presents significant challenges. The RBAVO-DE algorithm is designed to meticulously select the most informative genes from a dataset comprising more than 20,000 genes and assess their relevance across twenty-two cancer datasets. To determine the effectiveness of the selected genes, this study employs the Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classifiers. Compared to binary versions of widely recognized meta-heuristic algorithms, RBAVO-DE demonstrates superior performance. According to Wilcoxon’s rank-sum test, with a 5% significance level, RBAVO-DE achieves up to 100% classification accuracy and reduces the feature size by up to 98% in most of the twenty-two cancer datasets examined. This advancement underscores the potential of RBAVO-DE to enhance the precision of gene selection for cancer research, thereby facilitating more accurate and efficient identification of key genetic markers.

High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution.

We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial-indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000+ cells per run. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC has a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has an observed multiplet rate of less than 1.1% and a cell capture rate of ~50%. Although the assay can accurately profile the whole transcriptome (19,683 human or 21,400 mouse genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest - thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of 89,722 cells across multiple sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate at least 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies across thousands of samples with high data quality.

BLEND: Probabilistic Cellular Deconvolution with Automated Reference Selection.

Cellular deconvolution aims to estimate cell type fractions from bulk transcriptomic and other omics data. Most existing deconvolution methods fail to account for the heterogeneity in cell type-specific (CTS) expression across bulk samples, ignore discrepancies between CTS expression in bulk and cell type reference data, and provide no guidance on cell type reference selection or integration. To address these issues, we introduce BLEND, a hierarchical Bayesian method that leverages multiple reference datasets. BLEND learns the most suitable references for each bulk sample by exploring the convex hulls of references and employs a "bag-of-words" representation for bulk count data for deconvolution. To speed up the computation, we provide an efficient EM algorithm for parameter estimation. Notably, BLEND requires no data transformation, normalization, cell type marker gene selection, or reference quality evaluation. Benchmarking studies on both simulated and real human brain data highlight BLEND's superior performance in various scenarios. The analysis of Alzheimer's disease data illustrates BLEND's application in real data and reference resource integration.

12 Concordance Analysis of Tissue and Circulating Tumor DNA (ctDNA) in Renal Cell Carcinoma (RCC): Insights from a Multimodal Real-World Database

Abstract Background Next-generation sequencing (NGS) of circulating tumor DNA (ctDNA) has emerged as a powerful complement to tissue NGS, offering a noninvasive and serially conductible test. Its application holds promise in enhancing the assessment of spatial and temporal molecular tumor heterogeneity, thus providing valuable insights into cancer progression and treatment response. In this study, we explore the mutational landscape of renal cell carcinoma (RCC) patients through comprehensive profiling of mutations in ctDNA and matched tissue samples, aiming to elucidate the concordance and clinical significance of molecular alterations detected in both circulating and tissue-derived DNA. Methods From the Tempus multimodal database, we retrospectively analyzed de-identified NGS data from patients with RCC that had dual tissue (Tempus xT, 648 genes) and ctDNA testing (Tempus xF, 105 genes). Patients with matched samples (collected +/- 90 days of one another) were included. We evaluated socio-demographic and clinical characteristics and select pathogenic somatic short variants (PSSV) and copy number variants [(amplifications and deletions, two copy number losses (CNL)]. Concordance analyses were restricted to the 105 genes tested on the ctDNA panel and further restricted to short variants, with the exception of amplifications and CNL detected by both xF and xT. Analysis was further stratified by metastatic status (n=260, metastatic, n=120 non-metastatic) prior to collection of both xT and xF. Results Among all patients (n=393), the median age was 61 years, and 71% were male. The patient cohort comprised a diverse population based on race, with 75% white, 12% African American, 4.8% Asian and 8% others. The median time from tissue to blood collection was 21 days (IQR, 7, 39). 67% (n=265) and 68% (n=266) had metastatic disease at the time of tissue and blood collection, respectively. The most common tissue sites were kidney (49%, n=189), bone (11%, n=43), lung (9%, n=34), lymph node (8%, n=29), liver (6%, n=23), and brain/CNS (4%, n=17). Genes harboring the most common PSSV in tissue included VHL (59% n=232), PBRM1 (31%, n=123), SETD2 (23%, n=91), TP53 (14%, n=54), BAP1 (12%, n=46) and TERT (11%, n=45). Genes with common PSSV in ctDNA included TP53 (23%, n=91), VHL (18%, n=69), BAP1 (6%, n=23), PBRM1 (5%, n=21), PTEN (4%, n=15), KRAS (4%, n=14) and NF2 (4%, n=14). The combination of tissue and ctDNA testing increased the detection of mutations (Table 1). There was higher concordance between somatic alterations in select genes among patients with metastases vs. non-metastases, including BAP1 (55.9% vs. 9.1%), TP53 (36.8% vs. 9.1%), VHL (32.3% vs. 12.5%), ARID1A (25% vs. 16.7%), and ATM (25% vs. 0%). Table 1: Detection of genetic mutations in tissue and ctDNA testing Conclusions The analysis conducted in this study highlights the complementary nature of ctDNA profiling alongside tissue-based NGS in RCC, demonstrating an increased detection of mutations. Particularly, we observed a higher concordance between ctDNA and tissue profiling in individuals with metastatic disease, suggesting the potential utility of ctDNA analysis in advanced stages of RCC. Further research is warranted to elucidate how longitudinal ctDNA analysis can delineate biomarkers of response and resistance at both the mutation and ctDNA fraction levels. Understanding these dynamics could offer valuable insights into disease progression and guide personalized treatment strategies for RCC patients. DOD CDMRP Funding: no

Draft Genome of Akame (Lates Japonicus) Reveals Possible Genetic Mechanisms for Long-Term Persistence and Adaptive Evolution with Low Genetic Diversity.

It is known that some endangered species have persisted for thousands of years despite their very small effective population sizes and low levels of genetic polymorphisms. To understand the genetic mechanisms of long-term persistence in threatened species, we determined the whole genome sequences of akame (Lates japonicus), which has survived for a long time with extremely low genetic variations. Genome-wide heterozygosity in akame was estimated to be 3.3 to 3.4 × 10-4/bp, one of the smallest values in teleost fishes. Analysis of demographic history revealed that the effective population size in akame was around 1,000 from 30,000 years ago to the recent past. The relatively high ratio of nonsynonymous to synonymous heterozygosity in akame indicated an increased genetic load. However, a detailed analysis of genetic diversity in the akame genome revealed that multiple genomic regions, including genes involved in immunity, synaptic development, and olfactory sensory systems, have retained relatively high nucleotide polymorphisms. This implies that the akame genome has preserved the functional genetic variations by balancing selection, to avoid a reduction in viability and loss of adaptive potential. Analysis of synonymous and nonsynonymous nucleotide substitution rates has detected signs of positive selection in many akame genes, suggesting adaptive evolution to temperate waters after the speciation of akame and its close relative, barramundi (Lates calcarifer). Our results indicate that the functional genetic diversity likely contributed to the long-term persistence of this species by avoiding the harmful effects of the population size reduction.

Selection and Gene Duplication Associated With High-Elevation Diversification in Pristimantis, the Largest Terrestrial Vertebrate Genus.

The genus Pristimantis diversified in the tropical Andes mountains and is the most speciose genus of terrestrial vertebrates. Pristimantis are notable among frogs in that they thrive at high elevations (>2,000 m) and are direct developers without a tadpole stage. Despite their ecological significance, little is known about the genetic and physiological traits enabling their success. We conducted transcriptomic analysis on seven Pristimantis species sampled across elevations in the Ecuadorean Andes to explore three hypotheses for their success: (i) unique genes are under selection relative to all other frogs, (ii) common selection occurs across all direct developers, or (iii) common selection occurs across all high-elevation frog clades. Comparative analysis with 34 frog species revealed unique positive selection in Pristimantis genes related to aerobic respiration, hemostasis, signaling, cellular transportation of proteins and ions, and immunity. Additionally, we detected positive selection across all direct developers for genes associated with oxygenase activity and metal ion binding. While many genes under selection in Pristimantis were not positively selected in other high-elevation frog species, we identified some shared genes and pathways linked to lipid metabolism, innate immunity, and cellular redox processes. We observed more positive selection in duplicated- versus single-copy genes, while relaxed purifying selection was prevalent in single-copy genes. Notably, copy number of an innate immunity complement gene was positively correlated with Pristimantis species elevation. Our findings contribute novel insights into the genetic basis of adaptation in Pristimantis and provide a foundation for future studies on the evolutionary mechanisms leading to direct development and coping with high elevations.

Allele-Selective Thiomorpholino Antisense Oligonucleotides as a Therapeutic Approach for Fused-in-Sarcoma Amyotrophic Lateral Sclerosis.

Pathogenic variations in the fused in sarcoma (FUS) gene are associated with rare and aggressive forms of amyotrophic lateral sclerosis (ALS). As FUS-ALS is a dominant disease, a targeted, allele-selective approach to FUS knockdown is most suitable. Antisense oligonucleotides (AOs) are a promising therapeutic platform for treating such diseases. In this study, we have explored the potential for allele-selective knockdown of FUS. Gapmer-type AOs targeted to two common neutral polymorphisms in FUS were designed and evaluated in human fibroblasts. AOs had either methoxyethyl (MOE) or thiomorpholino (TMO) modifications. We found that the TMO modification improved allele selectivity and efficacy for the lead sequences when compared to the MOE counterparts. After TMO-modified gapmer knockdown of the target allele, up to 93% of FUS transcripts detected were from the non-target allele. Compared to MOE-modified AOs, the TMO-modified AOs also demonstrated reduced formation of structured nuclear inclusions and SFPQ aggregation that can be triggered by phosphorothioate-containing AOs. How overall length and gap length of the TMO-modified AOs affected allele selectivity, efficiency and off-target gene knockdown was also evaluated. We have shown that allele-selective knockdown of FUS may be a viable therapeutic strategy for treating FUS-ALS and demonstrated the benefits of the TMO modification for allele-selective applications.

A model-free and distribution-free multi-omics integration approach for detecting novel lung adenocarcinoma genes

Detection of important genes affecting lung adenocarcinoma (LUAD) is critical to finding effective therapeutic targets for this highly lethal cancer. However, many existing approaches have focused on single outcomes or phenotypic associations, which may not be as thorough as investigating molecular transcript levels within cells. In this article, we apply a novel multivariate rank-distance correlation-based gene selection procedure (MrDcGene) to LUAD multi-omics data downloaded from The Cancer Genome Atlas (TCGA). MrDcGene provides additional opportunities for detecting novel susceptibility genes as it leverages information from multiple platforms, while efficiently handling challenges such as high dimensionality, low signal-to-noise ratio, unknown distributions, and non-linear structures, etc. Notably, the MrDcGene method is able to detect two different scenarios, i.e., strong association strength with a few gene expressions and weak association strength with several gene expressions. After thoroughly exploring the association between gene expression (GE) and multiple other platforms, including reverse phase protein array (RPPA), miRNA, copy number variation (CNV) and DNA methylation (ME), we detect several novel genes that may play an important role in LUAD (ZNF133, CCDC159, YWHAZ, HNRNPR. ITPR2, PTHLH, and WIPI2). In addition, we quantitatively validate several other susceptibility genes that were reported in the literature using different methods and studies. The accuracy of the MrDcGene approach is theoretically assured and empirically demonstrated by the simulation studies.

Genes Selectively Expressed in Rat Organs.

Understanding organic functions at a molecular level is important for scientists to unveil the disease mechanism and to develop diagnostic or therapeutic methods. The present study tried to find genes selectively expressed in 11 rat organs, including the adrenal gland, brain, colon, duodenum, heart, ileum, kidney, liver, lung, spleen, and stomach. Three normal male Sprague-Dawley (SD) rats were anesthetized, their organs mentioned above were harvested, and RNA in the fresh organs was extracted. Purified RNA was reversely transcribed and sequenced using the Solexa high-throughput sequencing technique. The abundance of a gene was measured by the expected value of fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM). Genes in organs with the highest expression level were sought out and compared with their median value in organs. If a gene in the highest expressed organ was significantly different (p < 0.05) from that in the medianly expressed organ, accompanied by q value < 0.05, and accounted for more than 70% of the total abundance, the gene was assumed as the selective gene in the organ. The Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) pathways were enriched by the highest expressed genes. Based on the criterion, 1,406 selective genes were screened out, 1,283 of which were described in the gene bank and 123 of which were waiting to be described. KEGG and GO pathways in the organs were partly confirmed by the known understandings and a good portion of the pathways needed further investigation. The novel selective genes and organic functional pathways are useful for scientists to unveil the mechanisms of the organs at the molecular level, and the selective genes' products are candidate disease markers for organs.

Ethical Reflections on Preimplantation Genetic Diagnoses

With fertility rates at an all-time low, children have become even more the 'treasures' of their families. Progress in genetic selection technology has made preimplantation genetic diagnosis an increasingly common practice in clinics. However, the practice of purposively selecting genes for future children remains controversial. In this article, the process of preimplantation genetic diagnosis is introduced and related philosophical and social perspectives are reviewed. Finally, the ethics related to this practice are discussed in the contexts of obligation theory, utility theory, and four ethical principles. The authors hope this article sheds light on the diverse perspectives used to consider and discuss the ethical issues surrounding gene selection and, importantly, helps nurses provide care grounded in ethics and humanity in ethically uncertain circumstances.

Identification and validation of basement membrane-associated gene AGRN as prognostic and immune-associated biomarkers in colorectal cancer patients.

Colorectal cancer (COCA) has a poor prognosis, with growing evidence implicating basement membranes (BMs) in cancer progression. Our goal was to investigate the role and predictive significance of BMs in COCA patients. We obtained BMs-related genes from cutting-edge research and used TCGA and GTEx databases for mRNA expression and patient information. Cox regression and LASSO regression were used for prognostic gene selection and risk model construction. We compared prognosis using Kaplan-Meier analysis and examined drug sensitivity differences. The CMAP dataset identified potential small molecule drugs. Invitro tests involved suppressing a crucial gene to observe its impact on tumour metastasis. We developed a 12 BMs-based approach, finding it to be an independent prognostic factor. Functional analysis showed BMs concentrated in cancer-associated pathways, correlating with immune cell infiltration and immune checkpoint activation. High-risk individuals exhibited increased drug sensitivity. AGRN levels were linked to decreased progression-free survival (p < 0.001). AGRN knockdown suppressed tumour growth and metastasis. Our study offers new perspectives on BMs in COCA, concluding that AGRN is a dependable biomarker for patient survival and prognosis.

Whole-transcriptome sequencing revealed the ceRNA regulatory network during the proliferation and differentiation of goose myoblast

The Shitou goose, the largest meat-type goose breed, is an ideal model for offering insights into enhancing meat production efficiency through understanding its genetic regulation of muscle development. Here, through whole-transcriptomic analysis of embryonic leg muscles, we identified 847 differentially expressed genes (DEG), 244 differentially expressed lncRNAs (DEL), 37 differentially expressed circRNAs (DEC), and 84 differentially expressed miRNAs (DEM). Gene ontology (GO) analysis highlighted the significant enrichment of differentially expressed RNAs in muscle structure development, actin filament-based processes, and the actin cytoskeleton pathway. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified pathways associated with the FoxO signaling pathway, AMPK signaling pathway, Wnt signaling pathway and calcium signaling pathway. Furthermore, we utilized Miranda, TargetScan, and miRDB to identify regulatory networks that involve interactions between lncRNA-mRNA, circRNA-mRNA, miRNA-mRNA, lncRNA-miRNA-mRNA, and circRNA-miRNA-mRNA, which regulated the growth and development of skeletal muscle. Notably, differentially expressed genes within the ceRNA network were most significantly enriched in the regulation of actin cytoskeletal organization. Additionally, a lncRNA/circRNA-miRNA-mRNA ceRNA network related to muscle growth and development was constructed based on protein-protein interaction (PPI) analysis and hub genes selection using Cytoscape. This further elucidated the regulatory roles of noncoding RNAs (ncRNA) in the formation of muscle fibers in Shitou goose. In summary, this study provides a valuable transcriptional regulatory network for goose muscle development laying the groundwork for further exploration of the molecular regulatory mechanisms underlying the excellent meat production performance of Shitou goose.

Genomic Profiling of Small Intestine Cancers From a Real-World Data Set Identifies Subgroups With Actionable Alterations.

Panel-based comprehensive genomic profiling (CGP) is used in clinical practice worldwide; however, large real-world data (RWD) of patients with advanced small intestine cancer have not been characterized. We investigated differences in the prevalence of clinically relevant alterations across molecularly defined or age-stratified subgroups. This was a collaborative biomarker study of RWD from CGP testing (Foundation Medicine, Inc). Hybrid capture was conducted on at least 324 cancer-related genes and select introns from up to 31 genes frequently rearranged in cancer. Overall, 1,364 patients with advanced small intestine cancer were available for analyses and were stratified by age (≥40 years/<40 years), microsatellite instability (MSI) status, tumor mutational burden (TMB) status (high ≥10/low <10 Muts/Mb), and select gene alterations. The frequency of alterations was analyzed using a chi-square test with Yate's correction. Genes with frequent alterations included TP53 (59.8%), KRAS (54.8%), APC (27.7%), and CDKN2A (22.4%). Frequent genes with amplifications were MYC (6.7%), MDM2 (5.9%), GATA6 (5.5%), and CCND1 (3.4%). Patients younger than 40 years had significantly lower frequency of APC mutations than those 40 years and older (10.4% v 28.7%; P = .0008). Druggable genomic alterations were detected in 22.3% of patients: BRAF V600E (1.2%), BRCA1 (1.8%), BRCA2 (3.2%), ERBB2 amplification (3.2%), KRAS G12C (3.3%), NTRK1/2/3 fusion (0.07%), MSI-high (7.0%), and TMB-high (12.2%), with no significant differences in the frequency according to age (<40 years v ≥40 years; 22.1% v 22.3%). TMB of 10-20 Mut/Mb was observed in 4.8% of patients, and TMB ≥20 Mut/Mb was seen in 7.3% of the cohort. RWD from clinical panel testing revealed the genomic landscape in small intestine cancer by subgroup. These findings provide insights for the future development of treatments in advanced small intestine cancer.

Three-dimensional matrix stiffness modulates mechanosensitive and phenotypic alterations in oral squamous cell carcinoma spheroids.

Extracellular biophysical cues such as matrix stiffness are key stimuli tuning cell fate and affecting tumor progression in vivo. However, it remains unclear how cancer spheroids in a 3D microenvironment perceive matrix mechanical stiffness stimuli and translate them into intracellular signals driving progression. Mechanosensitive Piezo1 and TRPV4 ion channels, upregulated in many malignancies, are major transducers of such physical stimuli into biochemical responses. Most mechanotransduction studies probing the reception of changing stiffness cues by cells are, however, still limited to 2D culture systems or cell-extracellular matrix models, which lack the major cell-cell interactions prevalent in 3D cancer tumors. Here, we engineered a 3D spheroid culture environment with varying mechanobiological properties to study the effect of static matrix stiffness stimuli on mechanosensitive and malignant phenotypes in oral squamous cell carcinoma spheroids. We find that spheroid growth is enhanced when cultured in stiff extracellular matrix. We show that the protein expression of mechanoreceptor Piezo1 and stemness marker CD44 is upregulated in stiff matrix. We also report the upregulation of a selection of genes with associations to mechanoreception, ion channel transport, extracellular matrix organization, and tumorigenic phenotypes in stiff matrix spheroids. Together, our results indicate that cancer cells in 3D spheroids utilize mechanosensitive ion channels Piezo1 and TRPV4 as means to sense changes in static extracellular matrix stiffness, and that stiffness drives pro-tumorigenic phenotypes in oral squamous cell carcinoma.