Chromosome-level genome and population genomics of the intermediate horseshoe bat(Rhinolophus affinis)reveal the molecular basis of virus tolerance in Rhinolophus and echolocation call frequency variation

Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,and unique immune system.Advances in evolutionary biology,supported by high-quality reference genomes and comprehensive whole-genome data,have significantly enhanced our understanding of species origins,speciation mechanisms,adaptive evolutionary processes,and phenotypic diversity.However,genomic research and understanding of the evolutionary patterns of Rhinolophus are severely constrained by limited data,with only a single published genome of R.ferrumequinum currently available.In this study,we constructed a high-quality chromosome-level reference genome for the intermediate horseshoe bat(R.affinis).Comparative genomic analyses revealed potential genetic characteristics associated with virus tolerance in Rhinolophidae.Notably,we observed expansions in several immune-related gene families and identified various genes functionally associated with the SARS-CoV-2 signaling pathway,DNA repair,and apoptosis,which displayed signs of rapid evolution.In addition,we observed an expansion of the major histocompatibility complex class II(MHC-II)region and a higher copy number of the HLA-DQB2 gene in horseshoe bats compared to other chiropteran species.Based on whole-genome resequencing and population genomic analyses,we identified multiple candidate loci(e.g.,GLI3)associated with variations in echolocation call frequency across R.affinis subspecies.This research not only expands our understanding of the genetic characteristics of the Rhinolophus genus but also establishes a valuable foundation for future research.

Construction and validation of somatic mutation-derived long noncoding RNAs signatures of genomic instability to predict prognosis of hepatocellular carcinoma

BACKGROUND Long non-coding RNAs(LncRNAs)have been found to be a potential prognostic factor for cancers,including hepatocellular carcinoma(HCC).Some LncRNAs have been confirmed as potential indicators to quantify genomic instability(GI).Nevertheless,GI-LncRNAs remain largely unexplored.This study established a GI-derived LncRNA signature(GILncSig)that can predict the prognosis of HCC patients.AIM To establish a GILncSig that can predict the prognosis of HCC patients.METHODS Identification of GI-LncRNAs was conducted by combining LncRNA expression and somatic mutation profiles.The GI-LncRNAs were then analyzed for functional enrichment.The GILncSig was established in the training set by Cox regression analysis,and its predictive ability was verified in the testing set and TCGA set.In addition,we explored the effects of the GILncSig and TP53 on prognosis.RESULTS A total of 88 GI-LncRNAs were found,and functional enrichment analysis showed that their functions were mainly involved in small molecule metabolism and GI.The GILncSig was constructed by 5 LncRNAs(miR210HG,AC016735.1,AC116351.1,AC010643.1,LUCAT1).In the training set,the prognosis of high-risk patients was significantly worse than that of low-risk patients,and similar results were verified in the testing set and TCGA set.Multivariate Cox regression analysis and stratified analysis confirmed that the GILncSig could be used as an independent prognostic factor.Receiver operating characteristic curve analysis of the GILncSig showed that the area under the curve(0.773)was higher than the two LncRNA signatures published recently.Furthermore,the GILncSig may have a better predictive performance than TP53 mutation status alone.CONCLUSION We established a GILncSig that can predict the prognosis of HCC patients,which will help to guide prognostic evaluation and treatment decisions.

The evolution of cancer genomic medicine in Japan and the role of the National Cancer Center Japan

The journey to implement cancer genomic medicine(CGM)in oncology practice began in the 1980s,which is considered the dawn of genetic and genomic cancer research.At the time,a variety of activating oncogenic alterations and their functional significance were unveiled in cancer cells,which led to the development of molecular targeted therapies in the 2000s and beyond.Although CGM is still a relatively new discipline and it is difficult to predict to what extent CGM will benefit the diverse pool of cancer patients,the National Cancer Center(NCC)of Japan has already contributed considerably to CGM advancement for the conquest of cancer.Looking back at these past achievements of the NCC,we predict that the future of CGM will involve the following:1)A biobank of paired cancerous and non-cancerous tissues and cells from various cancer types and stages will be developed.The quantity and quality of these samples will be compatible with omics analyses.All biobank samples will be linked to longitudinal clinical information.2)New technologies,such as whole-genome sequencing and artificial intelligence,will be introduced and new bioresources for functional and pharmacologic analyses(e.g.,a patient-derived xenograft library)will be systematically deployed.3)Fast and bidirectional translational research(bench-to-bedside and bedside-to-bench)performed by basic researchers and clinical investigators,preferably working alongside each other at the same institution,will be implemented;4)Close collaborations between academia,industry,regulatory bodies,and funding agencies will be established.5)There will be an investment in the other branch of CGM,personalized preventive medicine,based on the individual's genetic predisposition to cancer.

Conservation genomic investigation of an endangered conifer,Thuja sutchuenensis,reveals low genetic diversity but also low genetic load

Endangered species generally have small populations with low genetic diversity and a high genetic load.Thuja sutchuenensis is an endangered conifer endemic to southwestern China.It was once considered extinct in the wild,but in 1999 was rediscovered.However,little is known about its genetic load.We collected 67 individuals from five wild,isolated T.sutchuenensis populations,and used 636,151 SNPs to analyze the level of genetic diversity and genetic load in T.sutchuenensis to delineate the conservation units of T.sutchuenensis,based on whole transcriptome sequencing data,as well as target capture sequencing data.We found that populations of T.sutchuenensis could be divided into three groups.These groups had low levels genetic diversity and were moderately genetically differentiated.Our findings also indicate that T.sutchuenensis suffered two severe bottlenecks around the Last Glaciation Period and Last Glacial Maximum.Among Thuja species,T.sutchuenensis presented the lowest genetic load and hence might have purged deleterious mutations efficiently through purifying selection.However,distribution of fitness effects analysis indicated a high extinction risk for T.sutchuenensis.Multiple lines of evidence identified three management units for T.sutchuenensis.Although T.sutchuenensis possesses a low genetic load,low genetic diversity,suboptimal fitness,and anthropogenic pressures all present an extinction risk for this rare conifer.This might also hold true for many endangered plant species in the mountains all over the world.

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

Prescreening of large-effect markers with multiple strategies improves the accuracy of genomic prediction

Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.Here,we set the genomic and transcriptomic data as the training population data,using BSLMM,TWAS,and eQTL mapping to prescreen features according to |β_(b)|>0,top 1%of phenotypic variation explained(PVE),expression-associated single nucleotide polymorphisms(eSNPs),and egenes(false discovery rate(FDR)<0.01),where these loci were set as extra fixed effects(named GBLUP-Fix)and random effects(GFBLUP)to improve the prediction accuracy in the validation population,respectively.The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle(LDM),water holding capacity(WHC),shear force(SF),and pH in Huaxi cattle on average from 2.14 to 8.69%,especially the improvement of GFBLUP-TWAS over GBLUP was 13.66%for SF.These methods also captured more genetic variance than GBLUP.Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracyofgenomic prediction.

Multi-omics Approach Reveals Influenza-A Virus Target Genes Associated Genomic,Clinical and Immunological Characteristics in Cancers

Objective To examine the precise function of influenza A virus target genes(IATGs)in malignancy.Methods Using multi-omics data from the TCGA and TCPA datasets,33 tumor types were evaluated for IATGs.IATG expression in cancer cells was analyzed using transcriptome analysis.Copy number variation(CNV)was assessed using GISTICS 2.0.Spearman’s analysis was used to correlate mRNA expression with methylation levels.GSEA was used for the enrichment analysis.Pearson’s correlation analysis was used to examine the association between IATG mRNA expression and IC50.The ImmuCellAI algorithm was used to calculate the infiltration scores of 24 immune cell types.Results In 13 solid tumors,IATG mRNA levels were atypically expressed.Except for UCS,UVM,KICH,PCPG,THCA,CHOL,LAMI,and MESO,most cancers contained somatic IATG mutations.The main types of CNVs in IATGs are heterozygous amplifications and deletions.In most tumors,IATG mRNA expression is adversely associated with methylation.RT-PCR demonstrated that EGFR,ANXA5,CACNA1C,CD209,UVRAG were upregulated and CLEC4M was downregulated in KIRC cell lines,consistent with the TCGA and GTEx data.Conclusion Genomic changes and clinical characteristics of IATGs were identified,which may offer fresh perspectives linking the influenza A virus to cancer.

Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.

Identification of new cotton fiber-quality QTL by multiple genomic analyses and development of markers for genomic breeding

Cotton fiber is one of the main raw materials for the textile industry.In recent years,many cotton fiber quality QTL have been identified,but few were applied in breeding.In this study,a genome wide association study(GWAS)of fiber-quality traits in 265 upland cotton breeding intermediate lines(GhBreeding),combined with genome-wide selective sweep analysis(GSSA)and genomic selection(GS),revealed 25 QTL.Most of these QTL were ignored by only using GWAS.The CRISPR/Cas9 mutants of GhMYB_D13 had shorter fiber,which indicates the credibility of QTL to a certain extent.Then these QTL were verified in other cotton natural populations,5 stable QTL were found having broad potential for application in breeding.Additionally,among these 5 stable QTL,superior genotypes of 4 showed an enrichment in most improved new varieties widely cultivated currently.These findings provide insights for how to identify more QTL through combined multiple genomic analysis to apply in breeding.

Genome-wide association mapping and genomic prediction of stalk rot in two mid-altitude tropical maize populations

Maize stalk rot reduces grain yield and quality.Information about the genetics of resistance to maize stalk rot could help breeders design effective breeding strategies for the trait.Genomic prediction may be a more effective breeding strategy for stalk-rot resistance than marker-assisted selection.We performed a genome-wide association study(GWAS)and genomic prediction of resistance in testcross hybrids of 677 inbred lines from the Tuxpe?o and non-Tuxpe?o heterotic pools grown in three environments and genotyped with 200,681 single-nucleotide polymorphisms(SNPs).Eighteen SNPs associated with stalk rot shared genomic regions with gene families previously associated with plant biotic and abiotic responses.More favorable SNP haplotypes traced to tropical than to temperate progenitors of the inbred lines.Incorporating genotype-by-environment(G×E)interaction increased genomic prediction accuracy.

Alleviative effects of Bacillus coagulans strains on irritable bowel syndrome-unraveling strain specificity through physiological and genomic analysis

The high intraspecies heterogeneity of Baciillus coagulans leads to significant phenotypic differences among different strains.Thus,6 B.coagulans strains were tested in the present study using an irritable bowel syndrome(IBS)animal model to determine whether the IBS-alleviating effects of B.coagulans strains are strain-specific.The results of this study showed that the ingestion of B.coagulans GBI-30,6086,and B.coagulans CCFM1041 significantly alleviated IBS symptoms in mice.In contrast,other B.coagulans strains showed no or limited alleviating effects on IBS symptoms.According to our experimental results,the two main common features of these strains were as follows:1)The resistance of vegetative cells to bile salts,and 2)ability to synthesize specific lipids and secondary metabolites.Screening strains based on these two indicators may greatly reduce costs and provide a basis for mining new functional B.coagulans strains.Our results also suggest that administration of B.coagulans could significantly regulate microbiota dysbiosis in animal models.Moreover,the close relationships between the gut microbiota,gut microbiota metabolites,and IBS were further confirmed in this study.

Genomic signatures of selection,local adaptation and production type characterisation of East Adriatic sheep breeds

Background The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations.Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production(carcass,wool and milk yield).Therefore,eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation(EAS),are generally considered as multipurpose breeds(milk,meat and wool),not specialised for a particular type of production,but known for their robustness and resistance to certain environmental conditions.Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures,decipher their biological and productive functionality,and provide a"genomic"characterization of EAS adaptation and determine its production type.Results We identified positive selection signatures in EAS using several methods based on reduced local variation,linkage disequilibrium and site frequency spectrum(eROHi,iHS,nSL and CLR).Our analyses identified numerous genomic regions and genes(e.g.,desmosomal cadherin and desmoglein gene families)associated with environmental adaptation and economically important traits.Most candidate genes were related to meat/production and health/immune response traits,while some of the candidate genes discovered were important for domestication and evolutionary processes(e.g.,HOXa gene family and FSIP2).These results were also confirmed by GO and QTL enrichment analysis.Conclusions Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type,ultimately providing a new opportunity for future breeding programmes.At the same time,the numerous genes identified will improve our understanding of ruminant(sheep)robustness and resistance in the harsh and specific Mediterranean environment.

Rice-wheat comparative genomics:Gains and gaps

Rice and wheat provide nearly 40%of human calorie and protein requirements.They share a common ancestor and belong to the Poaceae(grass)family.Characterizing their genetic homology is crucial for developing new cultivars with enhanced traits.Several wheat genes and gene families have been characterized based on their rice orthologs.Rice–wheat orthology can identify genetic regions that regulate similar traits in both crops.Rice–wheat comparative genomics can identify candidate wheat genes in a genomic region identified by association or QTL mapping,deduce their putative functions and biochemical pathways,and develop molecular markers for marker-assisted breeding.A knowledge of gene homology facilitates the transfer between crops of genes or genomic regions associated with desirable traits by genetic engineering,gene editing,or wide crossing.

Comparative genomic analysis revealed that dietary habits affected the adaptation of Bifidobacterium bifidum to the intestinal tract in different geographic populations

Recent research on the genome of Bifidobacterium bifidum has mainly focused on the isolation sources(intestinal tract niche)recently,but reports on the isolation region are limited.This study analyzed the differences in the genome of B.bifidum isolated from different geographical populations by comparative genomic analysis.Results at the genome level indicated that the GC content of American isolates was significantly higher than that of Chinese and Russian isolates.The phylogenetic tree,based on 919 core genes showed that B.bifidum might be related to the geographical characteristics of isolation region.Furthermore,functional annotation analysis demonstrated that copy numbers of carbohydrate-active enzymes(CAZys)involved in the degradation of polysaccharide from plant and host sources in B.bifidum were high,and 18 CAZys showed significant differences across different geographical populations,indicating that B.bifidum had adapted to the human intestinal environment,especially in the groups with diets rich in fiber.Dietary habits were one of the main reasons for the differences of B.bifidum across different geographical populations.Additionally,B.bifidum exhibited high diversity,evident in glycoside hydrolases,the CRISPR-Cas system,and prophages.This study provides a genetic basis for further research and development of B.bifidum.

Genomic divergence and mutation load in the Begonia masoniana complex from limestone karsts

Understanding genome-wide diversity,inbreeding,and the burden of accumulated deleterious mutations in small and isolated populations is essential for predicting and enhancing population persistence and resilience.However,these effects are rarely studied in limestone karst plants.Here,we re-sequenced the nuclear genomes of 62 individuals of the Begonia masoniana complex(B.liuyanii,B.longgangensis,B.masoniana and B.variegata)and investigated genomic divergence and genetic load for these four species.Our analyses revealed four distinct clusters corresponding to each species within the complex.Notably,there was only limited admixture between B.liuyanii and B.longgangensis occurring in overlapping geographic regions.All species experienced historical bottlenecks during the Pleistocene,which were likely caused by glacial climate fluctuations.We detected an asymmetric historical gene flow between group pairs within this timeframe,highlighting a distinctive pattern of interspecific divergence attributable to karst geographic isolation.We found that isolated populations of B.masoniana have limited gene flow,the smallest recent population size,the highest inbreeding coefficients,and the greatest accumulation of recessive deleterious mutations.These findings underscore the urgency to prioritize conservation efforts for these isolated population.This study is among the first to disentangle the genetic differentiation and specific demographic history of karst Begonia plants at the whole-genome level,shedding light on the potential risks associated with the accumulation of deleterious mutations over generations of inbreeding.Moreover,our findings may facilitate conservation planning by providing critical baseline genetic data and a better understanding of the historical events that have shaped current population structure of rare and endangered karst plants.

Conservation genomics provides insights into genetic resilience and adaptation of the endangered Chinese hazelnut, Corylus chinensis

Global climate change has increased concerns regarding biodiversity loss.However,many key conservation issues still required further research,including demographic history,deleterious mutation load,adaptive evolution,and putative introgression.Here we generated the first chromosome-level genome of the endangered Chinese hazelnut,Corylus chinensis,and compared the genomic signatures with its sympatric widespread C.kwechowensis-C yunnanensis complex.We found large genome rearrangements across all Corylus species and identified species-specific expanded gene families that may be involved in adaptation.Population genomics revealed that both C.chinensis and the C.kwechowensis-C.yunnanensis complex had diverged into two genetic lineages,forming a consistent pattern of southwestern-northern differentiation.Population size of the narrow southwestern lineages of both species have decreased continuously since the late Miocene,whereas the widespread northern lineages have remained stable(C.chinensis) or have even recovered from population bottlenecks(C.kwechowensis-C.yunnanensis complex) during the Quaternary.Compared with C.kwechowensis-C. yunnanensis complex,C.chinensis showed significantly lower genomic diversity and higher inbreeding level.However,C.chinensis carried significantly fewer deleterious mutations than C.kwechowensis-C. yunnanensis complex,as more effective purging selection reduced the accumulation of homozygous variants.We also detected signals of positive selection and adaptive introgression in different lineages,which facilitated the accumulation of favorable variants and formation of local adaptation.Hence,both types of selection and exogenous introgression could have mitigated inbreeding and facilitated survival and persistence of C.chinensis.Overall,our study provides critical insights into lineage differentiation,local adaptation,and the potential for future recovery of endangered trees.

Metabologenomics and network pharmacology to understand the molecular mechanism of cancer research

In this editorial I comment on the article“Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer”published in the recent issue of the World Journal of Clinical Cases 2023 November 6;11(31):7593-7609.Almost all living forms are able to manufacture particular chemicals-metabolites that enable them to differentiate themselves from one another and to overcome the unique obstacles they encounter in their natural habitats.Numerous methods for chemical warfare,communication,nutrition acquisition,and stress prevention are made possible by these specialized metabolites.Metabolomics is a popular technique for collecting direct mea-surements of metabolic activity from many biological systems.However,con-fusing metabolite identification is a typical issue,and biochemical interpretation is frequently constrained by imprecise and erroneous genome-based estimates of enzyme activity.Metabolite annotation and gene integration uses a biochemical reaction network to obtain a metabolite-gene association so called metabologe-nomics.This network uses an approach that emphasizes metabolite-gene consensus via biochemical processes.Combining metabolomics and genomics data is beneficial.Furthermore,computer networking proposes that using meta-bolomics data may improve annotations in sequenced species and provide testable hypotheses for specific biochemical processes.CONCLUSION The genome and metabolites of biological organisms are not fully characterized with current technologies.However,increasing high-throughput metabolomics and genomics data provide promising generation of paired data sets to understand the molecular mechanism of biochemical processes as well as determining targets for pharmaceutical drug design.Contemporary network infrastructures to integrate omics analysis can provide molecular mechanism of biochemical pathways.Furthermore,clinical data may be integrated to gene expression–metabolite expression by system genetics approach.Calculating pair-wise correlations and weighted correlation network analysis provide the basis of this integration[11-13].The occurrence of strong correlations between classified metabolites and co-expression transcripts implies either various roles of metabolites or linkages between metabolic pathways and the immune system.

Urgent need for prognostic markers for hepatocellular carcinoma in the light of genomic instability and non-coding RNA signatures

In this editorial,we comment on an original article by Duan et al.Despite ad-vancements in the diagnosis and treatment of hepatocellular carcinoma(HCC),the identification of suitable prognostic factors remains challenging.In their paper,Duan et al identified long non-coding RNAs(LncRNAs)to quantify ge-nomic instability(GI)by combining LncRNA expression and somatic mutation profiles.They confirmed that the GI-derived LncRNA signature(GI-LncSig)could be an independent prognostic factor with the area under the curve of 0.773.Fur-thermore,the authors stated that GI-LncSig may have a better predictive perfor-mance than TP53 mutation status alone.However,studies exploring genetic markers for predicting the prognosis of HCC are crucial for identifying thera-peutic targets and enhancing diagnostic and treatment strategies to mitigate the global burden of liver cancer.

Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical,genetic,environmental,and lifestyle factors to optimize medication management.This study investigates how artificial intelligence(AI)and machine learning(ML)can address key challenges in integrating pharmacogenomics(PGx)into psychiatric care.In this integration,AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions.AI-driven models integrating genomic,clinical,and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder.This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry,highlighting the importance of ethical considerations and the need for personalized treatment.Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care.Future research should focus on developing enhanced AI-driven predictive models,privacy-preserving data exchange,and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

Clinical application value of long non-coding RNAs signatures of genomic instability in predicting prognosis of hepatocellular carcinoma

Hepatocellular carcinoma(HCC)presents challenges due to its high recurrence and metastasis rates and poor prognosis.While current clinical diagnostic and prognostic indicators exist,their accuracy remains imperfect due to their biol-ogical complexity.Therefore,there is a quest to identify improved biomarkers for HCC diagnosis and prognosis.By combining long non-coding RNA(lncRNA)expression and somatic mutations,Duan et al identified five representative lncRNAs from 88 lncRNAs related to genomic instability(GI),forming a GI-derived lncRNA signature(LncSig).This signature outperforms previously re-ported LncSig and TP53 mutations in predicting HCC prognosis.In this editorial,we comprehensively evaluate the clinical application value of such prognostic evaluation model based on sequencing technology in terms of cost,time,and practicability.Additionally,we provide an overview of various prognostic models for HCC,aiding in a comprehensive understanding of research progress in pro-gnostic evaluation methods.