Molecular Basis for Control of Diverse Genome Stability Factors by the Multi-BRCT Scaffold Rtt107.

BRCT domains support myriad protein-protein interactions involved in genome maintenance. Although di-BRCT recognition of phospho-proteins is well known to support the genotoxic response, whether multi-BRCT domains can acquire distinct structures and functions is unclear. Here we present the tetra-BRCT structures from the conserved yeast protein Rtt107 in free and ligand-bound forms. The four BRCT repeats fold into a tetrahedral structure that recognizes unmodified ligands using a bi-partite mechanism, suggesting repeat origami enabling function acquisition. Functional studies show that Rtt107 binding of partner proteins of diverse activities promotes genome replication and stability in both distinct and concerted manners. A unified theme is that tetra- and di-BRCT domains of Rtt107 collaborate to recruit partner proteins to chromatin. Our work thus illustrates how a master regulator uses two types of BRCT domains to recognize distinct genome factors and direct them to chromatin for constitutive genome protection.

MACSPI enables tissue-selective proteomic and interactomic analyses in multicellular organisms.

Multicellular organisms are composed of many tissue types that have distinct morphologies and functions, which are largely driven by specialized proteomes and interactomes. To define the proteome and interactome of a specific type of tissue in an intact animal, we developed a localized proteomics approach called Methionine Analog-based Cell-Specific Proteomics and Interactomics (MACSPI). This method uses the tissue-specific expression of an engineered methionyl-tRNA synthetase to label proteins with a bifunctional amino acid 2-amino-5-diazirinylnonynoic acid in selected cells. We applied MACSPI in Caenorhabditis elegans, a model multicellular organism, to selectively label, capture, and profile the proteomes of the body wall muscle and the nervous system, which led to the identification of tissue-specific proteins. Using the photo-cross-linker, we successfully profiled HSP90 interactors in muscles and neurons and identified tissue-specific interactors and stress-related interactors. Our study demonstrates that MACSPI can be used to profile tissue-specific proteomes and interactomes in intact multicellular organisms.

Direct Phosphorylation of SRC Homology 3 Domains by Tyrosine Kinase Receptors Disassembles Ligand-Induced Signaling Networks.

Phosphotyrosine (pTyr) signaling has evolved into a key cell-to-cell communication system. Activated receptor tyrosine kinases (RTKs) initiate several pTyr-dependent signaling networks by creating the docking sites required for the assembly of protein complexes. However, the mechanisms leading to network disassembly and its consequence on signal transduction remain essentially unknown. We show that activated RTKs terminate downstream signaling via the direct phosphorylation of an evolutionarily conserved Tyr present in most SRC homology (SH) 3 domains, which are often part of key hub proteins for RTK-dependent signaling. We demonstrate that the direct EPHA4 RTK phosphorylation of adaptor protein NCK SH3s at these sites results in the collapse of signaling networks and abrogates their function. We also reveal that this negative regulation mechanism is shared by other RTKs. Our findings uncover a conserved mechanism through which RTKs rapidly and reversibly terminate downstream signaling while remaining in a catalytically active state on the plasma membrane.

Mutational biases favor complexity increases in protein interaction networks after gene duplication.

Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency.

GATDE: A graph attention network with diffusion-enhanced protein-protein interaction for cancer classification.

Cancer classification is crucial for effective patient treatment, and recent years have seen various methods emerge based on protein expression levels. However, existing methods oversimplify by assuming uniform interaction strengths and neglecting intermediate influences among proteins. Addressing these limitations, GATDE employs a graph attention network enhanced with diffusion on protein-protein interactions. By constructing a weighted protein-protein interaction network, GATDE captures the diversity of these interactions and uses a diffusion process to assess multi-hop influences between proteins. This information is subsequently incorporated into the graph attention network, resulting in precise cancer classification. Experimental results on breast cancer and pan-cancer datasets demonstrate that GATDE surpasses current leading methods. Additionally, in-depth case studies further validate the effectiveness of the diffusion process and the attention mechanism, highlighting GATDE's robustness and potential for real-world applications.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology.

PURPOSE: Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. METHODS: To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. RESULTS: We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. CONCLUSION: Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

Shared Proteins and Pathways of Cardiovascular and Cognitive Diseases: Relation to Vascular Cognitive Impairment.

One of the primary goals of systems medicine is the detection of putative proteins and pathways involved in disease progression and pathological phenotypes. Vascular cognitive impairment (VCI) is a heterogeneous condition manifesting as cognitive impairment resulting from vascular factors. The precise mechanisms underlying this relationship remain unclear, which poses challenges for experimental research. Here, we applied computational approaches like systems biology to unveil and select relevant proteins and pathways related to VCI by studying the crosstalk between cardiovascular and cognitive diseases. In addition, we specifically included signals related to oxidative stress, a common etiologic factor tightly linked to aging, a major determinant of VCI. Our results show that pathways associated with oxidative stress are quite relevant, as most of the prioritized vascular cognitive genes and proteins were enriched in these pathways. Our analysis provided a short list of proteins that could be contributing to VCI: DOLK, TSC1, ATP1A1, MAPK14, YWHAZ, CREB3, HSPB1, PRDX6, and LMNA. Moreover, our experimental results suggest a high implication of glycative stress, generating oxidative processes and post-translational protein modifications through advanced glycation end-products (AGEs). We propose that these products interact with their specific receptors (RAGE) and Notch signaling to contribute to the etiology of VCI.

Host cell protein networks as a novel co-elution mechanism during protein A chromatography.

Host cell proteins (HCPs) are process-related impurities of therapeutic proteins produced in for example, Chinese hamster ovary (CHO) cells. Protein A affinity chromatography is the initial capture step to purify monoclonal antibodies or Fc-based proteins and is most effective for HCP removal. Previously proposed mechanisms that contribute to co-purification of HCPs with the therapeutic protein are either HCP-drug association or leaching from chromatin heteroaggregates. In this study, we analyzed protein A eluates of 23 Fc-based proteins by LC-MS/MS to determine their HCP content. The analysis revealed a high degree of heterogeneity in the number of HCPs identified in the different protein A eluates. Among all identified HCPs, the majority co-eluted with less than three Fc-based proteins indicating a drug-specific co-purification for most HCPs. Only ten HCPs co-purified with over 50% of the 23 Fc-based proteins. A correlation analysis of HCPs identified across multiple protein A eluates revealed their co-elution as HCP groups. Functional annotation and protein interaction analysis confirmed that some HCP groups are associated with protein-protein interaction networks. Here, we propose an additional mechanism for HCP co-elution involving protein-protein interactions within functional networks. Our findings may help to guide cell line development and to refine downstream purification strategies.

Transcriptome-based network analysis reveals hub immune genes and pathways of hepatopancreas against LPS in Amphioctopus fangsiao.

The hepatopancreas is the biggest digestive organ in Amphioctopus fangsiao (A. fangsiao), but also undertakes critical functions like detoxification and immune defense. Generally, pathogenic bacteria or endotoxin from the gut microbiota would be arrested and detoxified in the hepatopancreas, which could be accompanied by the inevitable immune responses. In recent years, studies related to cephalopods immune have been increasing, but the molecular mechanisms associated with the hepatopancreatic immunity are still unclear. In this study, lipopolysaccharide (LPS), a major component of the cell wall of Gram-negative bacteria, was used for imitating bacteria infection to stimulate the hepatopancreas of A. fangsiao. To investigate the immune process happened in A. fangsiao hepatopancreas, we performed transcriptome analysis of hepatopancreas tissue after LPS injection, and identified 2615 and 1943 differentially expressed genes (DEGs) at 6 and 24 h post-injection, respectively. GO and KEGG enrichment analysis showed that these DEGs were mainly involved in immune-related biological processes and signaling pathways, including ECM-receptor interaction signaling pathway, Phagosome signaling pathway, Lysosome signaling pathway, and JAK-STAT signaling pathways. The function relationships between these DEGs were further analyzed through protein-protein interaction (PPI) networks. It was found that Mtor, Mapk14 and Atm were the three top interacting DEGs under LPS stimulation. Finally, 15 hub genes involving multiple KEGG signaling pathways and PPI relationships were selected for qRT-PCR validation. In this study, for the first time we explored the molecular mechanisms associated with hepatopancreatic immunity in A. fangsiao using a PPI networks approach, and provided new insights for understanding hepatopancreatic immunity in A. fangsiao.

Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer.

Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer.

SONAR Discovers RNA-Binding Proteins from Analysis of Large-Scale Protein-Protein Interactomes.

RNA metabolism is controlled by an expanding, yet incomplete, catalog of RNA-binding proteins (RBPs), many of which lack characterized RNA binding domains. Approaches to expand the RBP repertoire to discover non-canonical RBPs are currently needed. Here, HaloTag fusion pull down of 12 nuclear and cytoplasmic RBPs followed by quantitative mass spectrometry (MS) demonstrates that proteins interacting with multiple RBPs in an RNA-dependent manner are enriched for RBPs. This motivated SONAR, a computational approach that predicts RNA binding activity by analyzing large-scale affinity precipitation-MS protein-protein interactomes. Without relying on sequence or structure information, SONAR identifies 1,923 human, 489 fly, and 745 yeast RBPs, including over 100 human candidate RBPs that contain zinc finger domains. Enhanced CLIP confirms RNA binding activity and identifies transcriptome-wide RNA binding sites for SONAR-predicted RBPs, revealing unexpected RNA binding activity for disease-relevant proteins and DNA binding proteins.

Integrated analysis of gut microbiota and metabolomic profiling in colorectal cancer metastasis.

Colorectal cancer (CRC) is characterized by its heterogeneity and complex metastatic mechanisms, presenting significant challenges in treatment and prognosis. This study aimed to unravel the intricate interplay between the gut microbiota and metabolic alterations associated with CRC metastasis. By employing high-throughput sequencing and advanced metabolomic techniques, we identified distinct patterns in the gut microbiome and fecal metabolites across different CRC metastatic sites. The differential gene analysis highlighted significant enrichment in biological processes related to immune response and extracellular matrix organization, with key genes playing roles in the complement and clotting cascades, and staphylococcus aureus infections. Protein-protein interaction networks further elucidated the potential mechanisms driving CRC spread, emphasizing the importance of extracellular vesicles and the PPAR signaling pathway in tumor metastasis. Our comprehensive microbiota analysis revealed a relatively stable alpha diversity across groups but identified specific bacterial genera associated with metastatic stages. Metabolomic profiling using OPLS-DA models unveiled distinct metabolic signatures, with differential metabolites enriched in pathways crucial for cancer metabolism and immune modulation. Integrative analysis of the gut microbiota and metabolic profiles highlighted significant correlations, suggesting a complex interplay that may influence CRC progression and metastasis. These findings offer novel insights into the microbial and metabolic underpinnings of CRC metastasis, paving the way for innovative diagnostic and therapeutic strategies targeting the gut microbiome and metabolic pathways.

Dynamic remodeling of Escherichia coli interactome in response to environmental perturbations.

Proteins play an essential role in the vital biological processes governing cellular functions. Most proteins function as members of macromolecular machines, with the network of interacting proteins revealing the molecular mechanisms driving the formation of these complexes. Profiling the physiology-driven remodeling of these interactions within different contexts constitutes a crucial component to achieving a comprehensive systems-level understanding of interactome dynamics. Here, we apply co-fractionation mass spectrometry and computational modeling to quantify and profile the interactions of ∼2000 proteins in the bacterium Escherichia coli cultured under 10 distinct culture conditions. The resulting quantitative co-elution patterns revealed large-scale condition-dependent interaction remodeling among protein complexes involved in diverse biochemical pathways in response to the unique environmental challenges. The network-level analysis highlighted interactome-wide biophysical properties and structural patterns governing interaction remodeling. Our results provide evidence of the local and global plasticity of the E. coli interactome along with a rigorous generalizable framework to define protein interaction specificity. We provide an accompanying interactive web application to facilitate the exploration of these rewired networks.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities.

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

A multimodal deep learning model to infer cell-type-specific functional gene networks.

BACKGROUND: Functional gene networks (FGNs) capture functional relationships among genes that vary across tissues and cell types. Construction of cell-type-specific FGNs enables the understanding of cell-type-specific functional gene relationships and insights into genetic mechanisms of human diseases in disease-relevant cell types. However, most existing FGNs were developed without consideration of specific cell types within tissues. RESULTS: In this study, we created a multimodal deep learning model (MDLCN) to predict cell-type-specific FGNs in the human brain by integrating single-nuclei gene expression data with global protein interaction networks. We systematically evaluated the prediction performance of the MDLCN and showed its superior performance compared to two baseline models (boosting tree and convolutional neural network). Based on the predicted cell-type-specific FGNs, we observed that cell-type marker genes had a higher level of hubness than non-marker genes in their corresponding cell type. Furthermore, we showed that risk genes underlying autism and Alzheimer's disease were more strongly connected in disease-relevant cell types, supporting the cellular context of predicted cell-type-specific FGNs. CONCLUSIONS: Our study proposes a powerful deep learning approach (MDLCN) to predict FGNs underlying a diverse set of cell types in human brain. The MDLCN model enhances prediction accuracy of cell-type-specific FGNs compared to single modality convolutional neural network (CNN) and boosting tree models, as shown by higher areas under both receiver operating characteristic (ROC) and precision-recall curves for different levels of independent test datasets. The predicted FGNs also show evidence for the cellular context and distinct topological features (i.e. higher hubness and topological score) of cell-type marker genes. Moreover, we observed stronger modularity among disease-associated risk genes in FGNs of disease-relevant cell types. For example, the strength of connectivity among autism risk genes was stronger in neurons, but risk genes underlying Alzheimer's disease were more connected in microglia.

idenPC-MIIP: identify protein complexes from weighted PPI networks using mutual important interacting partner relation.

Protein complexes are key units for studying a cell system. During the past decades, the genome-scale protein-protein interaction (PPI) data have been determined by high-throughput approaches, which enables the identification of protein complexes from PPI networks. However, the high-throughput approaches often produce considerable fraction of false positive and negative samples. In this study, we propose the mutual important interacting partner relation to reflect the co-complex relationship of two proteins based on their interaction neighborhoods. In addition, a new algorithm called idenPC-MIIP is developed to identify protein complexes from weighted PPI networks. The experimental results on two widely used datasets show that idenPC-MIIP outperforms 17 state-of-the-art methods, especially for identification of small protein complexes with only two or three proteins.

Genome-wide profiling reveals pervasive transcriptional alterations in fibroblasts derived from lesional skin in vitiligo including a reduced potential to proliferate.

Fibroblasts interact with keratinocytes and melanocytes to maintain skin homeostasis. However, the impact of selective melanocyte loss on the transcriptome of fibroblasts is not fully understood. Thus, we sought to understand the genome-wide transcriptome of fibroblasts derived from non-lesional (NL) and lesional (L) dermis in patients with non-segmental vitiligo. Transcriptional profiling of NL and L fibroblasts was performed on three individuals with vitiligo using next-generation-sequencing. Functional protein-protein interaction (PPI) networks were constructed for the significantly upregulated and downregulated genes, as well as for a common set of genes that were downregulated in both fibroblasts and epidermis in L skin (identified previously). Proliferation potential of NL and L fibroblasts was assessed experimentally. Genome-wide transcriptome analysis revealed a total of 414 (282, downregulated; 132, upregulated) differentially expressed (DE)-transcripts in L as compared to NL fibroblasts. Unsupervised hierarchical clustering of DE-transcripts segregated L and NL fibroblasts into two distinct clades, despite the apparent heterogeneity in lesions of different vitiligo patients. Gene Ontology analysis of downregulated genes revealed enrichment of keratinocyte-specific biological processes such as cornification and keratinization. PPI networks constructed for the downregulated and upregulated genes revealed deregulation of several hub genes associated with cell cycle regulation and cAMP metabolism respectively. Similarly, the PPI networks constructed for 67 genes downregulated in both fibroblasts as well as epidermis of L skin revealed downregulation of hub genes including stratifin, PIK3CG and CDH1. Analysis of the in vitro proliferation potential of L fibroblasts revealed a decrease in the expression of proliferation markers Ki67, MCM6, pERK and pCDK2, a decreased S phase population and an increase in alpha-SMA and collagen expression, corroborating the downregulation of hub genes associated with proliferation identified by PPI network analysis. Our study revealed pervasive transcriptional alterations in L compared to NL fibroblasts in vitiligo. The PPI analysis suggested a reduced potential to proliferate in melanocyte-deprived lesional fibroblasts, which was validated experimentally as well.

Sequencing-based network analysis provides a core set of genes for understanding hemolymph immune response mechanisms against Poly I:C stimulation in Amphioctopus fangsiao.

Aquatic viruses can spread rapidly and widely in seawater for their high infective ability. Polyinosinic-polycytidylic acid (Poly I:C), a viral dsRNA analog, is an immunostimulant that has been proved to activate various immune responses of immune cells in invertebrate. Hemolymph is a critical site that host immune response in invertebrates, and its transcriptome information obtained from Amphioctopus fangsiao stimulated by Poly I:C is crucial for understanding the antiviral molecular mechanisms of this species. In this study, we analyzed gene expression data in A. fangsiao hemolymph tissue within 24 h under Poly I:C stimulation and found 1082 and 299 differentially expressed genes (DEGs) at 6 and 24 h, respectively. Union set (1,369) DEGs were selected for subsequent analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were carried out for identifying DEGs related to immunity. Several significant immune-related terms and pathways, such as toll-like receptor signaling pathways term, inflammatory response term, TNF signaling pathway, and chemokine signaling pathway were identified. A protein-protein interaction (PPI) network was constructed for examining the relationships among immune-related genes. Finally, 12 hub genes, including EGFR, ACTG1, MAP2K1, and other nine hub genes, were identified based on the KEGG enrichment analysis and PPI network. The quantitative RT-PCR (qRT-PCR) was used to verify the expression profile of 12 hub genes. This research provides a reference for solving the problem of high mortality of A. fangsiao and other mollusks and provides a reference for the future production of some disease-resistant A. fangsiao.

Link Prediction with Continuous-Time Classical and Quantum Walks.

Protein-protein interaction (PPI) networks consist of the physical and/or functional interactions between the proteins of an organism, and they form the basis for the field of network medicine. Since the biophysical and high-throughput methods used to form PPI networks are expensive, time-consuming, and often contain inaccuracies, the resulting networks are usually incomplete. In order to infer missing interactions in these networks, we propose a novel class of link prediction methods based on continuous-time classical and quantum walks. In the case of quantum walks, we examine the usage of both the network adjacency and Laplacian matrices for specifying the walk dynamics. We define a score function based on the corresponding transition probabilities and perform tests on six real-world PPI datasets. Our results show that continuous-time classical random walks and quantum walks using the network adjacency matrix can successfully predict missing protein-protein interactions, with performance rivalling the state-of-the-art.

Predictive Value of Gene Databases in Discovering New Biomarkers and New Therapeutic Targets in Lung Cancer.

Background and Objectives: The molecular mechanisms of lung cancer are still unclear. Investigation of immune cell infiltration (ICI) and the hub gene will facilitate the identification of specific biomarkers. Materials and Methods: Key modules of ICI and immune cell-associated differential genes, as well as ICI profiles, were identified using lung cancer microarray data from the single sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) in the gene expression omnibus (GEO) database. Protein-protein interaction networks were used to identify hub genes. The receiver operating characteristic (ROC) curve was used to assess the diagnostic significance of the hub genes, and survival analysis was performed using gene expression profiling interactive analysis (GEPIA). Results: Significant changes in ICI were found in lung cancer tissues versus adjacent normal tissues. WGCNA results showed the highest correlation of yellow and blue modules with ICI. Protein-protein interaction networks identified four hub genes, namely CENPF, AURKA, PBK, and CCNB1. The lung adenocarcinoma patients in the low hub gene expression group showed higher overall survival and longer median survival than the high expression group. They were associated with a decreased risk of lung cancer in patients, indicating their potential role as cancer suppressor genes and potential targets for future therapeutic development. Conclusions: CENPF, AURKA, PBK, and CCNB1 show great potential as biomarkers and immunotherapeutic targets specific to lung cancer. Lung cancer patients' prognoses are often foreseen using matched prognostic models, and genes CENPF, AURKA, PBK, and CCNB1 in lung cancer may serve as therapeutic targets, which require further investigations.