Identification and validation of key genes with prognostic value in non-small-cell lung cancer via integrated bioinformatics analysis

An integrated bioinformatics approach to early diagnosis, prognosis and therapeutics of non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is one of the most deadly tumors characterized by poor survival rates. Advances in therapeutics and precise identification of biomarkers can potentially reduce the mortality rate. Thus, this study aimed to identify a set of common and stable gene biomarkers through integrated bioinformatics approaches that might be effective for NSCLC early diagnosis, prognosis, and therapies. Four gene expression profiles (GSE19804, GSE19188, GSE10072, and GSE32863) downloaded from the Gene Expression Omnibus database to identify common differential expressed genes (DEGs). A total of 213 overlapping DEGs (oDEGs) between NSCLC and healthy samples were identified by using statistical LIMMA method. Then 6 common top-ranked key genes (KGs) (CENPF, CAV1, ASPM, CCNB2, PRC1, and KIAA0101) were selected by using four network-measurer methods in the protein- protein interaction network. The GO functional and KEGG pathway enrichment analysis were performed to reveal some significant functions and pathways associated with NSCLC progression. Transcriptional and post-transcriptional factors of KGs were identified through the regulatory interaction network. The prognostic power and expression level of KGs were validated by using the independent data through the Kaplan-Meier and Box plots, respectively. Finally, 4 KGs-guided repositioning candidate drugs (ZSTK474, GSK2126458, Masitinib, and Trametinib) were proposed. The stability of three top-ranked drug-target interactions (CAV1 vs. ZSTK474, CAV1 vs. GSK2126458, and ASPM vs. Trametinib) were investigated by computing their binding free energies for 140 ns MD-simulation based on MM-PBSA approach. Therefore, the findings of this computational study may be useful for early prognosis, diagnosis and therapies of NSCLC.

The Impact of DAXX, HJURP and CENPA Expression in Uveal Melanoma Carcinogenesis and Associations with Clinicopathological Parameters

Uveal melanomas (UMs) represent rare malignant tumors associated with grim prognosis for the majority of patients. DAXX (Death Domain-Associated Protein), HJURP (Holliday Junction Recognition Protein) and CENPA (Centromere Protein A) proteins are implicated in epigenetic mechanisms, now in the spotlight of cancer research to better understand the molecular background of tumorigenesis. Herein, we investigated their expression in UM tissues using immunohistochemistry and explored possible correlations with a multitude of clinicopathological and survival parameters. The Cancer Genome Atlas Program (TCGA) was used for the investigation of their mRNA levels in UM cases. Nuclear DAXX expression correlated with an advanced T-stage (p = 0.004), while cytoplasmic expression marginally with decreased disease-free survival (DFS) (p = 0.084). HJURP nuclear positivity also correlated with advanced T-status (p = 0.054), chromosome 3 loss (p = 0.042) and increased tumor size (p = 0.03). More importantly, both nuclear and cytoplasmic HJURP immunopositivity correlated with decreased overall survival (OS) (p = 0.011 and 0.072, respectively) and worse DFS (p = 0.071 and 0.019, respectively). Lastly, nuclear CENPA overexpression was correlated with presence of irido-corneal angle involvement (p = 0.015) and loss of chromosome 3 (p = 0.041). Nuclear and cytoplasmic CENPA immunopositivity associated with decreased OS (p = 0.028) and DFS (p = 0.018), respectively. HJURP and CENPA mRNA overexpression exhibited strong association with tumor epithelioid histology and was linked to worse prognosis. Our results show the compounding role of DAXX, HJURP and CENPA in UM carcinogenesis, designating them as potential biomarkers for assessing prognosis and possible targets for novel therapeutic interventions.

Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review

Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.

Advancements in the diagnosis and treatment of sub‑centimeter lung cancer in the era of precision medicine (Review)

Lung cancer is the malignancy with the highest global mortality rate and imposes a substantial burden on society. The increasing popularity of lung cancer screening has led to increasing number of patients being diagnosed with pulmonary nodules due to their potential for malignancy, causing considerable distress in the affected population. However, the diagnosis and treatment of sub-centimeter grade pulmonary nodules remain controversial. The evolution of genetic detection technology and the development of targeted drugs have positioned the diagnosis and treatment of lung cancer in the precision medicine era, leading to a marked improvement in the survival rate of patients with lung cancer. It has been established that lung cancer driver genes serve a key role in the development and progression of sub-centimeter lung cancer. The present review aimed to consolidate the findings on genes associated with sub-centimeter lung cancer, with the intent of serving as a reference for future studies and the personalized management of sub-centimeter lung cancer through genetic testing.

The Clinical Impact of Death Domain-Associated Protein and Holliday Junction Recognition Protein Expression in Cancer: Unmasking the Driving Forces of Neoplasia

Death domain-associated protein (DAXX) and Holliday junction recognition protein (HJURP) act as chaperones of H3 histone variants H3.3 and centromere protein A (CENPA), respectively, and are implicated in many physiological processes, including aging and epigenetic regulation, by controlling various genes' transcription and subsequently protein expression. Research has highlighted both these biomolecules as participants in key procedures of tumorigenesis, including cell proliferation, chromosome instability, and oncogene expression. As cancer continues to exert a heavy impact on patients' well-being and bears substantial socioeconomic ramifications, the discovery of novel biomarkers for timely disease detection, estimation of prognosis, and therapy monitoring remains of utmost importance. In the present review, we present data reported from studies investigating DAXX and HJURP expression, either on mRNA or protein level, in human tissue samples from various types of neoplasia. Of note, the expression of DAXX and HJURP has been associated with a multitude of clinicopathological parameters, including disease stage, tumor grade, patients' overall and disease-free survival, as well as lymphovascular invasion. The data reveal the tumor-promoting properties of DAXX and HJURP in a number of organs as well as their potential use as diagnostic biomarkers and underline the important association between aberrations in their expression and patients' prognosis, rendering them as possible targets of future, personalized and precise therapeutic interventions.

To explore the effect of kaempferol on non-small cell lung cancer based on network pharmacology and molecular docking

Non-small cell lung cancer (NSCLC) is a common pathological type of lung cancer, which has a serious impact on human life, health, psychology and life. At present, chemotherapy, targeted therapy and other methods commonly used in clinic are prone to drug resistance and toxic side effects. Natural extracts of traditional Chinese medicine (TCM) have attracted wide attention in cancer treatment because of their small toxic and side effects. Kaempferol is a flavonoid from natural plants, which has been proved to have anticancer properties in many cancers such as lung cancer, but the exact molecular mechanism is still unclear. Therefore, on the basis of in vitro experiments, we used network pharmacology and molecular docking methods to study the potential mechanism of kaempferol in the treatment of non-small cell lung cancer. The target of kaempferol was obtained from the public database (PharmMapper, Swiss target prediction), and the target of non-small cell lung cancer was obtained from the disease database (Genecards and TTD). At the same time, we collected gene chips GSE32863 and GSE75037 in conjunction with GEO database to obtain differential genes. By drawing Venn diagram, we get the intersection target of kaempferol and NSCLC. Through enrichment analysis, PI3K/AKT is identified as the possible key signal pathway. PIK3R1, AKT1, EGFR and IGF1R were selected as key targets by topological analysis and molecular docking, and the four key genes were further verified by analyzing the gene and protein expression of key targets. These findings provide a direction for further research of kaempferol in the treatment of NSCLC.

A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer

Non-small cell lung cancer (NSCLC), the primary histological form of lung cancer, accounts for about 25%-the highest-of all cancer deaths. As NSCLC is often undetected until symptoms appear in the late stages, it is imperative to discover more effective tumor-associated biomarkers for early diagnosis. Topological data analysis is one of the most powerful methodologies applicable to biological networks. However, current studies fail to consider the biological significance of their quantitative methods and utilize popular scoring metrics without verification, leading to low performance. To extract meaningful insights from genomic data, it is essential to understand the relationship between geometric correlations and biological function mechanisms. Through bioinformatics and network analyses, we propose a novel composite selection index, the C-Index, that best captures significant pathways and interactions in gene networks to identify biomarkers with the highest efficiency and accuracy. Furthermore, we establish a 4-gene biomarker signature that serves as a promising therapeutic target for NSCLC and personalized medicine. The C-Index and biomarkers discovered were validated with robust machine learning models. The methodology proposed for finding top metrics can be applied to effectively select biomarkers and early diagnose many diseases, revolutionizing the approach to topological network research for all cancers.

A Unique Gene Signature Predicting Recurrence Free Survival in Stage IA Lung Adenocarcinoma

Objective

Resected stage IA lung adenocarcinoma (LUAD) has a reported 5-year recurrence free survival (RFS) of 63-81%. A unique gene signature stratifying patients with early stage LUAD as high or low-risk of recurrence would be valuable.

Methods

GEO datasets combining European and North American LUAD patients (n=684) were filtered for stage IA (n=105) to develop a robust signature for recurrence (RFSscore). Univariate Cox proportional hazard regression model was used to assess associations of gene expression with RFS and OS. Leveraging a bootstrap approach of these identified upregulated genes allowed construction of a model which was evaluated by Area Under the Received Operating Characteristics. The optimal signature has RFSscore calculated via a linear combination of expression of selected genes weighted by the corresponding Cox regression derived coefficients. Log-rank analysis calculated RFS and OS. Results were validated using the LUAD TCGA transcriptomic NGS based dataset.

Results

Rigorous bioinformatic analysis identified a signature of 4 genes: KNSTRN, PAFAH1B3, MIF, CHEK1. Kaplan-Meier analysis of stage IA LUAD with this signature resulted in 5-year RFS for low-risk of 90% compared to 53% for high-risk (HR 6.55, 95%CI 2.65-16.18, p-value <0.001), confirming the robustness of the gene signature with its clinical significance. Validation of the signature using TCGA dataset resulted in an AUC of 0.797 and 5-year RFS for low and high-risk stage IA patients being 91% and 67%, respectively (HR 3.44, 95%CI 1.16-10.23, p-value=0.044).

Conclusions

This 4 gene signature stratifies European and North American patients with pathologically confirmed stage IA LUAD into low and high-risk groups for OS and more importantly RFS.

Five-hub genes identify potential mechanisms for the progression of asthma to lung cancer

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Advances in holliday junction recognition protein (HJURP): Structure, molecular functions, and roles in cancer

Oncogenes are increasingly recognized as important factors in the development and progression of cancer. Holliday Junction Recognition Protein (HJURP) is a highly specialized mitogenic protein that is a chaperone protein of histone H3. The HJURP gene is located on chromosome 2q37.1 and is involved in nucleosome composition in the mitotic region, forming a three-dimensional crystal structure with Centromere Protein A (CENP-A) and the histone 4 complex. HJURP is involved in the recruitment and assembly of centromere and kinetochore and plays a key role in stabilizing the chromosome structure of tumor cells, and its dysfunction may contribute to tumorigenesis. In the available studies HJURP is upregulated in a variety of cancer tissues and cancer cell lines and is involved in tumor proliferation, invasion, metastasis and immune response. In an in vivo model, overexpression of HJURP in most cancer cell lines promotes cell proliferation and invasiveness, reduces susceptibility to apoptosis, and promotes tumor growth. In addition, upregulation of HJURP was associated with poorer prognosis in a variety of cancers. These properties suggest that HJURP may be a possible target for the treatment of certain cancers. Various studies targeting HJURP as a prognostic and therapeutic target for cancer are gradually attracting interest and attention. This paper reviews the functional and molecular mechanisms of HJURP in a variety of tumor types with the aim of providing new targets for future cancer therapy.

DriverMP enables improved identification of cancer driver genes

BACKGROUND

Cancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amid an extensive array of passenger genes.

FINDINGS

We present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply nonsilent somatic mutation data, protein‒protein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in 10 cancer datasets from The Cancer Genome Atlas demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis.

CONCLUSIONS

The new method, DriverMP, which is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database for 10 cancer types and an online service that can be freely accessed without registration for users. The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers.

Pan-cancer analysis based on epigenetic modification explains the value of HJURP in the tumor microenvironment

To analyze the expression levels, prognostic value and immune infiltration association of Holliday junction protein (HJURP) as well as its feasibility as a pan-cancer biomarker for different cancers. The Protter online tool was utilized to obtain the localization of HJURP, then the methylation of HJURP in tumors were further explored. Thereafter, the mRNA data and clinical characteristics of 33 tumor types from TCGA database were obtained to investigate the expression and prognostic relationship of HJURP in different tumor types. Finally, the composition pattern and immune infiltration of HJURP in different tumors were detected in Tumor Immune Estimation Resource. HJURP was abnormally expressed in most of the cancer types and subtypes in TCGA database. Also, it was associated with poor prognosis of different cohorts. At the same time, the results also showed that HJURP was related to tumor immune evasion through different mechanisms, including T cell rejection and methylation in different cancer types. Besides, the methylation of HJURP was inversely proportional to mRNA expression levels, which mediated the dysfunctional phenotypes of T cells and poor prognosis of different cancer types. Alternatively, our results indicated that HJURP expression was associated with immune cell infiltration in a variety of cancers. HJURP may serve as an oncogenic molecule, and its expression and immune infiltration characteristics can be used as a biomarker for cancer detection, prognosis, treatment design and follow-up.

Identifying potential prognosis markers in hepatocellular carcinoma via integrated bioinformatics analysis and biological experiments

Background: Hepatocellular carcinoma is one kind of clinical common malignant tumor with a poor prognosis, and its pathogenesis remains to be clarified urgently. This study was performed to elucidate key genes involving HCC by bioinformatics analysis and experimental evaluation.

Methods: We identified common differentially expressed genes (DEGs) based on gene expression profile data of GSE60502 and GSE84402 from the Gene Expression Omnibus (GEO) database. Gene Ontology enrichment analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, REACTOME pathway enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were used to analyze functions of these genes. The protein-protein interaction (PPI) network was constructed using Cytoscape software based on the STRING database, and Molecular Complex Detection (MCODE) was used to pick out two significant modules. Hub genes, screened by the CytoHubba plug-in, were validated by Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (HPA) database. Then, the correlation between hub genes expression and immune cell infiltration was evaluated by Tumor IMmune Estimation Resource (TIMER) database, and the prognostic values were analyzed by Kaplan-Meier plotter. Finally, biological experiments were performed to illustrate the functions of RRM2.

Results: Through integrated bioinformatics analysis, we found that the upregulated DEGs were related to cell cycle and cell division, while the downregulated DEGs were associated with various metabolic processes and complement cascade. RRM2, MAD2L1, MELK, NCAPG, and ASPM, selected as hub genes, were all correlated with poor overall prognosis in HCC. The novel RRM2 inhibitor osalmid had anti-tumor activity, including inhibiting proliferation and migration, promoting cell apoptosis, blocking cell cycle, and inducing DNA damage of HCC cells.

Conclusion: The critical pathways and hub genes in HCC progression were screened out, and targeting RRM2 contributed to developing new therapeutic strategies for HCC.

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein–protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

microRNA-1321 and microRNA-7515 contribute to the progression of non-small cell lung cancer by targeting CDC20

Cell division cycle 20 (CDC20) and microRNAs (miRNAs) are differentially expressed in non-small cell lung cancer (NSCLC). The current study aimed to investigate the role of miR-1321 and miR-7515 regulation in CDC20 during NSCLC development. CDC20 expression in paracancerous and tumor tissues was assessed using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The relationship between CDC20 expression and prognosis of patients was analyzed using the TCGA database. The expression profile of CDC20 in healthy lung cells and NSCLC cells was detected using qRT-PCR and western blotting. After the knockdown of CDC20 in NSCLC cells, the cell proliferation, apoptosis, migration, invasion, and cell cycle changes were investigated by CCK8, EdU, flow cytometry, wound healing, and Transwell assays. The miRNAs targeting CDC20 were predicted using two bioinformatics websites and validated using dual-luciferase assays. CDC20 was enhanced in NSCLC tissues and cells, thus predicting the poor prognosis in NSCLC patients. After CDC20 inhibition, the malignant phenotype of NSCLC cells was reverted. miR-1321 and miR-7515 targeted CDC20 and exhibited the same anti-tumor effects as CDC20 silencing. Functional rescue experiments showed that CDC20 overexpression averted the anti-tumor effects of miR-1321 and miR-7515 on NSCLC cells. miR-1321 and miR-7515 inhibited NSCLC development by targeting CDC20. Thus, the current study has implications in NSCLC treatment and provides novel insights into NSCLC management.

CHEK1: a hub gene related to poor prognosis for lung adenocarcinoma

Aim: The study aims to pinpoint hub genes and investigate their functions in order to gain insightful understandings of lung adenocarcinoma (LUAD). Methods: Bioinformatic approaches were adopted to investigate genes in databases including Gene Expression Omnibus, WebGestalt, STRING and Cytoscape, GEPIA2, Oncomine, Human Protein Atlas, TIMER2.0, UALCAN, cBioPortal, TargetScanHuman, OncomiR, ENCORI, Kaplan-Meier plotter, UCSC Xena, European Molecular Biology Laboratory - European Bioinformatics Institute Single Cell Expression Atlas and CancerSEA. Results: Five hub genes were ascertained. CHEK1 was overexpressed in a range of cancers, including LUAD. Promoter methylation, amplification and miRNA regulation might trigger CHEK1 upregulation, signaling poor prognosis. CHEK1 with its coexpressed genes were enriched in the cell cycle pathway. Intratumor heterogeneity of CHEK1 expression could be observed. Cell clusters with CHEK1 expression were more prone to metastasis and epithelial-to-mesenchymal transition. Conclusion: CHEK1 might potentially act as a prognostic biomarker for LUAD.

Proliferating cell nuclear antigen clamp associated factor, a potential proto-oncogene with increased expression in malignant gastrointestinal tumors

Gastrointestinal (GI) cancers, including malignancies in the gastrointestinal tract and accessory organs of digestion, represent the leading cause of death worldwide due to the poor prognosis of most GI cancers. An investigation into the potential molecular targets of prediction, diagnosis, prognosis, and therapy in GI cancers is urgently required. Proliferating cell nuclear antigen (PCNA) clamp associated factor (PCLAF), which plays an essential role in cell proliferation, apoptosis, and cell cycle regulation by binding to PCNA, is a potential molecular target of GI cancers as it contributes to a series of malignant properties, including tumorigenesis, epithelial-mesenchymal transition, migration, and invasion. Furthermore, PCLAF is an underlying plasma prediction target in colorectal cancer and liver cancer. In addition to GI cancers, PCLAF is also involved in other types of cancers and autoimmune diseases. Several pivotal pathways, including the Rb/E2F pathway, NF-κB pathway, and p53-p21 cascade, are implicated in PCLAF-mediated diseases. PCLAF also contributes to some diseases through dysregulation of the p53 pathway, WNT signal pathway, MEK/ERK pathway, and PI3K/AKT/mTOR signal cascade. This review mainly describes in detail the role of PCLAF in physiological status and GI cancers. The signaling pathways involved in PCLAF are also summarized. Suppression of the interaction of PCLAF/PCNA or the expression of PCLAF might be potential biological therapeutic strategies for GI cancers.

Gene Targets Network Analysis for the Revealing and Guidance of Molecular Driving Mechanism of Lung Cancer

The objective was to explore the function of gene differential expressions between lung cancer tissues and the interaction between the relevant encoded proteins, thereby analyzing the important genes closely related to lung cancer. A total of 120 samples from the GEO database (including two groups, i.e., 60 lung cancer in situ specimens and 60 normal specimens) were taken as the research objects, which were submitted to the analysis of signaling pathway, biological function enrichment, and protein interactions to reveal the molecular driving mechanism of lung cancer. Results: A total of 875 differentially expressed genes were obtained, including 291 up-regulated genes and 584 down-regulated genes. The up-regulated genes were mainly involved in biological processes such as protein metabolism, protein hydrolysis, mitosis, and cell division. Down-regulated genes were mainly involved in neutrophil chemotaxis, inflammatory response, immune response, and angiogenesis. The protein expression of high expression genes and low expression genes in patients were higher than those in the control group. The protein corresponding to the high expression gene was highly expressed in the patient group. Meanwhile, the proteins corresponding to the low expression genes were also expressed in the patient group, which showed that although the proteins corresponding to the low expression genes were low in the patients, they were still the target genes related to lung cancer. In conclusion, the molecular driving mechanism in lung cancer was mainly related to protein metabolism, proteolysis, mitosis, and cell division. It was found that TOP2A, CCNB1, CCNA2, CDK1, and TTK might be the critical target genes of lung cancer.

Genetic variants of CHEK1, PRIM2 and CDK6 in the mitotic phase-related pathway are associated with nonsmall cell lung cancer survival

The mitotic phase is a vital step in cell division and may be involved in cancer progression, but it remains unclear whether genetic variants in mitotic phase-related pathways genes impact the survival of these patients. Here, we investigated associations between 31 032 single nucleotide polymorphisms (SNPs) in 368 mitotic phase-related pathway genes and overall survival (OS) of patients with nonsmall cell lung cancer (NSCLC). We assessed the associations in a discovery data set of 1185 NSCLC patients from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and validated the findings in another data set of 984 patients from the Harvard Lung Cancer Susceptibility Study. As a result, we identified three independent SNPs (ie, CHEK1 rs76744140 T>C, PRIM2 rs6939623 G>T and CDK6 rs113181986 G>C) to be significantly associated with NSCLC OS with an adjusted hazard ratio of 1.29 (95% confidence interval = 1.11-1.49, P = 8.26 × 10^-4 ), 1.26 (1.12-1.42, 1.10 × 10^-4 ) and 0.73 (0.63-0.86, 1.63 × 10^-4 ), respectively. Moreover, the number of combined unfavorable genotypes of these three SNPs was significantly associated with NSCLC OS and disease-specific survival in the PLCO data set (P_trend  < .0001 and .0003, respectively). Further expression quantitative trait loci analysis showed that the rs76744140C allele predicted CHEK1 mRNA expression levels in normal lung tissues and that rs113181986C allele predicted CDK6 mRNA expression levels in whole blood tissues. Additional analyses indicated CHEK1, PRIM2 and CDK6 may impact NSCLC survival. Taken together, these findings suggested that these genetic variants may be prognostic biomarkers of patients with NSCLC.

A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

Background

Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.

Methods

Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.

Results

A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.

Conclusion

Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01975-z.

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

Background

Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms.

Method

RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs).

Results

Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them.

Conclusion

In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Immune cell landscape analysis reveals prognostic immune cells and its potential mechanism in squamous cell lung carcinoma

Background

Squamous cell lung carcinoma (LUSC) was closely associated with smoking which was known to have a distant immunosuppression effect. In this study, we aimed to explore the relationship between immune cells and clinical outcomes of LUSC patients with smoking history.

Methods

The immune cell infiltration and RNA expression profiles of LUSC patients were collected from The Cancer Genome Atlas (TCGA). Then, the correlation between immune cell infiltration and clinical characteristics was explored. According to the level of immune cell infiltration, LUSC patients with smoking history were divided into high or low group to screen the differentially expressed lncRNAs and mRNAs. The prediction of target genes was performed by miRanda. Finally, the prognostic value of a certain signature was confirmed in an independent dataset.

Results

Higher abundance of tumor-infiltrating T follicular helper (Tfh) cells together with a lower abundance of resting memory CD4 T cells had been found in LUSC current reformed smokers for ≤15 years and current smoking patients. Moreover, Tfh cell infiltration was not only associated with better overall survival (OS) but also varied from different degrees of TNM stage. Low expression of lncRNA PWRN1 and its potential regulating genes DMRTB1, PIRT, APOBEC1, and ZPBP2 were associated with better OS. Combining PWRN1 and four regulating genes as a signature, patients with higher-level expression of the signature had shorter survival time in not only the TCGA but also in the GEO dataset.

Conclusions

It was found that Tfh cells presented higher infiltration in LUSC current reformed smokers for ≤15 years and current smokers, while resting memory CD4 T cells had lower infiltration. The signature consisting of PWRN1 as well as its predicted targeted mRNAs was dysregulated in different levels of Tfh cell infiltration and might indicate patients' OS.

Immunohistochemical basigin expression level in thyroid cancer tissues

BACKGROUND

Thyroid cancer (TC) is the most common endocrine malignancy; basigin (also known as BSG) plays a crucial role in tumor cell invasion, metastasis, and angiogenesis. This study was designed to identify the change of BSG expression in TC and its possible potential mechanism.

METHODS

The BSG expression levels in TC were demonstrated using data collected from in-house immunohistochemical (IHC), RNA-sequencing (RNA-seq), microarrays, and literatures. Integrated analysis was performed to determined BSG expression levels in TC comprehensively. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed with the integration of BSG co-expressed genes and differentially expressed genes (DEGs) in TC tissues to explore the potential mechanisms of BSG in TC.

RESULTS

The protein expression level of BSG was significantly higher in TC cases based on the IHC experiments. In addition, the combined SMD for BSG expression was 0.39 (p < 0.0001), the diagnostic odds ratio was 3.69, and the AUC of the sROC curve was 0.6986 using 1182 TC cases and 437 non-cancerous cases from 17 independent datasets. Furthermore, BSG co-expressed genes tended to be enriched in gene terms of the extracellular matrix (ECM), cell adhesion, and cell-cell interactions. The expression levels of nine hub BSG co-expressed genes were markedly upregulated in TC cases.

CONCLUSION

BSG expression levels were closely correlated with the progression of TC and may affect the signals of the ECM, cell adhesion, and cell-cell interactions.