Independent Prognostic Potential of GNPNAT1 in Lung Adenocarcinoma

Construction of a prognostic model based on memory CD4+ T cell-associated genes for lung adenocarcinoma and its applications in immunotherapy

The association between memory CD4+ T cells and cancer prognosis is increasingly recognized, but their impact on lung adenocarcinoma (LUAD) prognosis remains unclear. In this study, using the cell-type identification by estimating relative subsets of RNA transcripts algorithm, we analyzed immune cell composition and patient survival in LUAD. Weighted gene coexpression network analysis helped identify memory CD4+ T cell-associated gene modules. Combined with module genes, a five-gene LUAD prognostic risk model (HOXB7, MELTF, ABCC2, GNPNAT1, and LDHA) was constructed by regression analysis. The model was validated using the GSE31210 data set. The validation results demonstrated excellent predictive performance of the risk scoring model. Correlation analysis was conducted between the clinical information and risk scores of LUAD samples, revealing that LUAD patients with disease progression exhibited higher risk scores. Furthermore, univariate and multivariate regression analyses demonstrated the model independent prognostic capability. The constructed nomogram results demonstrated that the predictive performance of the nomogram was superior to the prognostic model and outperformed individual clinical factors. Immune landscape assessment was performed to compare different risk score groups. The results revealed a better prognosis in the low-risk group with higher immune infiltration. The low-risk group also showed potential benefits from immunotherapy. Our study proposes a memory CD4+ T cell-associated gene risk model as a reliable prognostic biomarker for personalized treatment in LUAD patients.

GNPNAT1 is a Biomarker That Predicts a Poor Prognosis of Breast Cancer

Background

Breast cancer (BC) is increasingly becoming the primary reason for death in women, which sounded the alarm. Thus, finding a novel management target for BC is imminent.

Materials and Methods

The data on gene expression and clinicopathological characteristics were downloaded from The Cancer Genome Atlas (TCGA). The expression of GNPNAT1 in 40 paired breast cancer and adjacent tissues was measured by quantitative real-time polymerase chain reaction (qRT-PCR). Univariate and Multivariate logistic regression methodology was applied to analyze the prognostic factors for lymph node metastasis (LNM). Based on the status of breast cancer-relative receptors, patients were distributed into six groups, and then the Kaplan-Meier survival analysis with a Log rank test was applied to investigate the involvement among the expression of GNPNAT1 and overall survival (OS).

Results

We found higher expression of GNPNAT1 was connected with poor survival in breast cancer by COX regulation analysis. GO, KEGG, and GSEA analysis prompted that GNPNAT1 was connected with the defense mechanism of cells, cell proliferation, and division. Immunization infiltration analysis showed that high GNPNAT1 was negatively connected with 16 immunization infiltration cell types and positively connected with four immunization infiltration cell types.

Conclusion

As a whole, our results indicated that GNPNAT1 might be a probable biomarker for diagnosis and prognosis in breast cancer.

Dysregulation of hexosamine biosynthetic pathway wiring metabolic signaling circuits in cancer

Metabolite sensing, a fundamental biological process, plays a key role in metabolic signaling circuit rewiring. Hexosamine biosynthetic pathway (HBP) is a glucose metabolic pathway essential for the synthesis of uridine diphosphate N-acetylglucosamine (UDP-GlcNAc), which senses key nutrients and integrally maintains cellular homeostasis. UDP-GlcNAc dynamically regulates protein N-glycosylation and O-linked-N-acetylglucosamine modification (O-GlcNAcylation). Dysregulated HBP flux leads to abnormal protein glycosylation, and contributes to cancer development and progression by affecting protein function and cellular signaling. Furthermore, O-GlcNAcylation regulates cellular signaling pathways, and its alteration is linked to various cancer characteristics. Additionally, recent findings have suggested a close association between HBP stimulation and cancer stemness; an elevated HBP flux promotes cancer cell conversion to cancer stem cells and enhances chemotherapy resistance via downstream signal activation. In this review, we highlight the prominent roles of HBP in metabolic signaling and summarize the recent advances in HBP and its downstream signaling, relevant to cancer.

Prognosis and personalized treatment prediction in lung adenocarcinoma: An in silico and in vitro strategy adopting cuproptosis related lncRNA towards precision oncology

Background: There is a rapid increase in lung adenocarcinomas (LUAD), and studies suggest associations between cuproptosis and the occurrence of various types of tumors. However, it remains unclear whether cuproptosis plays a role in LUAD prognosis. Methods: Dataset of the TCGA-LUAD was treated as training cohort, while validation cohort consisted of the merged datasets of the GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081. Ten studied cuproptosis-related genes (CRG) were used to generated CRG clusters and CRG cluster-related differential expressed gene (CRG-DEG) clusters. The differently expressed lncRNA that with prognosis ability between the CRG-DEG clusters were put into a LASSO regression for cuproptosis-related lncRNA signature (CRLncSig). Kaplan-Meier estimator, Cox model, receiver operating characteristic (ROC), time-dependent AUC (tAUC), principal component analysis (PCA), and nomogram predictor were further deployed to confirm the model's accuracy. We examined the model's connections with other forms of regulated cell death, including apoptosis, necroptosis, pyroptosis, and ferroptosis. The immunotherapy ability of the signature was demonstrated by applying eight mainstream immunoinformatic algorithms, TMB, TIDE, and immune checkpoints. We evaluated the potential drugs for high risk CRLncSig LUADs. Real-time PCR in human LUAD tissues were performed to verify the CRLncSig expression pattern, and the signature's pan-cancer's ability was also assessed. Results: A nine-lncRNA signature, CRLncSig, was built and demonstrated owning prognostic power by applied to the validation cohort. Each of the signature genes was confirmed differentially expressed in the real world by real-time PCR. The CRLncSig correlated with 2,469/3,681 (67.07%) apoptosis-related genes, 13/20 (65.00%) necroptosis-related genes, 35/50 (70.00%) pyroptosis-related genes, and 238/380 (62.63%) ferroptosis-related genes. Immunotherapy analysis suggested that CRLncSig correlated with immune status, and checkpoints, KIR2DL3, IL10, IL2, CD40LG, SELP, BTLA, and CD28, were linked closely to our signature and were potentially suitable for LUAD immunotherapy targets. For those high-risk patients, we found three agents, gemcitabine, daunorubicin, and nobiletin. Finally, we found some of the CRLncSig lncRNAs potentially play a vital role in some types of cancer and need more attention in further studies. Conclusion: The results of this study suggest our cuproptosis-related CRLncSig can help to determine the outcome of LUAD and the effectiveness of immunotherapy, as well as help to better select targets and therapeutic agents.

Development of a metabolism-related signature for predicting prognosis, immune infiltration and immunotherapy response in breast cancer

Breast cancer (BRCA) is the most commonly diagnosed cancer and among the top causes of cancer deaths globally. The abnormality of the metabolic process is an important characteristic that distinguishes cancer cells from normal cells. Currently, there are few metabolic molecular models to evaluate the prognosis and treatment response of BRCA patients. By analyzing RNA-seq data of BRCA samples from public databases via bioinformatic approaches, we developed a prognostic signature based on seven metabolic genes (PLA2G2D, GNPNAT1, QPRT, SHMT2, PAICS, NT5E and PLPP2). Low-risk patients showed better overall survival in all five cohorts (TCGA cohort, two external validation cohorts and two internal validation cohorts). There was a higher proportion of tumor-infiltrating CD8⁺ T cells, CD4⁺ memory resting T cells, gamma delta T cells and resting dendritic cells and a lower proportion of M0 and M2 macrophages in the low-risk group. Low-risk patients also showed higher ESTIMATE scores, higher immune function scores, higher Immunophenoscores (IPS) and checkpoint expression, lower stemness scores, lower TIDE (Tumor Immune Dysfunction and Exclusion) scores and IC50 values for several chemotherapeutic agents, suggesting that low-risk patients could respond more favorably to immunotherapy and chemotherapy. Two real-world patient cohorts receiving anti-PD-1 therapy were applied for validating the predictive results. Molecular subtypes identified based on these seven genes also showed different immune characteristics. Immunohistochemical data obtained from the human protein atlas database demonstrated the protein expression of signature genes. This research may contribute to the identification of metabolic targets for BRCA and the optimization of risk stratification and personalized treatment for BRCA patients.

Identification and validation of a novel phagocytosis regulators-related signature with potential prognostic and immunotherapeutic value in patients with lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a malignant tumor that seriously affects the prognosis of patients. Tumor-associated macrophages (TAMs) play a vital role in the tumor microenvironment and can be used as a potential target for tumor therapy, and phagocytosis regulators (PRs) are particularly important in this process. However, the PRs-related signature that can predict the potential prognostic and immunotherapeutic value in patients with LUAD has not been discovered.

METHODS

In this study, we mainly analyzed the effect of phagocytosis regulators on the prognosis of LUAD, and based on multiple screening analyses including differential analysis, univariate Cox analysis, and Lasso analysis, we constructed a prognostic risk model consisting of five genes. To verify the stability of the model, survival analysis and ROC curve verification were carried out through multiple data sets. In addition, we also combined many factors, such as immune infiltrating cells, clinical grouping characteristics, immune examination sites, pro-inflammatory factors, and other factors as well as in vitro cell experiments and clinical tissue samples for further validation analysis.

RESULTS

After identifying 29 differentially expressed PRs in LUAD samples, we further constructed a prognostic model consisting of five prognostic signatures (FURIN, KIF23, SASH3, GNPNAT1, and ITGAL). Further survival analysis tests, ROC verification, as well as univariate and multivariate Cox regression analysis showed that the risk score of the model could well predict the prognosis of LUAD patients and could be used as an independent prognostic factor. In addition, we further found that these phagocytic regulators-related signatures were closely related to the immune microenvironment and immunotherapy in LUAD patients, and could well predict the efficacy of immunotherapy in patients. In vitro cell experiments and Immunohistochemistry of clinical tissues showed that the expressions of FURIN, KIF23, SASH3, GNPNAT1 and ITGAL in normal lung cells/tissues and LUAD cells/tissues were consistent with bioinformatics results, and 3 of them had significant differences.

CONCLUSION

Our study identified a novel PRs-related signature that has potential application value in predicting the prognosis of LUAD patients and predicting the efficacy of immunotherapy. This provides a new basis for the prognosis assessment of LUAD patients and provides a novel target for immunotherapy of LUAD patients.

A novel variant in GNPNAT1 gene causing a spondylo-epi-metaphyseal dysplasia resembling PGM3-Desbuquois like dysplasia

Spondylo-epi-metaphyseal dysplasias (SEMDs) are a clinically and genetically heterogeneous group of skeletal dysplasias characterized by short stature and abnormal modeling of the spine and long bones. A novel form of rhizomelic skeletal dysplasia, Ain-Naz type, associated with a homozygous variant in GNPNAT1 was recently identified. Herein, we report an Egyptian patient, offspring of consanguineous parents, who presented with a severe form of unclassified SEMD. Whole exome sequencing identified a novel homozygous variant in exon 3, c.77T>G, (p.Phe26Cys) in GNPNAT1, that was confirmed by Sanger sequencing and both parents were found to be heterozygous for the identified variant. Main features included severe short stature, rhizomelic limb shortening, and wide flared metaphysis. Short broad long bones, brachydactyly, delayed epiphyseal ossification of long bones, advanced bone age, and immunodeficiency were additional findings expanding the clinical phenotype described in the previously reported family. We conclude that variants in the GNPNAT1 gene cause an autosomal recessive form of SEMD resembling Desbuquois like dysplasia caused by PGM3, which is involved in the same pathway as GNPNAT1.

XBP1 impacts lung adenocarcinoma progression by promoting plasma cell adaptation to the tumor microenvironment

Background: The activation of X-box binding protein 1 (XBP1) plays an essential role in the unfolded protein response (UPR) of the endoplasmic reticulum (ER). XBP1 is commonly expressed in various tumors and is closely related to tumorigenesis and progression. However, the role of XBP1 in lung adenocarcinoma (LUAD), especially the prognostic value of its alternative splicing isoforms, remains largely unknown.

Methods: The LUAD datasets were retrieved from the The Cancer Genome Atlas, ArrayExpress and Gene Expression Omnibus. GEPIA2 and meta-analysis were employed to explore the prognostic value, and bioinformatics analysis with the TIMER2.0 database was used to investigate immune cell infiltration. We performed single-cell analyses to identify cell types with high XBP1 expression. In addition, polymerase chain reaction (PCR) and DNA sequencing were performed to verify the authenticity of the new spliceosome.

Results: In this study, we found that high expression of XBP1 was significantly associated with a good prognosis, and XBP1 expression was significantly positively correlated with B cell infiltration in LUAD. In addition, we found that high-level expression of a novel splicing isoform, XBP1 (XBP1-003), improved the prognosis of LUAD. Protein structural analysis demonstrated that XBP1-003 has several specific protein domains that are different from those of other XBP1 isoforms, indicating a unique function of this isoform in LUAD.

Conclusion: All these results suggest that XBP1 plays an antitumorigenic role in LUAD through alternative splicing, which may be related to the adaptation of plasma cells. This sheds new light on the potential strategy for LUAD prognosis evaluation and immunotherapy.

GNPNAT1 Predicts Poor Prognosis and Cancer Development in Non-Small Cell Lung Cancer

Background

Glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1) is a key enzyme in the biosynthetic pathway of uridine diphosphate-N-acetylglucosamine and is upregulated in multiple malignancies. However, its function in cancer biology remains unclear.

Methods

Using TCGA dataset, this study analysed GNPNAT1 expression in non-small cell lung cancer (NSCLC) and assessed the correlation between GNPNAT1 and NSCLC patient prognosis. MTT and transwell assays were performed to determine the effect of GNPNAT1 on the growth and metastatic ability of lung cancer cells. GNPNAT1 expression was detected using immunohistochemistry in 78 NSCLC patients, and we analysed the correlation among clinicopathological parameters, overall survival (OS) and GNPNAT1 levels. Transcription factors that potentially regulate GNPNAT1 were explored using database analysis. RNF2 expression was verified using immunohistochemistry in NSCLC tissues.

Results

The results indicated that GNPNAT1 was upregulated in NSCLC, and patients with high GNPNAT1 levels had a poor prognosis. GNPNAT1 overexpression promoted the proliferative and metastatic ability of lung cancer cells, whereas GNPNAT1 knockdown showed the opposite effect. GNPNAT1 expression was upregulated in NSCLC tissues compared to matched normal tissues as assessed by immunohistochemistry. Moreover, GNPNAT1 levels were positively correlated with histological type and pathological stage. The negative correlation between GNPNAT1 levels and OS was confirmed in 78 NSCLC patients. Aberrant RNF2 partly contributed to the upregulation of GNPNAT1 expression in NSCLC.

Conclusion

These findings suggested that GNPNAT1 was upregulated and played an important role in NSCLC. GNPNAT1 is expected to represent an effective prognostic biomarker for NSCLC patients.

Glucosamine-phosphate N-acetyltransferase 1 and its DNA methylation can be biomarkers for the diagnosis and prognosis of lung cancer

Objective

Lung cancer ranking high in the cancer‐related list has long perplexed patients, in which glucosamine‐phosphate N‐acetyltransferase 1 (GNPNAT1) is found to be highly expressed. Besides, DNA methylation is perceived as a biomarker to assess the prognosis of patients with various cancers. However, the correlation between GNPNAT1 and DNA methylation and the role of GNPNAT1 in lung cancer remain vague.

Methods

Principal component analysis (PCA), heatmap, volcano map, Venn diagram, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to screen out the candidate genes. The viability, migration, and invasion of lung cancer cells were detected by CCK‐8 and Transwell assays. An xenograft tumor mouse model was established. The relative expressions of GNPNAT1, E‐cadherin, vimentin, Matrix metalloproteinase‐2 (MMP‐2), tissue inhibitor of metalloproteinase‐2 (TIMP‐2), E2F1, and cyclin D1 in cells or xenograft tumor tissues were quantified by Western blot, RT‐qPCR, or immunohistochemistry assay.

Results

GNPNAT1 was screened as the research object. GNPNAT1 methylation was downregulated, while GNPNAT1 expression was upregulated in lung cancer tissues. The methylation and mRNA levels of GNPNAT1 were correlated with the patient prognosis. GNPNAT1 increased cell viability, migration and invasion, and promoted the xenograft tumor volume and weight, whereas shGNPNAT1 acted oppositely. Moreover, expressions of Vimentin, MMP‐2, E2F1, and cyclin D1 were increased, but E‐cadherin and TIMP‐2 expressions were decreased by overexpressed GNPNAT1, whilst GNPNAT1 knockdown ran conversely.

Conclusion

GNPNAT1 and methylated GNPNAT1 coverage are biomarkers for the diagnosis and prognosis of lung cancer.

Evaluating the Prognostic and Therapeutic Potentials of the Proteasome 26S Subunit, ATPase (PSMC) Family of Genes in Lung Adenocarcinoma: A Database Mining Approach

This study explored the prognostic and therapeutic potentials of multiple Proteasome 26S Subunit, ATPase (PSMC) family of genes (PSMC1-5) in lung adenocarcinoma (LUAD) diagnosis and treatment. All the PSMCs were found to be differentially expressed (upregulated) at the mRNA and protein levels in LUAD tissues. The promoter and multiple coding regions of PSMCs were reported to be differentially and distinctly methylated, which may serve in the methylation-sensitive diagnosis of LUAD patients. Multiple somatic mutations (alteration frequency: 0.6–2%) were observed along the PSMC coding regions in LUAD tissues that could assist in the high-throughput screening of LUAD patients. A significant association between the PSMC overexpression and LUAD patients’ poor overall and relapse-free survival (p < 0.05; HR: >1.3) and individual cancer stages (p < 0.001) was discovered, which justifies PSMCs as the ideal targets for LUAD diagnosis. Multiple immune cells and modulators (i.e., CD274 and IDO1) were found to be associated with the expression levels of PSMCs in LUAD tissues that could aid in formulating PSMC-based diagnostic measures and therapeutic interventions for LUAD. Functional enrichment analysis of neighbor genes of PSMCs in LUAD tissues revealed different genes (i.e., SLIRP, PSMA2, and NUDSF3) previously known to be involved in oncogenic processes and metastasis are co-expressed with PSMCs, which could also be investigated further. Overall, this study recommends that PSMCs and their transcriptional and translational products are potential candidates for LUAD diagnostic and therapeutic measure discovery.

A Distinct Glucose Metabolism Signature of Lung Adenocarcinoma With Prognostic Value

Background: Lung adenocarcinoma (LUAD) remains the most common type of lung cancer and is the main cause of cancer-related death worldwide. Reprogramming of glucose metabolism plays a crucial role in tumorigenesis and progression. However, the regulation of glucose metabolism is still being explored in LUAD. Determining the underlying clinical value of glucose metabolism will contribute in increasing clinical interventions. Our study aimed to conduct a comprehensive analysis of the landscape of glucose metabolism-related genes in LUAD and develop a prognostic risk signature.

Methods: We extracted the RNA-seq data and relevant clinical variants from The Cancer Genome Atlas (TCGA) database and identified glucose metabolism-related genes associated with the outcome by correlation analysis. To generate a prognostic signature, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed.

Results: Finally, ten genes with expression status were identified to generate the risk signature, including FBP2, ADH6, DHDH, PRKCB, INPP5J, ABAT, HK2, GNPNAT1, PLCB3, and ACAT2. Survival analysis indicated that the patients in the high-risk group had a worse survival than those in the low-risk group, which is consistent with the results in validated cohorts. And receiver operating characteristic (ROC) curve analysis further validated the prognostic value and predictive performance of the signature. In addition, the two risk groups had significantly different clinicopathological characteristics and immune cell infiltration status. Notably, the low-risk group is more likely to respond to immunotherapy.

Conclusion: Overall, this study systematically explored the prognostic value of glucose metabolism and generated a prognostic risk signature with favorable efficacy and accuracy, which help select candidate patients and explore potential therapeutic approaches targeting the reprogrammed glucose metabolism in LUAD.

Development of a 5-Gene Signature to Evaluate Lung Adenocarcinoma Prognosis Based on the Features of Cancer Stem Cells

Cancer stem cells (CSCs) can induce recurrence and chemotherapy resistance of lung adenocarcinoma (LUAD). Reliable markers identified based on CSC characteristic of LUAD may improve patients' chemotherapy response and prognosis. OCLR was used to calculate mRNA expression-based stemness index (mRNAsi) of LUAD patients' data in TCGA. Association analysis of mRNAsi was performed with clinical features, somatic mutation, and tumor immunity. A prognostic prediction model was established with LASSO Cox regression. Kaplan-Meier Plotter (KM-plotter) and time-dependent ROC were applied to assess signature performance. For LUAD, univariate and multivariate Cox analysis was performed to identify independent prognostic factors. LUAD tissues showed a noticeably higher mRNAsi in than nontumor tissues, and it showed significant differences in T, N, M, AJCC stages, and smoking history. The most frequently mutated gene was TP53, with a higher mRNAsi relating to more frequent mutation of TP53. The mRNAsi was significantly negatively correlated with immune score, stromal score, and ESTIMATE score in LUAD. The blue module was associated with mRNAsi. The 5-gene signature was confirmed as an independent indicator of LUAD prognosis that could promote personalized treatment of LUAD and accurately predict overall survival (OS) of LUAD patients.

BTG2 Serves as a Potential Prognostic Marker and Correlates with Immune Infiltration in Lung Adenocarcinoma

Background

B-cell translocation gene 2 (BTG2) has been revealed to be involved in the occurrence and development of multiple cancers. However, the role of BTG2 in lung adenocarcinoma (LUAD) is still ambiguous. Thus, this study aims to investigate the prognostic value of BTG2 and its correlation with immune infiltration in LUAD.

Methods

The expression of BTG2 in LUAD was analyzed using the TIMER and UALCAN databases. The correlations between BTG2 expression and clinicopathological factors were investigated using the UALCAN databases. The Kaplan–Meier plotter, GEPIA, and TCGA databases were employed to assess the prognostic value of BTG2. The STRING database and Cytoscape software were used to construct an interaction network and mine co-expression genes. The TISIDB database was examined for a correlation between BTG2 and driver genes in LUAD. Enrichment analysis of co-expressed genes and BTG2 was performed using the LinkedOmics database. Finally, the correlations between BTG2 and immune infiltrates were investigated using the TIMER, GEO, and TISIDB database.

Results

BTG2 was significantly downregulated in LUAD. The decreased expression of BTG2 in LUAD was significantly correlated with higher cancer stages and shorter duration of overall survival. The expressions of BTG2-related co-expression genes were associated with the prognosis in LUAD. The expression of BTG2 was closely associated with the mutations of TP53 and ROS1. Enrichment analysis revealed that BTG2 was significantly correlated with immune‐associated signaling pathways and function. In addition, the expression of BTG2 was found to be closely related to immune infiltration, multiple gene markers of immune cells, chemokines, and chemokine receptors.

Conclusion

Our findings have effectively demonstrated that BTG2 expression was downregulated in LUAD, indicating poor prognosis. Closely relating to immune cell infiltration, BTG2 may be a promising immune-related biomarker and molecular target for patients with LUAD.

Prognostic model of AU-rich genes predicting the prognosis of lung adenocarcinoma

Background

AU-rich elements (ARE) are vital cis-acting short sequences in the 3’UTR affecting mRNA stability and translation. The deregulation of ARE-mediated pathways can contribute to tumorigenesis and development. Consequently, ARE-genes are promising to predict prognosis of lung adenocarcinoma (LUAD) patients.

Methods

Differentially expressed ARE-genes between LUAD and adjacent tissues in TCGA were investigated by Wilcoxon test. LASSO and Cox regression analyses were performed to identify a prognostic genetic signature. The genetic signature was combined with clinicopathological features to establish a prognostic model. LUAD patients were divided into high- and low-risk groups by the model. Kaplan–Meier curve, Harrell’s concordance index (C-index), calibration curves and decision curve analyses (DCA) were used to assess the model. Function enrichment analysis, immunity and tumor mutation analyses were performed to further explore the underlying molecular mechanisms. GEO data were used for external validation.

Results

Twelve prognostic genes were identified. The gene riskScore, age and stage were independent prognostic factors. The high-risk group had worse overall survival and was less sensitive to chemotherapy and radiotherapy (P < 0.01). C-index and calibration curves showed good performance on survival prediction in both TCGA (1, 3, 5-year ROC: 0.788, 0.776, 0.766) and the GSE13213 validation cohort (1, 3, 5-year ROC: 0.781, 0.811, 0.734). DCA showed the model had notable clinical net benefit. Furthermore, the high-risk group were enriched in cell cycle, DNA damage response, multiple oncological pathways and associated with higher PD-L1 expression, M1 macrophage infiltration. There was no significant difference in tumor mutation burden (TMB) between high- and low-risk groups.

Conclusion

ARE-genes can reliably predict prognosis of LUAD and may become new therapeutic targets for LUAD.

A Novel Approach for the Discovery of Biomarkers of Radiotherapy Response in Breast Cancer

Radiotherapy (RT) is an important treatment modality for the local control of breast cancer (BC). Unfortunately, not all patients that receive RT will obtain a therapeutic benefit, as cancer cells that either possess intrinsic radioresistance or develop resistance during treatment can reduce its efficacy. For RT treatment regimens to become personalised, there is a need to identify biomarkers that can predict and/or monitor a tumour's response to radiation. Here we describe a novel method to identify such biomarkers. Liquid chromatography-mass spectrometry (LC-MS) was used on conditioned media (CM) samples from a radiosensitive oestrogen receptor positive (ER⁺) BC cell line (MCF-7) to identify cancer-secreted biomarkers which reflected a response to radiation. A total of 33 radiation-induced secreted proteins that had higher (up to 12-fold) secretion levels at 24 h post-2 Gy radiation were identified. Secretomic results were combined with whole-transcriptome gene expression experiments, using both radiosensitive and radioresistant cells, to identify a signature related to intrinsic radiosensitivity. Gene expression analysis assessing the levels of the 33 proteins showed that 5 (YBX3, EIF4EBP2, DKK1, GNPNAT1 and TK1) had higher expression levels in the radiosensitive cells compared to their radioresistant derivatives; 3 of these proteins (DKK1, GNPNAT1 and TK1) underwent in-lab and initial clinical validation. Western blot analysis using CM samples from cell lines confirmed a significant increase in the release of each candidate biomarker from radiosensitive cells 24 h after treatment with a 2 Gy dose of radiation; no significant increase in secretion was observed in the radioresistant cells after radiation. Immunohistochemistry showed that higher intracellular protein levels of the biomarkers were associated with greater radiosensitivity. Intracellular levels were further assessed in pre-treatment biopsy tissues from patients diagnosed with ER⁺ BC that were subsequently treated with breast-conserving surgery and RT. High DKK1 and GNPNAT1 intracellular levels were associated with significantly increased recurrence-free survival times, indicating that these two candidate biomarkers have the potential to predict sensitivity to RT. We suggest that the methods highlighted in this study could be utilised for the identification of biomarkers that may have a potential clinical role in personalising and optimising RT dosing regimens, whilst limiting the administration of RT to patients who are unlikely to benefit.

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.