Value In Lung Adenocarcinoma Research Articles

S100A16 stabilizes the ITGA3‑mediated ECM‑receptor interaction pathway to drive the malignant properties of lung adenocarcinoma cells via binding MOV10.

Lung adenocarcinoma (LUAD) is highly associated with lung cancer‑associated mortality. Notably, S100 calcium‑binding protein A16 (S100A16) has been increasingly considered to have prognostic value in LUAD; however, the underlying mechanism remains unknown. In the present study, S100A16 expression levels in LUAD tissues and cells were respectively analyzed by the UALCAN database and western blotting. Cell Counting Kit‑8 and 5‑ethynyl‑2'‑deoxyuridine assays were used to examine cell proliferation, whereas wound healing, Transwell and tube formation assays were used to assess cell migration, invasion and angiogenesis, respectively. Western blotting was also used to examine the expression levels of proteins associated with metastasis, angiogenesis, focal adhesion and the extracellular matrix (ECM)‑receptor interaction pathways. The relationship between S100A16 and Mov10 RNA helicase (MOV10) was predicted by bioinformatics tools, and was verified using a co‑immunoprecipitation assay. Furthermore, the interaction between MOV10 and integrin α3 (ITGA3) was verified by RNA immunoprecipitation assay, and the actinomycin D assay was used to detect ITGA3 mRNA stability. The results demonstrated that S100A16 expression was increased in LUAD tissues and cell lines, and was associated with unfavorable outcomes. Knocking down S100A16 expression hindered the proliferation, migration, invasion and angiogenesis of LUAD cells. Furthermore, S100A16 was shown to bind to MOV10 and positively modulate MOV10 expression in LUAD cells, while MOV10 overexpression partially reversed the suppressive role of S100A16 knockdown on the aggressive phenotypes of LUAD cells. Furthermore, it was demonstrated that S100A16 regulated the stability of ITGA3 mRNA via MOV10 to mediate ECM‑receptor interactions. In conclusion, S100A16 may bind to MOV10 to stabilize ITGA3 mRNA and regulate ECM‑receptor interactions, hence contributing to the malignant progression of LUAD.

Comprehensive analysis identifies YKT6 as a potential prognostic and diagnostic biomarker in lung adenocarcinoma

BackgroundLung cancer is the most common cause of cancer-related death worldwide. The most prevalent histological subtype of lung cancer is lung adenocarcinoma (LUAD), with incidence rising each year. Treating LUAD remains a significant issue due to a lack of early diagnosis and poor therapy outcomes. YKT6 is a member of the SNARE protein family, whose clinical value and biological function in LUAD has yet to be established.MethodsTCGA, HPA and UALCAN were used to analyze YKT6 mRNA and protein levels, the correlation between YKT6 expression and clinicopathological features and prognosis. YKT6 mRNA and protein expression were verified by qRT-PCR, immunohistochemistry (IHC) and tissue microarrays (TMA). Additionally, lung cancer cell lines were chosen for YKT6 silencing to explore the effects on cell proliferation and migration. The cBioPortal was used to select YKT6-related genes. Protein-protein interaction (PPI) network was created based on STRING database and hub genes were screened, with their expression levels and prognosis values in LUAD analyzed accordingly. YKT6-related genes were enriched by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses.ResultsIn LUAD, YKT6 was distinctly highly expressed with relation to clinical features of staging, smoking, lymph node metastasis, and TP53 mutation. Elevated YKT6 expression was linked to adverse prognosis, serving as an independent unfavorable prognostic factor. Moreover, YKT6 presented high diagnostic value in LUAD patients (AUC = 0.856). Experimental validation indicated that freshly collected LUAD tissues showed significantly high mRNA expression of YKT6. IHC and TMA verified increased YKT6 protein level in LUAD. Knockdown of YKT6 inhibited cell proliferation and promoted apoptosis, with mitigated capability of migration and invasion. The top ten hub genes screened by PPI network were highly expressed in LUAD, and significantly associated with poor prognosis. GO and KEGG analyses showed that YKT6-related genes were mainly involved in cell cycle.ConclusionElevated YKT6 expression is related to poor prognosis of LUAD patients. YKT6 can serve as a novel biomarker for LUAD diagnosis and prognosis. Cell proliferation, migration and invasion was impaired with increased apoptosis upon YKT6 silencing in lung cancer cells. In summary, this study comprehensively uncovered that YKT6 could be identified as a potential prognostic and diagnostic biomarker in LUAD.

G0 arrest gene patterns to predict the prognosis and drug sensitivity of patients with lung adenocarcinoma.

G0 arrest (G0A) is widely recognized as a crucial factor contributing to tumor relapse. The role of genes related to G0A in lung adenocarcinoma (LUAD) was unclear. This study aimed to develop a gene signature based on for LUAD patients and investigate its relationship with prognosis, tumor immune microenvironment, and therapeutic response in LUAD. We use the TCGA-LUAD database as the discovery cohort, focusing specifically on genes associated with the G0A pathway. We used various statistical methods, including Cox and lasso regression, to develop the model. We validated the model using bulk transcriptome and single-cell transcriptome datasets (GSE50081, GSE72094, GSE127465, GSE131907 and EMTAB6149). We used GSEA enrichment and the CIBERSORT algorithm to gain insight into the annotation of the signaling pathway and the characterization of the tumor microenvironment. We evaluated the response to immunotherapy, chemotherapy, and targeted therapy in these patients. The expression of six genes was validated in cell lines by quantitative real-time PCR (qRT-PCR). Our study successfully established a six-gene signature (CHCHD4, DUT, LARP1, PTTG1IP, RBM14, and WBP11) that demonstrated significant predictive power for overall survival in patients with LUAD. It demonstrated independent prognostic value in LUAD. To enhance clinical applicability, we developed a nomogram based on this gene signature, which showed high reliability in predicting patient outcomes. Furthermore, we observed a significant association between G0A-related risk and tumor microenvironment as well as drug susceptibility, highlighting the potential of the gene signature to guide personalized treatment strategies. The expression of six genes were significantly upregulated in the LUAD cell lines. This signature holds the potential to contribute to improved prognostic prediction and new personalized therapies specifically for LUAD patients.

Unraveling CASTOR1's tumor suppressive role: Genetic ablation of CASTOR1 and KRAS-driven mouse model of non-small cell lung cancer.

8528 Background: Cytosolic Arginine Sensor for mTORC1 Subunit 1 (CASTOR1) has emerged as a potent suppressor of mTORC1 activity, playing a pivotal role in various cancer contexts. Previously implicated as a tumor suppressor in multiple cancers, CASTOR1 is tightly regulated in cancer, including inhibition by viral miRNAs in KSHV-induced cancer and degradation via AKT-phosphorylation and RNF167-mediated ubiquitination. Notably, CASTOR1's role extends beyond normal cellular contexts, as evidenced by its tumor-suppressive function in breast cancer xenograft models and its prognostic value in lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). This study aims to elucidate CASTOR1’s role in a transgenic Kras-driven LUAD mouse model, shedding light on its tumor-suppressive potential. Methods: Utilizing a novel CASTOR1 knockout (KO) mouse model crossed with the Kras (LSL-KrasG12D/+) NSCLC model, we established a new mouse model with simultaneous Kras activation and homozygous CASTOR1 ablation. Tumor incidence and growth were monitored in Kras mice harboring wild-type (WT) and KO CASTOR1. Further investigations examined the proliferative dynamics and activation status of various oncogenic pathways, and assessed mTORC1 inhibitor sensitivities in human LUAD cell lines with altered CASTOR1 expression levels. Results: CASTOR1 deficiency markedly heightened lung tumor incidence and size, concomitant with an elevated proliferative index. Intriguingly, a novel antibody identified robust pCASTOR1 levels in CASTOR1 WT mouse LUAD tumors, positively correlated with tumor stage and inversely related to total CASTOR1 abundance, hinting at an active degradation mechanism promoting tumor progression. Elevated mTORC1 signaling, evidenced by increased levels of p4EBP and pS6, underscored the pathway's activation consequent to CASTOR1 loss, likely driving tumor development. Furthermore, heightened pERK levels in CASTOR1 KO tumors unveiled a novel feedback loop intertwining CASTOR1/mTORC1 and the Kras/ERK pathway. Notably, treatment with mTORC1 inhibitors sensitized organoids of Kras tumors with mTORC1 hyperactivation to Kras G12D inhibitor. Conclusions: Our findings elucidate CASTOR1's tumor-suppressive role in Kras-dependent mouse LUAD tumors, suggesting the potential efficacy of combined Kras and mTORC1 inhibition in LUAD subsets characterized by hyperactivated Kras and diminished CASTOR1 levels or mTORC1 hyperactivity.

Characterization of an endoplasmic reticulum stress-associated lncRNA prognostic signature and the tumor-suppressive role of RP11-295G20.2 knockdown in lung adenocarcinoma

Endoplasmic reticulum stress (ERS) is commonly induced by accumulating misfolded or unfolded proteins in tumor microenvironment. Long non-coding RNAs (lncRNAs) play important roles in ERS response and lung adenocarcinoma (LUAD) progression. However, the role of ERS-related lncRNAs in LUAD remains unknown. In this study, we aimed to identify ERS-associated lncRNAs with prognostic value in LUAD and characterize their clinical implications. Cox and least absolute shrinkage and selection operator regression analyses identified nine ERS-related lncRNAs with independent prognostic abilities, including five protective factors (CROCCP2, KIAA0125, LINC0996, RPARP-AS1 and TBX5-AS1) and four risk factors (LINC0857, LINC116, RP11-21L23.2 and RP11-295G20.2). We developed an ERS-related lncRNA risk prediction model in predicting overall survival of LUAD patients, which classified TCGA cohorts into high-risk (HS) and low-risk (LS) groups. Comprehensive bioinformatic analyses revealed HS patients featured with late-stage tumors, greater mutation burdens, weaker anti-tumor immunity/responses, and lower sensitivity to targeted drugs compared to LS patients, contributing to tumor progression and a poor prognosis. Functional enrichment analysis implicated these ERS-related lncRNAs in cell migration, cell death, and immunity. Furthermore, expression of the most significantly upregulated risk lncRNA, RP11-295G20.2, was validated at the mRNA level using clinical LUAD samples. Knockdown of RP11-295G20.2 obviously reduced ERS and suppressed proliferation, invasion, and migration of LUAD cells. This novel ERS-related lncRNA signature provides a new biomarker for prognostic prediction, and ERS-associated RP11-295G20.2 serves as a potential therapeutic target in LUAD.

ITGB4 is a prognostic biomarker and correlated with lung adenocarcinoma brain metastasis.

The aim of this study is to explore the prognostic value and immune signature of ITGB4 expression in lung adenocarcinoma (LUAD) brain metastasis. We comprehensively screened genes associated with LUAD brain metastasis by integrating datasets from the GEO database and TMT-based quantitative proteomics profiles. Univariable survival and Multivariate Cox analysis was used to compare several clinical characteristics with survival, and a risk model was constructed. The biological functions were explored via GO and KEGG analysis. Gene set enrichment analysis (GSEA) was performed using the TCGA dataset. In addition, we use TIMER to explore the collection of ITGB4 Expression and Immune Infiltration Level in LUAD. The ability of ITGB4 to regulate tumor metastasis was further assessed by migration, invasion assay and Western-blot in H1975-BrM4 cells. We found that ITGB4 was the only gene with high clinical diagnostic and prognostic value in LUAD. Enrichment analysis indicated that ITGB4 is associated with brain metastasis, infiltration of immune cells, and the response to immunotherapy. ITGB4 expression can effectively predict the outcomes of patients with LUAD who are receiving anti-PD-1 therapy. ITGB4 knockdown inhibited the invasion, migration of H1975-BrM4 brain metastasis cells, as well as epithelial-mesenchymal transition (EMT) abilities. The heightened expression of ITGB4 protein was shown to promote EMT and enhance the metastatic potential. ITGB4 promotes the progression in H1975-BrM4 cells via MEK/ERK signaling pathway. Our findings indicate that the expression of ITGB4 is linked to the occurrence of brain metastasis and infiltration of immune cells, suggesting that ITGB4 might be a clinical treatment target for LUAD.

Human epididymis protein 4, a novel potential biomarker for diagnostic and prognosis monitoring of lung cancer.

This study aimed to explore the application value of human epididymis protein 4 (HE4) in diagnosing and monitoring the prognosis of lung cancer. First, TCGA (The Cancer Genome Atlas) databases were used to analyze whey-acidic-protein 4-disulfide bond core domain 2 (WFDC2) gene expression levels in lung cancer tissues. Then, a total of 160 individuals were enrolled, categorized into three groups: the lung cancer group (n = 80), the benign lesions group (n = 40), and the healthy controls group (n = 40). Serum HE4 levels and other biomarkers were quantified using an electro-chemiluminescent immunoassay. Additionally, the expression of HE4 in tissues was analyzed through immunohistochemistry (IHC). In vitro cultures of human airway epithelial (human bronchial epithelial [HBE]) cells and various lung cancer cell lines (SPC/PC9/A594/H520) were utilized to detect HE4 levels via western blot (WB). Analysis of the TCGA and UALCAN (The University of Alabama at Birmingham Cancer Data Analysis Portal) databases showed that WFDC2 gene expression levels were upregulated in lung cancer tissues (p < 0.01). Compared with the control group and the benign group, HE4 was significantly higher in the serum of patients with lung cancer (p < 0.001). Receiver operating characteristic (ROC) analysis confirmed that HE4 had better diagnostic efficacy than classical markers in the differential diagnosis of lung cancer and benign lesions and had the highest diagnostic value in lung adenocarcinoma (area under the ROC curve [AUC] = 0.826). HE4 increased in early lung cancer and positively correlated with poor prognosis (p < 0.001). Moreover, the results of WB and IHC revealed that the expression of HE4 was increased in lung cancer cells (SPC/A549/H520) and lung cancer tissues but decreased in PC9 cells with a lack of exon EGFR19 (p < 0.05). Serum HE4 emerges as a promising novel biomarker for the diagnosis and prognosis assessment of lung cancer.

SFXN1-mediated immune cell infiltration and tumorigenesis in lung adenocarcinoma: A potential therapeutic target

BackgroundSideroflexin 1 (SFXN1), a mitochondrial serine transporter implicated in one-carbon metabolism, is a prognostic biomarker in lung adenocarcinoma (LUAD). However, its role in LUAD progression remains elusive. This study aimed to investigate the functional significance of SFXN1 in LUAD and evaluate its potential as a therapeutic target. MethodsWe analyzed SFXN1 expression and its diagnostic and prognostic value in LUAD using the Pan-cancer TCGA dataset. In vitro assays (CCK-8, cell cycle, EDU, wound-healing, and transwell) were employed to assess the role of SFXN1, complemented by in vivo experiments. RNA sequencing elucidated SFXN1-mediated cellular functions and potential mechanisms. Bulk RNA-seq and scRNA-seq data from TCGA and GEO were used to investigate the correlation between SFXN1 and the tumor immune microenvironment. RT-qPCR, Western blot, and IHC assays validated SFXN1 expression and its impact on the immune microenvironment in LUAD. ResultsSFXN1 was upregulated in LUAD tissues and associated with poor prognosis. RNA-seq and scRNA-seq analyses revealed increased SFXN1 expression in tumor cells, accompanied by decreased infiltration of NK and cytotoxic T cells. SFXN1 knockdown significantly reduced cell proliferation and migration, and the inhibition of ERK phosphorylation and CCL20 expression may be the molecular mechanism involved. In vivo, targeting SFXN1 decreased Tregs infiltration and inhibited tumor growth. ConclusionsOur findings suggest that SFXN1 may be a potential therapeutic target for LUAD treatment.

SLC7A8 overexpression inhibits the growth and metastasis of lung adenocarcinoma and is correlated with a dismal prognosis.

Overexpression of solute carrier family 7 member 8 (SLC7A8) has been shown to relate to the survival time and tumor progression in cancer patients. However, the role of SLC7A8 in lung adenocarcinoma (LUAD) is still obscure. The relationships between SLC7A8 expression in LUAD tissues and clinical values as well as immune infiltration were explored through bioinformatics. The functions and pathways of SLC7A8 in LUAD were investigated using Kyoto Encyclopedia of Genes and Genomes enrichment analysis, Gene Set Enrichment Analysis, Western blotting, and other methods. We found that the expression of SLC7A8 was decreased significantly in LUAD tissues compared with normal tissues, which was related to the dismal survival time and disease progression. Moreover, it carried diagnostic value in LUAD and was a risk factor for dismal prognosis. Receiver operating characteristic curve analysis indicated that the expression level of SLC7A8 carried significant diagnostic value in LUAD. Overexpression of SLC7A8 inhibited the proliferation, invasion, and migration of LUAD cells, likely through a mechanism involving the cell cycle. SLC7A8 expression in LUAD was significantly correlated with the infiltration of immune cells, especially B cells, interstitial dendritic cells, mast cells, CD56 bright cells, natural killer cells, plasmacytoid dendritic cells, T follicular helper cells, T helper 2 and 17 cells, and immune factors. The downregulation of SLC7A8 was related to a dismal prognosis and immune cell infiltration in LUAD. Increasing the expression of SLC7A8 inhibited the growth and migration of LUAD cells, thereby improving the prognosis of patients.

NCAPD2 is a favorable predictor of prognostic and immunotherapeutic biomarker for multiple cancer types including lung cancer

BackgroundNon-SMC condensin I complex subunit D2 (NCAPD2) belongs to the chromosomal structural maintenance family. While the different contribution of NCAPD2 to chromosome in mitosis have been thoroughly investigated, much less is known about the expression of NCAPD2 in pan-cancer. Thus, we used a bioinformatics dataset to conduct a pan-cancer analysis of NCAPD2 to determine its regulatory role in tumors.MethodsMultiple online databases were analyzed NCAPD2 gene expression, protein level, patient survival and functional enrichment in pan-cancer. Genetic alteration and tumor stemness of NCAPD2 were analyzed using cBioPortal and SangerBox. The GSCA and CellMiner were used to explore the relationship between NCAPD2 and drug sensitivity. The diagnostic value of prognosis was evaluated by ROC curve. Subsequently, the immune infiltration level and immune subtype of NCAPD2 in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) were analyzed using TIMER1 and TISIDB.ResultsNCAPD2 gene expression was significantly higher in most cancers and associated with clinical stage and poor prognosis. Genomic heterogeneity of NCAPD2 promoted the occurrence and development of tumors. GO enrichment analysis suggested NCAPD2 might be involved in DNA repair and immune response. NCAPD2 was involved in immune infiltration of LUAD and LUSC. ROC curves showed that NCAPD2 has important prognosis diagnostic value in LUAD and LUSC. Moreover, NCAPD2 was drug sensitive to topotecan, which may be an optimize immunotherapy.ConclusionsIt was found that NCAPD2 was overexpressed in pan-cancers, which was associated with poor outcomes. Importantly, NCAPD2 could be a diagnostic marker and an immune related biomarker for LUAD and LUSC.

Overexpression of MTFR1 promotes cancer progression and drug-resistance on cisplatin and is related to the immune microenvironment in lung adenocarcinoma.

The roles of MTFR1 in the drug resistance of lung adenocarcinoma (LAC) to cisplatin remain unexplored. In this study, the expression, clinical values and mechanisms of MTFR1 were explored, and the relationship between MTFR1 expression and immune microenvironment was investigated in LAC using bioinformatics analysis, cell experiments, and meta-analysis. MTFR1 expression and clinical values, and the relationship between MTFR1 expression and immunity were explored, through bioinformatics analysis. The effects of MTFR1 on the growth, migration and cisplatin sensitivity of LAC cells were identified using cell counting kit-8, wound healing and Transwell experiments. Additionally, the mechanisms of drug resistance of LAC cells involving MTFR1 were investigated using western blotting. MTFR1 was elevated in LAC tissues. MTFR1 overexpression was associated with sex, age, primary therapy outcome, smoking, T stage, unfavourable prognosis and diagnostic value and considered an independent risk factor for an unfavourable prognosis in patients with LAC. MTFR1 co-expressed genes involved in the cell cycle, oocyte meiosis, DNA replication and others. Moreover, interfering with MTFR1 expression inhibited the proliferation, migration and invasion of A549 and A549/DDP cells and promoted cell sensitivity to cisplatin, which was related to the inhibition of p-AKT, p-P38 and p-ERK protein expression. MTFR1 overexpression was associated with stromal, immune and estimate scores along with natural killer cells, pDC, iDC and others in LAC. MTFR1 overexpression was related to the unfavourable prognosis, diagnostic value and immunity in LAC. MTFR1 also participated in cell growth and migration and promoted the drug resistance of LAC cells to cisplatin via the p-AKT and p-ERK/P38 signalling pathways.

Diagnostic value of plasma circular RNA based on droplet digital polymerase chain reaction in lung adenocarcinoma.

Plasma circular (circ)RNAs detected by droplet digital polymerase chain reaction (ddPCR) may be ideal markers for liquid biopsy. However, ddPCR detection of circRNAs in plasma for diagnosis of lung adenocarcinoma has been rarely reported. An RNA sequencing analysis was performed in plasma from patients with early lung adenocarcinoma and healthy individuals. Droplet digital PCR was used to verify the differentially expressed genes. The copy numbers of circle RNALZIC (circLZIC)and circle RNACEP350 (circCEP350) in the plasma of lung adenocarcinoma patients were significantly higher than in plasma of healthy people, and the copy numbers in postoperative plasma of the same patients were significantly lower than those in preoperative plasma. CircLZIC and circCEP350 alone and in combination had diagnostic value in lung adenocarcinoma and early lung adenocarcinoma. CircLZIC and circCEP350 had more binding sites with multiple microRNAs. Their target genes were enriched in several signaling pathways. The copy numbers of circLZIC and circCEP350 were higher in plasma of lung adenocarcinoma patients than in plasma of healthy controls, significantly correlated with tumor size and TNM stage, and closely related to the occurrence and development of tumors. These circRNAs may serve as molecular markers for the diagnosis of lung adenocarcinoma.

Recognition and combination of multiple cell-death features showed good predictive value in lung adenocarcinoma

BackgroundCell death is a key regulatory process in organisms and its study has become increasingly important in the field of cancer. While prior research has primarily centered on the individual pathways of cell death in cancer, there has been a lack of comprehensive investigation into the synergistic effects of multiple cell death pathways. MethodsGenes related to autophagy, apoptosis, necroptosis, pyroptosis, and cuproptosis was selected, and patients’ data was collected from The Cancer Genome Atlas (TCGA)project. Cell death features were identified using principal component analysis and combined to create a composite score. A scalable prediction model was then created using LASSO regression after a thorough assessment of the composite scores. The model was subsequently validated across multiple external datasets to establish its robustness and reliability. ResultsThe cell death features effectively represented the gene expression patterns in the samples. The composite score well predicted prognosis, clinical stage, mutation, tumor microenvironment, and immunotherapy effectiveness. The model built on composite scores accurately predicted prognosis and immunotherapy effectiveness across multiple datasets. GJB2 was identified as a potential biomarker. ConclusionModels based on multiple cell death pathways have significant predictive power for prognosis and immunotherapy effectiveness in lung adenocarcinoma. This highlights the synergistic role of multiple cell death pathways in cancer development and offers a new perspective for cancer research.

Soluble galectin-3 as a microenvironment-relevant immunoregulator with prognostic and predictive value in lung adenocarcinoma.

Despite the success of therapies in lung cancer, more studies of new biomarkers for patient selection are urgently needed. The present study aims to analyze the role of galectin-3 (GAL-3) in the lung tumor microenvironment (TME) using tumorspheres as a model and explore its potential role as a predictive and prognostic biomarker in non-small cell lung cancer patients. For in vitro studies, lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) primary cultures from early-stage patients and commercial cell lines were cultured, using tumorsphere-forming assays and adherent conditions for the control counterparts. We analyzed the pattern of secretion and expression of GAL-3 using reverse transcription-quantitative real-time PCR (RTqPCR), immunoblot, immunofluorescence, flow cytometry, and immunoassay analysis. Our results using three-dimensional (3D) models of lung tumor cells revealed that soluble GAL-3 (sGAL-3) is highly expressed and secreted. To more accurately mimic the TME, a co-culture of tumorspheres and fibroblasts was used, revealing that GAL-3 could be important as an immunomodulatory molecule expressed and secreted in the TME, modulating immunosuppression through regulatory T cells (TREGS ). In the translational phase, we confirmed that patients with high expression levels of GAL-3 had more TREGS , which suggests that tumors may be recruiting this population through GAL-3. Next, we evaluated levels of sGAL-3 before surgery in LUAD and LUSC patients, hypothesizing that sGAL-3 could be used as an independent prognostic biomarker for overall survival and relapse-free survival in early-stage LUAD patients. Additionally, levels of sGAL-3 at pretreatment and first response assessment from plasma to predict clinical outcomes in advanced LUAD and LUSC patients treated with first-line pembrolizumab were evaluated, further supporting that sGAL-3 has a high efficiency in predicting durable clinical response to pembrolizumab with an area under curve of 0.801 (P = 0.011). Moreover, high levels might predict decreased progression-free survival and OS to anti-PD-1 therapy, with sGAL-3 being a prognosis-independent biomarker for advanced LUAD.

Unraveling SARS-CoV-2-associated lncRNAs' prognostic significance in lung adenocarcinoma-survival, immunity, and chemotherapy responses

BackgroundThis study investigates the link between SARS-CoV-2-associated long non-coding RNAs (lncRNAs) and lung adenocarcinoma (LUAD). LUAD is a prevalent and aggressive lung cancer type. The study aims to identify prognostic lncRNAs and construct a predictive model while shedding light on potential therapeutic targets during the COVID-19 era. ResultsEight SARS-CoV-2-associated lncRNAs with significant prognostic value in LUAD were identified, forming a robust prognostic risk model. The model exhibited strong predictive performance, with high area under the ROC curve (AUC) values at one, three, and five years. Furthermore, the risk score was an independent prognostic factor, correlating with the cancer stage. Notably, differences in immune function, drug sensitivity, and immune checkpoint expression were observed between high- and low-risk groups. ConclusionsThis study unveils eight SARS-CoV-2-associated lncRNAs as valuable prognostic markers in LUAD, yielding a reliable prognostic risk model. Additionally, the model's ability to predict patient outcomes and its correlation with cancer stage underscores its clinical utility. The observed variances in immune function, drug sensitivity, and immune checkpoint expression suggest potential avenues for personalized LUAD treatment strategies. Clinicians can utilize the prognostic risk model to predict LUAD patient outcomes, informing treatment decisions. The insights into immune function, drug sensitivity, and immune checkpoints offer opportunities for tailored therapies, potentially enhancing patient outcomes. This study underscores the importance of considering the interplay between SARS-CoV-2-associated factors and cancer biology, especially in the context of the COVID-19 pandemic.How to cite: Zhou Q, Yuan T, Xie Z, et al. Unraveling SARS-CoV-2-associated lncRNAs' prognostic significance in lung adenocarcinoma-survival, immunity, and chemotherapy responses. Electron J Biotechnol 2024;67. https://doi.org/10.1016/j.ejbt.2023.10.001.

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma.

Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy. This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites. Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models. To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

Identification of CDT1 as a prognostic marker in human lung adenocarcinoma using bioinformatics approaches.

Lung cancer has the highest cancer-related mortality worldwide. Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC). Chromatin licensing and DNA replication factor 1 (CDT1), a key regulator of cell cycle control and replication in eukaryotic cells, has been implicated in various cancer-related processes. Given its significant role in cancer, the focus on CDT1 in this study is justified as it holds promise as a potential biomarker or therapeutic target for cancer treatment. However, its prognostic value in lung adenocarcinoma (LUAD) remains unclear. Bioinformatics analysis was conducted using data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were utilized to predict biological processes and signaling pathways, respectively. The LinkedOmics database was employed to identify differentially expressed genes (DEGs) associated with CDT1. Nomograms and Kaplan-Meier plots were generated to assess the survival rates of patients with lung adenocarcinoma (LUAD). To determine the RNA and protein expression levels of CDT1 in LUAD and adjacent normal tissues, quantitative polymerase chain reaction (qPCR) and immunohistochemistry techniques were employed, respectively. CDT1 was upregulated in the vast majority of cancer tissues, based on pan-cancer analysis in TCGA and GEO datasets, as to lung cancer, the level of CDT1 expression was much higher in LUAD tissue than in healthy lung tissue. Our clinical data supported these findings. In our study, we used a specific cutoff value to dichotomize the patient samples into high and low CDT1 expression groups. The Kaplan-Meier survival curve revealed poor survival rates in CDT1 high expression group than the low expression group. To determine if CDT1 expression was an independent risk factor in LUAD patients, univariate and multivariate Cox regression analyses were performed. The result showed that CDT1 was a potential novel prognosis factor for LUAD patients, whose prognosis was poorer when CDT1 expression was higher. Based on functional enrichment analysis, highly expressed DEGs of CDT1-high patients were predicted to be involved in the cell cycle. According to our analysis of immune infiltration, CDT1 exhibited a strong correlation with specific immune cell subsets and was found to be a significant predictor of poor survival in patients with LUAD. Our research found that CDT1 was upregulated in LUAD and that high CDT1 expression predicted poor prognosis. We comprehensively and systematically analyzed the expression level in the datasets as well as in our own clinical samples, we also evaluated the prognostic and diagnostic value of CDT1, and finally, the potential mechanisms of CDT1 in the progression of LUAD. These results suggested that CDT1 may be a prognostic marker and therapeutic target for LUAD.

Prognostic and immune correlation analysis of mitochondrial autophagy and aging-related genes in lung adenocarcinoma.

Mitophagy and aging (MiAg) are very important pathophysiological mechanisms contributing to tumorigenesis. MiAg-related genes have prognostic value in lung adenocarcinoma (LUAD). However, prognostic, and immune correlation studies of MiAg-related genes in LUAD are lacking. MiAg differentially expressed genes (DEGs) in LUAD were obtained from public sequencing datasets. A prognostic model including MiAg DEGs was constructed according to patients divided into low- and high-risk groups. Gene Ontology, gene set enrichment analysis, gene set variation analysis, CIBERSORT immune infiltration analysis, and clinical characteristic correlation analyses were performed for functional annotation and correlation of MiAgs with prognosis in patients with LUAD. Seven MiAg DEGsof LUAD were identified: CAV1, DSG2, DSP, MYH11, NME1, PAICS, PLOD2, and the expression levels of these genes were significantly correlated (P < 0.05). TheRiskScoreof theMiAgDEGprognostic modeldemonstrated high predictive ability of overall survival of patients diagnosed with LUAD. Patients with high and low MiAg phenotypic scores exhibitedsignificant differences in the infiltration levels of eight types of immune cells (P < 0.05). The multi-factor DEG regression model showed higher efficacy in predicting 5-year survival than 3- and 1-year survival of patients with LUAD. Seven MiAg-related genes were identified to be significantly associated with the prognosis of patients diagnosed with LUAD. Moreover, the identified MiAg DEGs might affect the immunotherapy strategy of patients with LUAD.

Identification of pivotal genes with prognostic evaluation value in lung adenocarcinoma by bioinformatics analysis.

Lung cancer remains the leading cause of cancer morbidity and mortality worldwide, and over-diagnosis causes various unnecessary losses in patients' lives and health. How to more effectively screen lung cancer patients and their potential prognostic risk become the focus of our current study. By analyzing the LUAD expression profile in The Cancer Genome Atlas (TCGA), we constructed a weighted gene co-expression network using differentially expressed genes (DEGs) to find the key modules and pivotal genes. A COX proportional risk regression model based on the least absolute shrinkage and selection operator (LASSO) was used to assess the predictive value of the model for the prognosis of LUAD patients. A total of 4107 up-regulated DEGs and 2022 down-regulated DEGs were identified in this study, and enrichment analysis showed that these analyzes were associated with the extracellular matrix of cells and adhesion. Ten gene markers consisting of LDHA, TOP2A, UBE2C, TYMS, TRIP13, EXO1, TTK, TPX2, ZWINT, and UHRF1 were established by extracting the central genes in the key modules, and the upregulation of these genes was accompanied by an increased prognostic risk of patients. Among them, high expression of LDHA, TRIP13, and TTK in LUAD was associated with shorter overall survival and could be used as independent prognostic factors to participate in metabolic processes such as tumor NAD. The present study provides a powerful molecular target for the study of LUAD prognosis and provides a theoretical basis for the diagnosis and treatment of LUAD and the development of targeted inhibitors.

MMR gene patterns evaluation provides novel insights for personalized immunotherapy compared to neoadjuvant chemotherapy in lung adenocarcinama

BackgroundThe association involving mismatch repair (MMR) genes, molecular subtype and specific immune cell group in tumor microenvironment has been focused by more recent studies. Its prognosis value in lung adenocarcinoma (LUAD) neoadjuvant chemotherapy remains elusive.MethodsThe correlation between the MMR gene patterns and the immune landscape were comprehensively evaluated. The MMRScore was calculated using principal component analysis (PCA) after grouping using R/mclust package. The prognostic significance of the MMRScore was evaluated by Kaplan-merrier analysis. Then a cohort of 103 Chinese LUAD patients was collected for neoadjuvant chemotherapy prognosis evaluation and validation using MMRScore.ResultsFour MMRclusters (mc1, 2, 3, 4)-characterized by differences in extent of aneuploidy, expression of immunomodulatory (IM) genes, mRNA expression, lncRNA expression and prognosis were identified. We established MMRscore to quantify the MMR pattern of individual LUAD patients. As is shown in further analyses, the MMRscore was a potential independent prognostic factor of LUAD. Finally, the prognostic value of the MMRscore and its association with tumor immune microenvironment (TIME) of LUAD were verified in Chinese LUAD cohort.ConclusionsWe demonstrated the correlation between MMR gene pattern, the CNV and tumor immune landscape in LUAD. A MMRcluster mc2 with high MMRscore, high TMB and high CNV subtype was identified with poor prognosis and infiltrating immunocyte. The comprehensive evaluation of MMR patterns in individual LUAD patients enhances the understanding of TIME and gives a new insight toward improved immune treatment strategies for LUAD patients compared to neoadjuvant chemotherapy.