Clinical significance and inflammatory landscapes of a novel recurrence-associated immune signature in early-stage lung adenocarcinoma

The HIF-1α/PKM2 Feedback Loop in Relation to EGFR Mutational Status in Lung Adenocarcinoma

OBJECTIVE

Gene mutations in tumor cells can lead to several unique metabolic phenotypes, which are crucial for the proliferation of cancer cells. EGFR mutation (EGFR-mt) is the main oncogenic driving mutation in lung adenocarcinoma (LUAD). HIF-1 α and PKM2 are two key metabolic regulatory proteins that can form a feedback loop and promote cancer growth by promoting glycolysis. Here, the linkage between EGFR mutational status and HIF-1α/PKM2 feedback loop in LUAD were evaluated.

METHODS

Retrospective study were performed on LUAD patients (n = 89) undergoing first-time therapeutic surgical resection. EGFR mutation was analyzed by real-time PCR. Immunohistochemistry was used to measure the expressions of HIF-1α and PKM2.

RESULTS

We found that the protein expressions of HIF-1α and PKM2 were significantly higher in LUAD than normal lung tissues. In adenocarcinomas, the two protein expressions were both correlated with worse pTNM stage. Moreover, the correlation between the proteins of HIF-1α/PKM2 feedback loop and the EGFR mutational status were also analyzed. We found that EGFR-mt tumors showed higher HIF-1α and PKM2 proteins compared to tumors with EGFR wild-type. Meanwhile, HIF-1α expression was significantly correlated with higher pTNM stage, and PKM2 showed a similar trend, only in EGFR-mutated tumors. The expression of HIF-1α was positively correlated with PKM2 in LUAD, furthermore, this correlation was mainly in patients with EGFR-mt.

CONCLUSION

Different expression and clinical features of HIF-1α/PKM2 feedback loop was existed between LUAD and normal lung tissues, especially in EGFR mutational tumors, supporting the relationship between EGFR mutation and the key related proteins of aerobic glycolysis (HIF-1α and PKM2) in lung adenocarcinomas.

A novel CAF-cancer cell crosstalk-related gene prognostic index based on machine learning: prognostic significance and prediction of therapeutic response in head and neck squamous cell carcinoma

BACKGROUND

Cancer-associated fibroblast (CAF)-cancer cell crosstalk (CCCT) plays an important role in tumor microenvironment shaping and immunotherapy response. Current prognostic indexes are insufficient to accurately assess immunotherapy response in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to develop a CCCT-related gene prognostic index (CCRGPI) for assessing the prognosis and response to immune checkpoint inhibitor (ICI) therapy of HNSCC patients.

METHODS

Two cellular models, the fibroblast-cancer cell indirect coculture (FCICC) model, and the fibroblast-cancer cell organoid (FC-organoid) model, were constructed to visualize the crosstalk between fibroblasts and cancer cells. Based on a HNSCC scRNA-seq dataset, the R package CellChat was used to perform cell communication analysis to identify gene pairs involved in CCCT. Least absolute shrinkage and selection operator (LASSO) regression was then applied to further refine the selection of these gene pairs. The selected gene pairs were subsequently subjected to stepwise regression to develop CCRGPI. We further performed a comprehensive analysis to determine the molecular and immune characteristics, and prognosis associated with ICI therapy in different CCRGPI subgroups. Finally, the connectivity map (CMap) analysis and molecular docking were used to screen potential therapeutic drugs.

RESULTS

FCICC and FC-organoid models showed that cancer cells promoted the activation of fibroblasts into CAFs, that CAFs enhanced the invasion of cancer cells, and that CCCT was somewhat heterogeneous. The CCRGPI was developed based on 4 gene pairs: IGF1-IGF1R, LGALS9-CD44, SEMA5A-PLXNA1, and TNXB-SDC1. Furthermore, a high CCRGPI score was identified as an adverse prognostic factor for overall survival (OS). Additionally, a high CCRGPI was positively correlated with the activation of the P53 pathway, a high TP53 mutation rate, and decreased benefit from ICI therapy but was inversely associated with the abundance of various immune cells, such as CD4+ T cells, CD8+ T cells, and B cells. Moreover, Ganetespib was identified as a potential drug for HNSCC combination therapy.

CONCLUSIONS

The CCRGPI is reliable for predicting the prognosis and immunotherapy response of HSNCC patients and may be useful for guiding the individualized treatment of HNSCC patients.

The predictive value of E2F7 in immunotherapy efficacy for lung adenocarcinoma: An observational study

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. In recent years, immunotherapy has greatly changed the treatment pattern of advanced LUAD. However, only a small proportion of LUAD patients benefitted from immune checkpoint inhibitor therapy. There is an urgent need to develop a biomarker to predict immune therapy response. E2F7 has been shown to be closely related to immune cell infiltration and immune checkpoint expression in tumors. However, it is unclear whether the E2F7 expression is related to the immunotherapy efficacy in LUAD. Therefore, we conducted this study to investigate the clinical characteristics, function, and immunotherapy responsiveness of E2F7 expression, and to explore the potential of E2F7 as an immunotherapy response biomarker in LUAD. We analyzed the clinical characteristics and biological function of E2F7 expression based on data from the Cancer Genome Atlas and Gene Expression Omnibus database. In addition, we used single-cell sequencing data to analyze the immune regulatory effects of E2F7 in LUAD. Furthermore, we analyzed the immunotherapy response prediction ability of E2F7 expression based on the immunotherapy database. Compared to normal lung tissue, E2F7 was specifically overexpressed in LUAD, and its expression was associated with higher malignancy and poor efficacy. E2F7 high expression was an independent risk factor affecting the prognosis of LUAD. E2F7 was enriched in cell division and cell cycle functions. In addition, the expressions of immune checkpoints were correlated with the E2F7 expression. E2F7 was highly expressed in myeloid cells, and E2F7 highly expressed myeloid cells were associated with immune and inflammatory responses. Moreover, the expression level of E2F7 can effectively distinguish different immune therapy responses in LUAD patients. E2F7 was upregulated in LUAD, and high expression of E2F7 was associated with higher malignancy and poor efficacy. E2F7 high expression was an independent risk factor affecting the prognosis of LUAD. Moreover, E2F7 may exert its immunosuppressive effect by affecting the function of myeloid cells. These results indicated the potential role of E2F7 as a biomarker for predicting LUAD immunotherapy responses.

Pyroptosis-Derived Long Noncoding RNA Profiles Reveal a Novel Signature for Evaluating the Prognosis of Patients With Lung Adenocarcinoma

PURPOSE

Long noncoding RNAs (lncRNAs) were recently implicated in modifying pyroptosis. Nonetheless, pyroptosis-related lncRNAs and their possible clinical relevance persist largely uninvestigated in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

A sum of 921 samples were collected from three independent data sets. We obtained pyroptosis-related genes from both the Molecular Signatures Database and relevant literature sources and used four machine learning techniques, comprising stepwise Cox, ridge regression, least absolute shrinkage and selection operator, and random forest. Multiple bioinformatics approaches were used to further investigate the underlying mechanisms.

RESULTS

In total, 39 differentially expressed pyroptosis genes were identified by comparing normal and tumor samples. Correlation analysis revealed 933 pyroptosis-related lncRNAs. Furthermore, univariate Cox regression determined 11 lncRNAs that exhibited stable associations with prognosis in the three cohorts, which were used to construct the pyroptosis-derived lncRNA signature. After analyzing the optimal results from four machine learning algorithms, we ultimately selected random forest to develop the pyroptosis-derived lncRNA signature. This signature was proven to be an independent prognostic factor and exhibited robust performance in three cohorts.

CONCLUSION

We provided novel insight and established a pyroptosis-derived lncRNA signature for patients with LUAD, exhibiting strong predictive capabilities in both the training and validation sets.

Molecular and immunological features of TREM1 and its emergence as a prognostic indicator in glioma

Triggering receptor expressed on myeloid cells 1 (TREM1), which belongs to the Ig-like superfamily expressed on myeloid cells, is reportedly involved in various diseases but has rarely been studied in glioma. In this study, the prognostic value and functional roles of TREM2 in glioma were analyzed. TERM1 was observed to be significantly upregulated in GBM compared to in other grade gliomas and was associated with poor prognosis. Increased TREM1 accompanied distinct mutation and amplification of driver oncogenes. Moreover, gene ontology and KEGG analyses showed that TREM1 might play a role in immunologic biological processes in glioma. TREM1 was also found to be tightly correlated with immune checkpoint molecules. xCell research revealed a link between TREM1 expression and multiple immune cell types, especially monocytes and macrophages. Single-cell analysis and immunofluorescence results showed that macrophages expressed TREM1. In vitro, inhibition of TREM1 signaling could result in a decrease in tumor-promoting effects of monocytes/TAMs. In summary, TREM1 may be a potential independent prognostic factor and immune target, which might provide new avenues to improve the efficacy of immunotherapy in glioma patients.

CDK16 as a potential prognostic biomarker correlated with an immunosuppressive tumor microenvironment and benefits in enhancing the effectiveness of immunotherapy in human cancers

BACKGROUND

Cyclin-Dependent Kinase 16 (CDK16) plays significant biological roles in various diseases. Nonetheless, its function in different cancer types and its relationship with the Tumor Immune Microenvironment (TIME) are still not well-understood.

METHODS

We analyzed the expression profile, genetic alterations, clinical features, and prognostic value of CDK16 in pan-cancer using data from The Cancer Genome Atlas, Genotype-Tissue Expression databases, and in vitro experiments. Additionally, the TIMER2 and ImmuCellAI databases were utilized to assess the correlation between CDK16 expression and immune cell infiltration levels. Finally, we examined the correlation between CDK16 and the response to immunotherapy using collected immunotherapy data.

RESULTS

CDK16 is notably overexpressed in pan-cancer and is a risk factor for poor prognosis in various cancers. Our findings reveal that CDK16 regulates not only cell cycle-related functions to promote cell proliferation but also the autoimmunity-related functions of the innate and adaptive immune systems, along with other immune-related signaling pathways. Moreover, CDK16 overexpression contributes to an immunosuppressive tumor microenvironment, extensively suppressing immune-related features such as the expression of immune-related genes and pathways, as well as the count of immune-infiltrating cells. Our analysis indicated that individuals with low CDK16 expression showed higher response rates to immune checkpoint inhibitors and longer overall survival compared to those with high CDK16 expression.

CONCLUSIONS

This study establishes CDK16 as a potential biomarker for predicting poor prognosis in a wide range of cancers. Its role in shaping the immunosuppressive tumor microenvironment and influencing the efficacy of immunotherapy highlights the urgent need for developing targeted therapies against CDK16, offering new avenues for cancer treatment and management.

Transcriptomic landscape based on annotated clinical features reveals PLPP2 involvement in lipid raft-mediated proliferation signature of early-stage lung adenocarcinoma

BACKGROUND

Image-based screening improves the detection of early-stage lung adenocarcinoma (LUAD)but also highlights the issue of high false-positive diagnoses, which puts patients at a risk of unnecessary over-treatment. Therefore, more precise discrimination criteria are required to ensure that patients with early-stage LUAD receive appropriate treatments.

METHODS

We integrated 158 early-stage LUAD cases from 2 independent cohorts, including 30 matched resected specimens with complete radiological and pathological information, and 128 retrospective pathological pair-samples with partial follow-up data. This integration allowed us to conduct a correlation analysis between clinical phenotype and transcriptome landscape. Immunohistochemistry was performed using tissue microarrays to examine the expression of phospholipid phosphatase 2 (PLPP2) and lipid-raft markers. Lipidomics analysis was used to determine the changes of lipid components in PLPP2-overexpressed cells. To assess the effects of PLPP2 on the malignant phenotypes of LUAD cells, we conducted mice tumor-bearing experiments and in vitro cellular experiments by knocking down PLPP2 and inhibiting lipid raft synthesis with MβCD, respectively.

RESULTS

Bioinformatics analysis indicated that the co-occurrence of lipid raft formation and rapid cell proliferation might exhibit synergistic effects in driving oncogenesis from lung preneoplasia to adenocarcinoma. The enhanced activation of the cell cycle promoted the transition from non-invasive to invasive status in early-stage LUAD, which was related to an increase in lipid rafts within LUAD cells. PLPP2 participated in lipid raft formation by altering the component contents of lipid rafts, such as esters, sphingomyelin, and sphingosine. Furthermore, elevated PLPP2 levels were identified as an independent prognostic risk factor for LUAD patients. Further results from in vivo and in vitro experiments confirmed that PLPP2 could induce excessive cell proliferation by enhancing lipid raft formation in LUAD cells.

CONCLUSIONS

Our study has revealed the characteristics of gene expression profiles in early-stage LUAD patients with the different radiological and pathological subtypes, as well as deciphered transcriptomic evolution trajectory from preneoplasia to invasive LUAD. Furthermore, it suggests that PLPP2-mediated lipid raft synthesis may be a significant biological event in the initiation of early-stage LUAD, offering a potential target for more precise diagnosis and therapy in clinical settings.

Signal sequence receptor subunit 3: A novel indicator of immunosuppressive tumor microenvironment and clinical benefits from immunotherapy

BACKGROUND

Signal sequence receptor subunit 3 (SSR3), a translocation-associated protein complex, plays a vital role in various diseases. However, its involvement in human cancers remains unclear.

METHODS

We conducted a comprehensive analysis by integrating data from multiple sources, including the Cancer Genome Atlas, Cancer Cell Lineage Encyclopedia, Genotype Tissue Expression, Human Protein Atlas, cBioPortal, TIMER, and ImmuCellAI. Additionally, we incorporated data from a clinical trial, two immunotherapy cohorts, and in vitro experiments to investigate SSR3's impact on cancer prognosis and immune response.

RESULTS

Our findings revealed a significant correlation between elevated SSR3 expression and unfavorable prognosis across various cancer types. Amplification is the most common genetic alteration in SSR3. Furthermore, functional enrichment analysis highlighted SSR3's regulatory role in promoting proliferation. In addition, SSR3 also serves as a pivotal mediator bridging the innate and adaptive immune systems and several related signaling pathways. Moreover, the correlation of SSR3 expression with tumor mutation burden in five cancer types, as well as with microsatellite instability in nine cancer types, suggests the potential of SSR3 as a predictive marker for immunotherapy response. To validate this hypothesis, we examined data from patients who underwent immunotherapy treatment. Our analysis revealed that individuals with low SSR3 expression demonstrated higher response rates to immune checkpoint inhibitors and longer overall survival compared to those with high SSR3 expression.

CONCLUSIONS

Our study identifies SSR3 as a potential oncogene in humans, implicated in both tumorigenesis and cancer immunity. Elevated SSR3 expression is indicative of an immunosuppressive tumor microenvironment. Therefore, SSR3 holds promise as a potential prognostic biomarker and a target for immunotherapy in cancer treatment.

Circular RNA P4HB promotes glycolysis and tumor progression by binding with PKM2 in lung adenocarcinoma

BACKGROUND

Emerging evidence indicates that circular RNAs (circRNAs) play vital roles in tumor progression, including lung adenocarcinomas (LUAD). However, the mechanisms by which circRNAs promote the progression of LUAD still require further investigation.

METHODS

Quantitative real-time PCR was performed to detect the expression of circP4HB in LUAD tissues and cells. Then, Kaplan-Meier analysis was used to determine the prognostic value of circP4HB expression. We employed RNA pull-down, RNA immunoprecipitation, mass spectrometry, cells fraction, glucose consumption, lactate production, pyruvate kinase M2 (PKM2) activity, and macrophage polarization assays to uncover the underlying mechanisms of circP4HB in LUAD.

RESULTS

We found that circP4HB is upregulated in LUAD tissues and correlated with advanced TNM stages and lymph node metastasis. LUAD patients with high circP4HB expression had poor prognoses. Functionally, circP4HB promoted LUAD progression in vivo and in vitro. Upregulated circP4HB increased glucose consumption, lactate production and accelerated aerobic glycolysis in LUAD cells. Mechanically, circP4HB mainly accumulated in the cytoplasm of LUAD cells and bound with PKM2 and subsequently upregulating PKM2 enzymatic activity by increasing its tetramer formation. Additionally, circP4HB promoted M2 macrophage phenotype shift via targeting PKM2. Finally, rescue assays further confirmed that circP4HB could promote LUAD cell progression through its interaction with PKM2.

CONCLUSION

These results demonstrate that circP4HB could promote LUAD progression, indicating circP4HB might be a potential therapeutic target of LUAD.

HGH1 and the immune landscape: a novel prognostic marker for immune-desert tumor microenvironment identification and immunotherapy outcome prediction in human cancers

HGH1 homolog, a protein-coding gene, plays a crucial role in human growth and development. However, its role in human cancer remains unclear. For the first time, this study comprehensively evaluated the potential involvement of HGH1 in cancer prognosis and immunological function. To achieve this, data from various databases, including The Cancer Genome Atlas, Genotype Tissue Expression, Cancer Cell Lineage Encyclopedia, Human Protein Atlas, cBioPortal, Tumor Immune Estimation Resource and Immune Cell Abundance Identifier, were collated, as well as from one large clinical study, three immunotherapy cohorts and in vitro experiments. This study aims to elucidate the role of HGH1 expression in cancer prognosis and immune response. Our findings revealed a significant association between increased HGH1 expression and a worse prognosis across various cancer types. Predominantly, copy number variations were identified as the most common genetic mutations. Additionally, HGH1 was observed to not only regulate cell cycle-related functions to promote cell proliferation but also influence autoimmunity-related functions within both the innate and adaptive immune systems, along with other relevant immune-related signaling pathways. Gene set enrichment analysis and gene set variation analysis were used to substantiate these findings. HGH1 overexpression contributed to an immune-deficient (immune-desert) tumor microenvironment, which was characterized by a significant expression of immune-related features such as immune-related gene and pathway expression and the number of immune-infiltrating cells. Furthermore, the correlation between HGH1 expression and tumor mutational burden in four cancers and microsatellite instability in eight cancers was observed. This suggests that HGH1 has potential as an immunotherapeutic target. Immunotherapy data analysis supports this notion, demonstrating that patients with low HGH1 expression treated with immune checkpoint inhibitors exhibit improved survival rates and a higher likelihood of responding to immunotherapy than patients with high HGH1 expression. Collectively, these findings highlight the significant role of HGH1 in human cancers, illuminating its involvement in tumorigenesis and cancer immunity. Elevated HGH1 expression was identified to be indicative of an immune-desert tumor microenvironment. Consequently, the targeting of HGH1, particularly in combination with immune checkpoint inhibitor therapy, holds promise for enhancing therapeutic outcomes in patients with cancer.

Metastasis-related long non-coding RNAs AL359220.1, SH3BP5-AS1 and ZF-AS1 are significant for prognostic assessment of lung adenocarcinoma

BACKGROUND

Metastasis of lung adenocarcinoma (LUAD) severely worsens prognosis. Genetic alteration in the tumor microenvironment (TME) is closely associated with metastasis and other malignant biological properties of LUAD. In this study, we establish a metastasis-related risk model to accurately predict LUAD prognosis.

METHODS

RNA-sequencing profiles and clinical data of LUAD patients including 503 tumor tissues and 54 adjacent normal tissues were collected in TCGA database. Additionally, the paired specimens from 156 LUAD patients were obtained in a single center. The metastatic relevance and clinical significance of metastasis-related long non-coding RNA (MRLNRs) was validated by series of in vitro experiments including western blotting, qPCR and transwell assays.

RESULTS

Six MRLNRs were significantly correlated to prognoses of LUAD patients, of which AL359220.1, SH3BP5-AS1 and ZF-AS1 were further used to establish a metastasis-related risk scoring model (MRRS) due to the close associations with overall survival of LUAD patients. According to the MRRS, patients with higher scores in the high-risk group obtained poorer prognoses and survival outcomes. ZFAS1 expressed highly in tumor tissues and showed the inverse results compared to SH3BP5-AS1 and AL359220.1. In addition, the high expression of ZFAS1 was prominently correlated to the more advanced T-stage and distant metastasis. The reduction of ZFAS1 induced by siRNAs dramatically diminished the migration and invasion abilities of LUAD cells.

CONCLUSIONS

In the present research, we elucidate the metastatic relevance and clinical significance of AL359220.1, SH3BP5-AS1 and ZF-AS1 in LUAD. Moreover, MRRS provide a promising assessing model for clinical decision making and prognosis of LUAD.

The prognostic value and immune landscaps of m6A/m5C-related lncRNAs signature in the low grade glioma

BACKGROUND

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) are the main RNA methylation modifications involved in the oncogenesis of cancer. However, it remains obscure whether m6A/m5C-related long non-coding RNAs (lncRNAs) affect the development and progression of low grade gliomas (LGG).

METHODS

We summarized 926 LGG tumor samples with RNA-seq data and clinical information from The Cancer Genome Atlas and Chinese Glioma Genome Atlas. 105 normal brain samples with RNA-seq data from the Genotype Tissue Expression project were collected for control. We obtained a molecular classification cluster from the expression pattern of sreened lncRNAs. The least absolute shrinkage and selection operator Cox regression was employed to construct a m6A/m5C-related lncRNAs prognostic signature of LGG. In vitro experiments were employed to validate the biological functions of lncRNAs in our risk model.

RESULTS

The expression pattern of 14 sreened highly correlated lncRNAs could cluster samples into two groups, in which various clinicopathological features and the tumor immune microenvironment were significantly distinct. The survival time of cluster 1 was significantly reduced compared with cluster 2. This prognostic signature is based on 8 m6A/m5C-related lncRNAs (GDNF-AS1, HOXA-AS3, LINC00346, LINC00664, LINC00665, MIR155HG, NEAT1, RHPN1-AS1). Patients in the high-risk group harbored shorter survival times. Immunity microenvironment analysis showed B cells, CD4 + T cells, macrophages, and myeloid-derived DC cells were significantly increased in the high-risk group. Patients in high-risk group had the worse overall survival time regardless of followed TMZ therapy or radiotherapy. All observed results from the TCGA-LGG cohort could be validated in CGGA cohort. Afterwards, LINC00664 was found to promote cell viability, invasion and migration ability of glioma cells in vitro.

CONCLUSION

Our study elucidated a prognostic prediction model of LGG by 8 m6A/m5C methylated lncRNAs and a critical lncRNA regulation function involved in LGG progression. High-risk patients have shorter survival times and a pro-tumor immune microenvironment.

The circadian rhythm key gene ARNTL2: a novel prognostic biomarker for immunosuppressive tumor microenvironment identification and immunotherapy outcome prediction in human cancers

Background

Aryl hydrocarbon receptor nuclear translocator-like 2 (ARNTL2) belongs to the b HLH- PAS domain transcription factor family and is one of the key clock genes that control the circadian rhythm. ARNTL2 plays an important role in human biological functions. However, its role in various tumors, especially in the tumor immune microenvironment (TIME) and immunotherapy, remains unclear.

Methods

We integrated data from cancer patients from multiple databases, including the Cancer Genome Atlas, Cancer Cell Lineage Encyclopedia, Genotype Tissue Expression, Human Protein Atlas, cBioPortal, TIMER, and ImmuCellAI, with data from a large clinical study, three immunotherapy cohorts, and in vitro experiments to investigate the involvement of ARNTL2 expression in cancer prognosis and immune response.

Results

ARNTL2 displayed abnormal expression within most malignant tumors, and is significantly associated with poorer survival and pathologic staging. Through gene-set enrichment analysis (GSEA) and gene-set variation analysis (GSVA), we found that ARNTL2 not only regulates cell cycle-related functions to promote cell proliferation but also regulates autoimmunity-related functions of the innate and adaptive immune systems, and other immune-related signaling pathways. In addition, ARNTL2 overexpression contributes to an immunosuppressive tumor microenvironment that plays a key role in immunosuppression-related features, such as the expression of immunosuppression-related genes and pathways and the number of immunosuppressive-infiltrating cells, including regulatory T cells (Tregs), tumor-associated macrophages (TAMs), and cancer-associated fibroblasts (CAFs). The group of patients with low ARNTL2 expression who received immune checkpoint inhibitors (ICI) therapy had better response rates and longer survival when compared to those with high ARNTL2 expression.

Conclusion

The findings of this study suggest that ARNTL2 is a potential human oncogene that plays an important role in tumorigenesis and cancer immunity. Elevated ARNTL2 expression indicates an immunosuppressive tumor microenvironment. Targeting ARNTL2 in combination with ICI therapy could bring more significant therapeutic benefits to patients with cancer. Our study sheds light on the remarkable potential of ARNTL2 in tumor immunity and provides a novel perspective for anti-tumor strategies.

Integrated Analysis of N1-Methyladenosine Methylation Regulators-Related lncRNAs in Hepatocellular Carcinoma

N1-methyladenosine (m1A) and long non-coding RNAs (lncRNAs) play significant roles in tumor progression in hepatocellular carcinoma (HCC). However, their association with HCC is still unclear. In this study, lncRNAs related to m1A were extracted from the mRNA expression matrix in The Cancer Genome Atlas (TCGA) database. Five m1A-related lncRNAs (AL031985.3, NRAV, WAC-AS1, AC026412.3, and AC099850.4) were identified based on lasso Cox regression and they generated a prognostic signature of HCC. The prognostic signature was identified as an independent prognosis factor in HCC patients. Moreover, the prognostic signature achieved better performance than TP53 mutation status or tumor mutational burden (TMB) scores in the stratification of patient survival. The immune landscape indicated that most immune checkpoint genes and immune cells were distributed differently between both risk groups. A higher IC50 of chemotherapeutics (sorafenib, nilotinib, sunitinib, and gefitinib) was observed in the high-risk group, and a lower IC50 of gemcitabine in the low-risk group, suggesting the potential of the prognostic signature in chemosensitivity. In addition, fifty-five potential small molecular drugs were found based on drug sensitivity and NRAV expression. Together, five m1A-related lncRNAs generated a prognostic signature that could be a promising prognostic prediction approach and therapeutic response assessment tool for HCC patients.

Clinical Significance and Tumor Microenvironment Characterization of a Novel Immune-Related Gene Signature in Bladder Cancer

Cancer immunotherapy plays a crucial role in bladder cancer (BC) progression. Increasing evidence has elucidated the clinicopathologic significance of the tumor microenvironment (TME) in predicting outcomes and therapeutic efficacy. This study sought to establish a comprehensive analysis of the immune-gene signature combined with TME to assist in BC prognosis. We selected sixteen immune-related genes (IRGs) after a weighted gene co-expression network and survival analysis. Enrichment analysis revealed that these IRGs were actively involved in Mitophagy and Renin secretion pathways. After multivariable COX analysis, the IRGPI comprising NCAM1, CNTN1, PTGIS, ADRB3, and ANLN was established to predict the overall survival of BC, which was validated in both TCGA and GSE13507 cohorts. In addition, a TME gene signature was developed for molecular and prognosis subtyping with unsupervised clustering, followed by a panoramic landscape characterization of BC. In summary, the IRGPI model developed in our study provided a valuable tool with an improved prognosis for BC.

Identification of a novel gene signature of lung adenocarcinoma based on epidermal growth factor receptor-tyrosine kinase inhibitor resistance

INTRODUCTION

Tyrosine kinase inhibitors (TKIs) that target epidermal growth factor receptor (EGFR) mutations are commonly administered to EGFR-positive lung cancer patients. However, resistance to EGFR-TKIs (mostly gefitinib and erlotinib) is presently a significant problem. Limited studies have focused on an EGFR-TKI resistance-related gene signature (ERS) in lung adenocarcinoma (LUAD).

METHODS

Gefitinib and erlotinib resistance-related genes were obtained through the differential analyses of three Gene Expression Omnibus datasets. These genes were investigated further in LUAD patients from The Cancer Genome Atlas (TCGA). Patients in the TCGA-LUAD cohort were split into two groups: one for training and one for testing. The training cohort was used to build the ERS, and the testing cohort was used to test it. GO and KEGG analyses were explored for the enriched pathways between the high-risk and low-risk groups. Various software, mainly CIBERSORT and ssGSEA, were used for immune infiltration profiles. Somatic mutation and drug sensitivity analyses were also explored.

RESULTS

An ERS based on five genes (FGD3, PCDH7, DEPDC1B, SATB2, and S100P) was constructed and validated using the TCGA-LUAD cohort, resulting in the significant stratification of LUAD patients into high-risk and low-risk groups. Multivariable Cox analyses confirmed that ERS had an independent prognostic value in LUAD. The pathway enrichment analyses showed that most of the genes that were different between the two risk groups were related to the immune system. Further immune infiltration results revealed that a lower immune infiltration score was observed in high-risk patients, and that various leukocytes were significantly related to the ERS. Importantly, samples from the high-risk group showed lower levels of PD-1, PD-L1, and CTLA-4, which are important biomarkers for immunotherapy responses. Patients in the high-risk group also had more gene mutation changes and were more sensitive to chemotherapy drugs like docetaxel and sorafenib. The ERS was also validated in the GSE30219, GSE11969 and GSE72094, and showed a favorable prognostic value for LUAD patients.

DISCUSSION

The ERS established during this study was able to predict a poor prognosis for LUAD patients and had great potential for predicting drug responses.

A Robust Prognostic Signature of Tumor Microenvironment in Colorectal Cancer

Background: Colorectal cancer (CRC) has been a major public health problem. Tumor microenvironment (TME) greatly contributes to the heterogeneity of CRC and is crucial for the regulation of CRC progression. The authors' study aimed to develop a robust prognostic signature for CRC patients based on TME-related genes. Materials and Methods: Gene expression data and clinicopathologic information of CRC patients were collected from Gene Expression Omnibus and The Cancer Genome Atlas databases. TME-related genes with prognostic value were identified by Cox regression and bootstrap method. The authors used the prognostic genes to construct a robust prognostic model using the least absolute shrinkage and selection operator (LASSO) regression method. The immune and stromal cell abundance of CRC samples were estimated by a microenvironment cell populations-counter method. Results: Based on a training set that comprised 893 CRC samples and 4775 TME-related genes, they established a prognostic model consisting of 25 TME-related genes. With specific risk score formulae, the prognostic model divided CRC patients into high-risk and low-risk subgroups with significantly different survival, which were further confirmed in validation cohorts consisting of other 473 CRC cases or subpopulation of specific stages. The result of time-dependent receiver operating characteristic analysis demonstrated strong predictive accuracy of the prognostic model both in training and validation cohorts. Multivariate Cox regression analysis showed that the 25-gene signature was an independent prognostic factor for overall survival, which was validated through clinical subgroups analysis. Further analysis revealed that CRC samples of high-risk group was abundant of stromal-relevant processes and had a significantly higher proportion of fibroblasts and endothelial cells infiltration. Conclusion: The authors established a robust prognostic signature of 25 TME-related genes which may be an effective tool for prognostic prediction and CRC patient stratification to assist in making treatment decisions.

Up-Frameshift Suppressor 3 as a prognostic biomarker and correlated with immune infiltrates: A pan-cancer analysis

BACKGROUND

The mRNA expression of protein Up-Frameshift Suppressor 3 Homolog B (UPF3B) differ in different tumors. However, the clinical relevance of UPF3B in cancer patients, such as with prognosis, tumor stage, and levels of tumor-infiltrating immune cells remain unclear.

METHODS

We performed bioinformatics analysis of UPF3B with The Cancer Genome Atlas (TCGA) database (https://xenabrowser.net) and TIMER2.0 (Tumor Immune Estimation Resource 2.0, http://timer.comp-genomics.org/). UPF3B expression in 33 cancers versus counterpart normal tissues was analyzed using TCGA pan-cancer data. The influence of UPF3B in long-term prognosis was evaluated using Kaplan-Meier method, and the associations between UPF3B transcription levels and immune-related gene expression, immune cell infiltration, tumor microenvironment (TME) score are analyzed by spearman correlation analysis. Enrichment analysis of UPF3B was conducted using the R package "clusterProfiler."

RESULTS

The transcriptional level of UPF3B was dysregulated in the human pan-cancer dataset. A significant correlation was found between the expression of UPF3B and the pathological stage of Esophageal Carcinoma (ESCA), Kidney Chromophobe (KIHC), Liver Hepatocellular Carcinoma (LIHC), and Skin Cutaneous Melanoma (SKCM). Multiple cancer types with high transcriptional levels of UPF3B were associated with a significantly worse prognosis. The functions of expressed UPF3B gene are primarily related to ubiquitin mediated proteolysis, cell cycle, and mRNA surveillance pathway. Our results also show that immune cells infiltration and immunosuppressive markers such as CTLA-4, PD-1 and PD-L1 significantly correlate with UPF3B expression.

CONCLUSIONS

In the present study, we synthetically explored the expression status and prognostic significance of UPF3B, and the relationship with clinic characters and immune microenvironment across cancers. Our results may provide novel insights for UPF3B as an immunotherapeutic target and valuable prognostic biomarker in various malignant tumor.

The small molecule chemical compound cinobufotalin attenuates resistance to DDP by inducing ENKUR expression to suppress MYH9-mediated c-Myc deubiquitination in lung adenocarcinoma

The small molecule chemical compound cinobufotalin (CB) is reported to be a potential antitumour drug that increases cisplatin (DDP) sensitivity in nasopharyngeal carcinoma. In this study, we first found that CB decreased DDP resistance, migration and invasion in lung adenocarcinoma (LUAD). Mechanistic studies showed that CB induced ENKUR expression by suppressing PI3K/AKT signalling to downregulate c-Jun, a negative transcription factor of ENKUR. Furthermore, ENKUR was shown to function as a tumour suppressor by binding to β-catenin to decrease c-Jun expression, thus suppressing MYH9 transcription. Interestingly, MYH9 is a binding protein of ENKUR. The Enkurin domain of ENKUR binds to MYH9, and the Myosin_tail of MYH9 binds to ENKUR. Downregulation of MYH9 reduced the recruitment of the deubiquitinase USP7, leading to increased c-Myc ubiquitination and degradation, decreased c-Myc nuclear translocation, and inactivation of epithelial-mesenchymal transition (EMT) signalling, thus attenuating DDP resistance. Our data demonstrated that CB is a promising antitumour drug and may be a candidate chemotherapeutic drug for LUAD patients.

Tumor Infiltrating Lymphocytes in Multi-National Cohorts of Ductal Carcinoma In Situ (DCIS) of Breast

Tumor-infiltrating lymphocytes (TILs) are prognostic in invasive breast cancer. However, their prognostic significance in ductal carcinoma in situ (DCIS) has been controversial. To investigate the prognostic role of TILs in DCIS outcome, we used different scoring methods for TILs in multi-national cohorts from Asian and European women. Self-described race was genetically confirmed using QC Infinium array combined with radmixture software. Stromal TILs, touching TILs, circumferential TILs, and hotspots were quantified on H&E-stained slides and correlated with the development of second breast cancer events (BCE) and other clinico-pathological variables. In univariate survival analysis, age older than 50 years, hormone receptor positivity and the presence of circumferential TILs were weakly associated with the absence of BCE at the 5-year follow-up in all cohorts (p < 0.03; p < 0.02; and p < 0.02, respectively, adjusted p = 0.11). In the multivariable analysis, circumferential TILs were an independent predictor of a better outcome (Wald test p = 0.01), whereas younger age was associated with BCE. Asian patients were younger with larger, higher grade, HR negative DCIS lesions, and higher TIL variables. The spatial arrangement of TILs may serve as a better prognostic indicator in DCIS cases than stromal TILs alone and may be added in guidelines for TILs evaluation in DCIS.

Clinical Value of Cytokine Assay in Diagnosis and Severity Assessment of Lung Cancer

Purpose

To investigate the clinical value of interleukin 2 (IL-2), interleukin 4 (IL-4), interleukin 6 (IL-6), interleukin 10 (IL-10), tumor necrosis factor α (TNF-α), and interferon-γ (IFN-γ) in diagnosis and severity assessment of lung cancer.

Methods

In this observational study, 50 physical examination healthy subjects were included in the control group and 100 lung cancer patients were included in the study group. In the study group, 53 cases with pleural effusion were subgrouped to the pleural effusion group (n = 53), while 47 patients were assigned to the nonpleural effusion group (n = 47). Plasma cytokines IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ, and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores of all eligible subjects were collected and compared.

Results

The study group showed significantly higher levels of plasma cytokines IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ versus healthy subjects (P < 0.05). Deterioration of lung cancer was associated with increased plasma cytokine levels and APACHE II scores. The combination assay of the above plasma cytokines showed significantly better diagnostic efficacy for lung cancer versus the single assay of the cytokines. Dead patients had higher plasma cytokine levels versus survived patients. The accuracy of plasma IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ levels in the severity assessment of lung cancer was comparable with that of the APACHE II scale.

Conclusion

The plasma cytokines IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ are effective markers for the diagnosis of lung cancer. The combined assay contributes to the early diagnosis of lung cancer patients, and the persistent elevation of cytokines suggests an increased risk of death in lung cancer patients, so the detection of cytokine levels facilitates the severity assessment of lung cancer.

AP3S1 is a Novel Prognostic Biomarker and Correlated With an Immunosuppressive Tumor Microenvironment in Pan-Cancer

Background: Adaptor-related protein complex 3, sigma one subunit (AP3S1) is one of the encoding subunits of the adaptor complex AP-3. However, its role in various tumor types and relationship with the tumor immune microenvironment (TIME) remains unclear.

Methods: AP3S1 expression was analyzed using datasets from The Cancer Genome Atlas, Genotype-Tissue Expression, UALCAN, and HPA databases. Then, we performed a systematic analysis of the genetic alterations, clinical features, and prognostic value of AP3S1 in pan-cancer. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to identify the signaling pathways associated with AP3S1. The correlation between immune cell infiltration and AP3S1 expression was analyzed using immune cell infiltration data from the ImmuCellAI, TIMER2, and a previous study. Finally, we analyzed the association of AP3S1 with tumor mutational burden (TMB), microsatellite instability (MSI), and immune-related genes.

Results: We found AP3S1 overexpression in most tumors and a significant association with low survival rates. GSEA and GSVA results show that AP3S1 is involved in tumor progression and associated with immune pathways in different tumor types. We also found that AP3S1 expression was positively correlated with the level of infiltration of immunosuppressive cells (tumor-associated macrophages, cancer-associated fibroblasts, Tregs) and negatively correlated with immune killer cells, including NK cells and CD8+ T cells, in pan-cancer. The expression of AP3S1 could affect TMB and MSI in various cancers. In addition, AP3S1 was positively correlated with most immunosuppressive genes, including PD-1, PD-L1, CTLA4, LAG3 and TIGIT in most cancer types.

Conclusion: Our study reveals that AP3S1 is a potential pan-cancer oncogene and plays an essential role in tumorigenesis and cancer immunity. Elevated expression of AP3S1 indicates an immunosuppressive microenvironment and can be used as a potential prognostic biomarker and a target for immunotherapy.

A Novel Secreted Protein-Related Gene Signature Predicts Overall Survival and Is Associated With Tumor Immunity in Patients With Lung Adenocarcinoma

Secreted proteins are important proteins in the human proteome, accounting for approximately one-tenth of the proteome. However, the prognostic value of secreted protein-related genes has not been comprehensively explored in lung adenocarcinoma (LUAD). In this study, we screened 379 differentially expressed secretory protein genes (DESPRGs) by analyzing the expression profile in patients with LUAD from The Cancer Genome Atlas database. Following univariate Cox regression and least absolute shrinkage and selection operator method regression analysis, 9 prognostic SPRGs were selected to develop secreted protein-related risk score (SPRrisk), including CLEC3B, C1QTNF6, TCN1, F2, FETUB, IGFBP1, ANGPTL4, IFNE, and CCL20. The prediction accuracy of the prognostic models was determined by Kaplan–Meier survival curve analysis and receiver operating characteristic curve analysis. Moreover, a nomogram with improved accuracy for predicting overall survival was established based on independent prognostic factors (SPRrisk and clinical stage). The DESPRGs were validated by quantitative real-time PCR and enzyme-linked immunosorbent assay by using our clinical samples and datasets. Our results demonstrated that SPRrisk can accurately predict the prognosis of patients with LUAD. Patients with a higher risk had lower immune, stromal, and ESTIMATE scores and higher tumor purity. A higher SPRrisk was also negatively associated with the abundance of CD8⁺ T cells and M1 macrophages. In addition, several genes of the human leukocyte antigen family and immune checkpoints were expressed in low levels in the high-SPRrisk group. Our results provided some insights into assessing individual prognosis and choosing personalized treatment modalities.

SEC61G participates in endoplasmic reticulum stress by interacting with CREB3 to promote the malignant progression of lung adenocarcinoma

As the most common type of lung cancer, lung adenocarcinoma (LUAD) poses a great threat to human health worldwide and severely compromises the quality of life of the patients. The present study aimed to explore the potential pathogenesis of LUAD. Reverse transcription-quantitative PCR and western blotting were applied to measure the expression levels of SEC61 translocon subunit γ (SEC61G) and cyclic AMP-responsive element-binding protein 3 (CREB3). Western blotting was also used to determine the expression of endoplasmic reticulum (ER) stress-, apoptosis- and migration-related proteins. Cell Counting Kit-8, colony formation, TUNEL, wound healing and Transwell assays were used, respectively, to determine the viability, proliferation, apoptosis, migration and invasion of LUAD A549 cells. The association between SEC61G and CREB3 was verified by co-immunoprecipitation assay. The results revealed that SEC61G was upregulated in A549 cells and its downregulation could activate ER stress. It was also found that silencing SEC61G inhibited the malignant development of LUAD through ER stress. In addition, SEC61G was verified to participate in ER stress in LUAD via CREB3 and silencing SEC61G exerted inhibitory effects on the malignant progression of LUAD by regulating CREB3. In summary, SEC61G participated in ER stress and its knockdown exerted inhibitory effects on A549 cells via regulating CREB3, which suggests that SEC61G may be a potential therapy for patients with LUAD.

An In Silico Analysis Reveals an EMT-Associated Gene Signature for Predicting Recurrence of Early-Stage Lung Adenocarcinoma

Background:

The potential micrometastasis tends to cause recurrence of lung adenocarcinoma (LUAD) after surgical resection and consequently leads to an increase in the mortality risk. Compelling evidence has suggested the underlying mechanisms of tumor metastasis could involve the activation of an epithelial-mesenchymal transition (EMT) program. Hence, the objective of this study was to develop an EMT-associated gene signature for predicting the recurrence of early-stage LUAD.

Methods:

The mRNA expression data of patients with early-stage LUAD were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) available databases. Gene Set Variation Analysis (GSVA) was first performed to provide an assessment of EMT phenotype, whereas Weighted Gene Co-expression Network Analysis (WGCNA) was constructed to determine EMT-associated key modules and genes. Based on the genes, a novel EMT-associated signature for predicting the recurrence of early-stage LUAD was identified using a least absolute shrinkage and selection operator (LASSO) algorithm and a stepwise Cox proportional hazards regression model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves and Cox regression analyses were used to estimate the performance of the identified gene signature.

Results:

GSVA revealed diverse EMT states in the early-stage LUAD. Further correlation analyses showed that the EMT states presented high correlations with several hallmarks of cancers, tumor purity, tumor microenvironment cells, and immune checkpoint genes. More importantly, Kaplan-Meier survival analyses indicated that patients with high EMT scores had worse recurrence-free survival (RFS) and overall survival (OS) than those with low EMT scores. A novel 5-gene signature ( AGL, ECM1, ENPP1, SNX7, and TSPAN12) was established based on the EMT-associated genes from WGCNA and this signature successfully predicted that the high-risk patients had a higher recurrence rate compared with the low-risk patients. In further analyses, the signature represented robust prognostic values in 2 independent validation cohorts (GEO and TCGA datasets) and a combined GEO cohort as evaluated by Kaplan-Meier survival ( P-value < .0001) and ROC analysis (AUC = 0.781). Moreover, the signature was corroborated to be independent of clinical factors by univariate and multivariate Cox regression analyses. Interestingly, the combination of the signature-based recurrence risk and tumor-node-metastasis (TNM) stage showed a superior predictive ability on the recurrence of patients with early-stage LUAD.

Conclusion:

Our study suggests that patients with early-stage LUAD exhibit diverse EMT states that play a vital role in tumor recurrence. The novel and promising EMT-associated 5-gene signature identified and validated in this study may be applied to predict the recurrence of early-stage LUAD, facilitating risk stratification, recurrence monitoring, and individualized management for the patients after surgical resection.

TXNDC9 knockdown inhibits lung adenocarcinoma progression by targeting YWHAG

Lung adenocarcinoma (LUAD) is the most common form of lung cancer and with the highest mortality rate. Therefore, the identification and development of effective methods for the treatment of LUAD is of great importance. The present study aimed to investigate the role of thioredoxin domain-containing protein 9 (TXNDC9) and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein γ (YWHAG; also known as 14-3-3γ) in the progression of LUAD. The expression of TXNDC9 and its association with the survival of patients with LUAD was analyzed using Encyclopedia of RNA Interactomes. Reverse transcription-quantitative PCR and western blot analysis were used to detect TXNDC9 mRNA and protein expression levels, respectively, in in vitro studies. To investigate the role of TXNDC9 in the progression of LUAD, TXNDC9 was silenced using small interfering RNA transfection. Furthermore, the viability, proliferation, migration, invasiveness and apoptosis of TXNDC9-silenced A549 cells were detected using Cell Counting Kit (CCK)-8, colony formation, wound healing, Transwell and TUNEL assays, respectively. The association between TXNDC9 and YWHAG was analyzed using STRING and Gene Expression Profiling Interactive Analysis databases, as well as co-immunoprecipitation assays. Subsequently, YWHAG was overexpressed to similarly determine effects of YWHAG on viability, proliferation, migration, invasiveness and apoptosis of A549 cells. TXNDC9 expression was markedly upregulated in lung cancer cells, particularly A549 cells, and silencing of TXNDC9 expression suppressed the viability of the lung cancer cells. The results also revealed that TXNDC9 silencing exerted inhibitory effects on the viability, proliferation, migration and invasiveness of A549 cells, whereas the apoptotic rate was increased. Similar to TXNDC9, YWHAG expression was also upregulated in the A549 cells. Furthermore, TXNDC9 was demonstrated to bind to YWHAG and was positively associated with YWHAG. YWHAG overexpression reversed the inhibitory effects of TXNDC9 silencing on LUAD, as evidenced by increased viability, proliferation, migration and invasiveness, and decreased apoptosis, of A549 cells. The present study demonstrated that the knockdown of TXNDC9 exerted suppressive effects on LUAD, whereas YWHAG overexpression reversed the inhibitory effects of TXNDC9 silencing on LUAD. Therefore, TXNDC9 silencing may exert protective effects against LUAD by targeting YWHAG.

Machine learning and bioinformatics analysis revealed classification and potential treatment strategy in stage 3-4 NSCLC patients

BACKGROUND

Precision medicine has increased the accuracy of cancer diagnosis and treatment, especially in the era of cancer immunotherapy. Despite recent advances in cancer immunotherapy, the overall survival rate of advanced NSCLC patients remains low. A better classification in advanced NSCLC is important for developing more effective treatments.

METHOD

The calculation of abundances of tumor-infiltrating immune cells (TIICs) was conducted using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), xCell (xCELL), Tumor IMmune Estimation Resource (TIMER), Estimate the Proportion of Immune and Cancer cells (EPIC), and Microenvironment Cell Populations-counter (MCP-counter). K-means clustering was used to classify patients, and four machine learning methods (SVM, Randomforest, Adaboost, Xgboost) were used to build the classifiers. Multi-omics datasets (including transcriptomics, DNA methylation, copy number alterations, miRNA profile) and ICI immunotherapy treatment cohorts were obtained from various databases. The drug sensitivity data were derived from PRISM and CTRP databases.

RESULTS

In this study, patients with stage 3-4 NSCLC were divided into three clusters according to the abundance of TIICs, and we established classifiers to distinguish these clusters based on different machine learning algorithms (including SVM, RF, Xgboost, and Adaboost). Patients in cluster-2 were found to have a survival advantage and might have a favorable response to immunotherapy. We then constructed an immune-related Poor Prognosis Signature which could successfully predict the advanced NSCLC patient survival, and through epigenetic analysis, we found 3 key molecules (HSPA8, CREB1, RAP1A) which might serve as potential therapeutic targets in cluster-1. In the end, after screening of drug sensitivity data derived from CTRP and PRISM databases, we identified several compounds which might serve as medication for different clusters.

CONCLUSIONS

Our study has not only depicted the landscape of different clusters of stage 3-4 NSCLC but presented a treatment strategy for patients with advanced NSCLC.

Identification and Validation of 7-lncRNA Signature of Epigenetic Disorders by Comprehensive Epigenetic Analysis

The survival rate of patients with lung adenocarcinoma (LUAD) is low. This study analyzed the correlation between the expression of long noncoding RNA (lncRNA) and epigenetic alterations along with the investigation of the prognostic value of these outcomes for LUAD. Differentially expressed lncRNAs were identified based on multiomic data and positively related genes using DESeq2 in R, differentially histone-modifying genes specific to LUAD based on histone modification data, gene enhancers from information collected from the FANTOM5 (Function Annotation Of The Mammalian Genome-5) (fantom.gsc.riken.jp/5) human enhancer database, gene promoters using the ChIPseeker and the human lincRNAs Transcripts database in R, and differentially methylated regions (DMRs) using Bumphunter in R. Overall survival was estimated by Kaplan-Meier, comparisons were performed among groups using log-rank tests to derive differences between sample subclasses, and epigenetic lncRNAs (epi-lncRNAs) potentially relevant to LUAD prognosis were identified. A total of seven dysregulated epi-lncRNAs in LUAD were identified by comparing histone modifications and alterations in histone methylation regions on lncRNA promoter and enhancer elements, including H3K4me2, H3K27me3, H3K4me1, H3K9me3, H4K20me1, H3K9ac, H3K79me2, H3K27ac, H3K4me3, and H3K36me3. Furthermore, 69 LUAD-specific dysregulated epi-lncRNAs were identified. Moreover, lncRNAs-based prognostic analysis of LUAD samples was performed and explored that seven of these lncRNAs, including A2M-AS1, AL161431.1, DDX11-AS1, FAM83A-AS1, MHENCR, MNX1-AS1, and NKILA (7-EpiLncRNA), showed the potential to serve as markers for LUAD prognosis. Additionally, patients having a high 7-EpiLncRNA score showed a generally more unfavorable prognosis compared with those which scored lower. Seven lncRNAs were identified as markers of prognosis in patients with LUAD. The outcomes of this research will help us understand epigenetically aberrant regulation of lncRNA expression in LUAD in a better way and have implications for research advances in the regulatory role of lncRNAs in LUAD.

Long non-coding RNA FAM83A antisense RNA 1 (lncRNA FAM83A-AS1) targets microRNA-141-3p to regulate lung adenocarcinoma cell proliferation, migration, invasion, and epithelial-mesenchymal transition progression

The current paper investigates how long non-coding RNA (lncRNA) FAM83A antisense RNA 1 (lncRNA FAM83A-AS1) affected the epithelial-mesenchymal transformation (EMT), growth, invasion and migration of lung adenocarcinoma (LUAD) via targeting miRNA-141-3p. The GEPIA and ENCORI databases were used to analyze differences in lncRNA FAM83A-AS1 levels within LUAD samples. FAM83A-AS1 and miR-141-3p levels were assessed using qRT-PCR among 30 LUAD samples and surrounding normal tissues. In addition, we analyzed how FAM83A-AS1 affected proliferation, invasion, migration, and EMT processes of LUAD cells by targeting miR-141-3p through EdU, CCK-8 assay, scratch assay, transwell migration and invasion assay, immunofluorescence (IF) staining and WB assay. MicroRNAs targeting FAM83A-AS1 were screened using AnnoLnc2 and identified by RT-qPCR. Dual-luciferase assays were utilized to evaluate the connection between FAM83A-AS1 and miR-141-3p. FAM83A-AS1 expression was remarkably raised in lung cancer cells and tissue samples; however, miR-141-3p level markedly reduced relative to healthy samples. FAM83A-AS1 silencing suppressed EMT, growth, invasion and migration of LUAD cells. MiR-141-3p was the possible FAM83A-AS1 binding target negatively associated with FAM83A-AS1. The miR-141-3p inhibitor partly abolished the FAM83A-AS1 knockdown-induced inhibition on EMT, cell growth, invasion and migration in LUAD cells. In addition, miR-141-3p down-regulation abolished the inhibition of E-box-bound zinc finger protein 1 and 2 protein production following FAM83A-AS1 knockdown. According to our results, FAM83A-AS1/miR-141-3p axis plays an important role in LUAD occurrence and development. FAM83A-AS1 sponged miR-141-3p to down-regulate the level of the latter within LUAD and thereby encouraging LUAD development and suggesting a possible novel therapeutic approach for LUAD.

Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis

Background

The early-stage lung adenocarcinoma (LUAD) rate has increased with heightened public awareness and lung cancer screening implementation. Lipid metabolism abnormalities are associated with lung cancer initiation and progression. However, the comprehensive features and clinical significance of the immunometabolism landscape and lipid metabolism-related genes (LMRGs) in cancer recurrence for early-stage LUAD remain obscure.

Methods

LMRGs were extracted from Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Samples from The Cancer Genome Atlas (TCGA) were used as training cohort, and samples from four Gene Expression Omnibus (GEO) datasets were used as validation cohorts. The LUAD recurrence-associated LMRG molecular pattern and signature was constructed through unsupervised consensus clustering, time-dependent receiver operating characteristic (ROC), and least absolute shrinkage and selection operator (LASSO) analyses. Kaplan-Meier, ROC, and multivariate Cox regression analyses and prognostic meta-analysis were used to test the suitability and stability of the signature. We used Gene Ontology (GO), KEGG pathway, immune cell infiltration, chemotherapy response analyses, gene set variation analysis (GSVA), and GSEA to explore molecular mechanisms and immune landscapes related to the signature and the potential of the signature to predict immunotherapy or chemotherapy response.

Results

First, two LMRG molecular patterns were established, which showed diverse prognoses and immune infiltration statuses. Then, a 12-gene signature was identified, and a risk model was built. The signature remained an independent prognostic parameter in multivariate Cox regression and prognostic meta-analysis. In addition, this signature stratified patients into high- and low-risk groups with significantly different recurrence rates and was well validated in different clinical subgroups and several independent validation cohorts. The results of GO and KEGG analyses and GSEA showed that there were differences in multiple lipid metabolism, immune response, and drug metabolism pathways between the high- and low-risk groups. Further analyses revealed that the signature-based risk model was related to distinct immune cell proportions, immune checkpoint parameters, and immunotherapy and chemotherapy response, consistent with the GO, KEGG, and GSEA results.

Conclusions

This is the first lipid metabolism-based signature for predicting recurrence, and it could provide vital guidance to achieve optimized antitumor for immunotherapy or chemotherapy for early-stage LUAD.

A Novel 16-Genes Signature Scoring System as Prognostic Model to Evaluate Survival Risk in Patients with Glioblastoma

Previous studies have found that gene expression levels are associated with prognosis and some genes can be used to predict the survival risk of glioblastoma (GBM) patients. However, most of them just built the survival-related gene signature, and personal survival risk can be evaluated only in group. This study aimed to find the prognostic survival related genes of GBM, and construct survival risk prediction model, which can be used to evaluate survival risk by individual. We collected gene expression data and clinical information from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Cox regression analysis and LASSO-cox regression analysis were performed to get survival-related genes and establish the overall survival prediction model. The ROC curve and Kaplan Meier analysis were used to evaluate the prediction ability of the model in training set and two independent cohorts. We also analyzed the biological functions of survival-related genes by GO and KEGG enrichment analysis. We identified 99 genes associated with overall survival and selected 16 genes (IGFBP2, GPRASP1, C1R, CHRM3, CLSTN2, NELL1, SEZ6L2, NMB, ICAM5, HPCAL4, SNAP91, PCSK1N, PGBD5, INA, UCHL1 and LHX6) to establish the survival risk prediction model. Multivariate Cox regression analysis indicted that the risk score could predict overall survival independent of age and gender. ROC analyses showed that our model was more robust than four existing signatures. The sixteen genes can also be potential transcriptional biomarkers and the model can assist doctors on clinical decision-making and personalized treatment of GBM patients.

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer

As the most aggressive tumor, the outcome of pancreatic cancer (PACA) has not improved observably over the last decade. Anatomy-based TNM staging does not exactly identify treatment-sensitive patients, and an ideal biomarker is urgently needed for precision medicine. Based on expression files of 1280 patients from 10 multicenter cohorts, we screened 32 consensus prognostic genes. Ten machine-learning algorithms were transformed into 76 combinations, of which we selected the optimal algorithm to construct an artificial intelligence-derived prognostic signature (AIDPS) according to the average C-index in the nine testing cohorts. The results of the training cohort, nine testing cohorts, Meta-Cohort, and three external validation cohorts (290 patients) consistently indicated that AIDPS could accurately predict the prognosis of PACA. After incorporating several vital clinicopathological features and 86 published signatures, AIDPS exhibited robust and dramatically superior predictive capability. Moreover, in other prevalent digestive system tumors, the nine-gene AIDPS could still accurately stratify the prognosis. Of note, our AIDPS had important clinical implications for PACA, and patients with low AIDPS owned a dismal prognosis, higher genomic alterations, and denser immune cell infiltrates as well as were more sensitive to immunotherapy. Meanwhile, the high AIDPS group possessed observably prolonged survival, and panobinostat may be a potential agent for patients with high AIDPS. Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment of PACA.

Exploration and Validation of a Novel Inflammatory Response-Associated Gene Signature to Predict Osteosarcoma Prognosis and Immune Infiltration

Background

Inflammatory response took part in the progression of tumor and was regarded as the hallmark of cancer. However, the prognostic relationship between osteosarcoma and inflammatory response-associated genes (IRGs) was unclear. This research aimed to explore the correlations between osteosarcoma prognosis and IRG signature.

Methods

The inflammatory response-associated differentially expressed messenger RNAs (DEmRNAs) were screened out through Gene Expression Omnibus (GEO) and Molecular Signature Database (MSigDB) databases. Univariate and multivariate cox regression analyses were utilized to construct the IRG signature. The prognostic value of signature was investigated through Kaplan–Meier (KM) survival curve and nomogram. DEmRNAs among high and low inflammatory response-associated risks were identified and functional enrichment analyses were conducted. ESTIMATE, CIBERSORT and single-sample gene set enrichment analyses (ssGSEA) were implied to reveal the alterations in immune infiltration. All the above results were validated in Target database. The expression of IRGs was also validated in different cell lines by quantitative real-time PCR (qRT-PCR) and osteosarcoma patient samples by immunohistochemistry.

Results

The IRG signature that consisted of two genes (MYC, CLEC5A) was established. In training and validation datasets, patients with lower risk scores survived longer and the IRG signature was confirmed as the independent prognostic factor in osteosarcoma. The nomogram was constructed and the calibration curves demonstrated the reliability of this model. Functional analysis of risk score-associated DEmRNAs indicated that immune-related pathways and functions were significantly enriched. ssGSEA revealed that 14 immune cells and 11 immune functions were significantly dysregulated. The qRT-PCR results indicated IRGs were significantly differently expressed in osteosarcoma and osteoblast cell lines. The immunohistochemistry analyses of patients’ samples revealed the same result.

Conclusion

The novel osteosarcoma inflammatory response-associated prognostic signature was established and validated in this study. This model could serve as the biomarker and therapeutic target for osteosarcoma in the future.

A circadian rhythm-related gene signature associated with tumor immunity, cisplatin efficacy, and prognosis in bladder cancer

Circadian dysregulation involves malignant tumor initiation and progression, but the understanding of circadian rhythm’s roles in bladder cancer (BCa) remains insufficient. The circadian rhythm-related genes were collected and clustered based on the Cancer Genome Atlas (TCGA), and the clustering was significantly associated with the prognosis and risk clinicopathological features. Through genomic difference analysis and gene pairing, a circadian rhythm-related signature was successfully established. Kaplan-Meier survival analysis and time-dependent receiver operating curves displayed that the prognosis model was a reliable prognosis biomarker both in the training cohort (n = 396, P = 2.687e-10) and external validation cohort (n = 224, P = 1.45e-02). The patients with high risk have high immune infiltration and high expression of immune checkpoint genes, which partly account for the poor prognosis. TIDE algorithm and the validation in IMvigor210 cohort indicated that the risk signature was a promising marker for the immunotherapeutic response. The risk model could also predict the therapeutic response of cisplatin, which was validated in the Genomics of Drug Sensitivity in Cancer database (P = 0.0049), TCGA (P = 0.038), and T24 BCa cells treated with cisplatin. The functional enrichment showed the risk model was significantly correlated with some malignant phenotypes, such as angiogenesis, epithelial-mesenchymal transition, and KRAS signaling pathway. Totally, we proposed a novel circadian rhythm-related signature for prognosis evaluation, which also helped to predict the immune infiltration and cisplatin sensitivity in BCa.

The Prognostic Value and Immune Landscapes of a m6A/m5C/m1A-Related LncRNAs Signature in Head and Neck Squamous Cell Carcinoma

Background: N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A) are the main RNA methylation modifications involved in the progression of cancer. However, it is still unclear whether m6A/m5C/m1A-related long non-coding RNAs (lncRNAs) affect the prognosis of head and neck squamous cell carcinoma (HNSCC). Methods: We summarized 52 m6A/m5C/m1A-related genes, downloaded 44 normal samples and 501 HNSCC tumor samples with RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) database, and then searched for m6A/m5C/m1A-related genes co-expressed lncRNAs. We adopt the least absolute shrinkage and selection operator (LASSO) Cox regression to obtain m6A/m5C/m1A-related lncRNAs to construct a prognostic signature of HNSCC. Results: This prognostic signature is based on six m6A/m5C/m1A-related lncRNAs (AL035587.1, AC009121.3, AF131215.5, FMR1-IT1, AC106820.5, PTOV1-AS2). It was found that the high-risk subgroup has worse overall survival (OS) than the low-risk subgroup. Moreover, the results showed that most immune checkpoint genes were significantly different between the two risk groups (p < 0.05). Immunity microenvironment analysis showed that the contents of NK cell resting, macrophages M2, and neutrophils in samples of low-risk group were significantly lower than those of high-risk group (p < 0.05), while the contents of B cells navie, plasma cells, and T cells regulatory (Tregs) were on the contrary (p < 0.05). In addition, patients with high tumor mutational burden (TMB) had the worse overall survival than those with low tumor mutational burden. Conclusion: Our study elucidated how m6A/m5C/m1A-related lncRNAs are related to the prognosis, immune microenvironment, and TMB of HNSCC. In the future, these m6A/m5C/m1A-related lncRNAs may become a new choice for immunotherapy of HNSCC.

Prognostic model of lung adenocarcinoma constructed by the CENPA complex genes is closely related to immune infiltration

BACKGROUND

Lung adenocarcinoma (LUAD) is still one of the primary malignant diseases leading to higher mortality worldwide. It has been previously reported that multiple genes in the CENPA-nucleosome associated complex (NAC) complex in lung cancer can be used as prognostic markers; however, there is lack of comprehensive research on the CENPA-NAC complex.

METHODS

The hub genes of lung cancer were obtained by analyzing multiple gene expression omnibus (GEO) lung cancer datasets. The key genes of the CENPA-NAC complex in the evolution of LUAD were identified according to lung cancer data obtained from The Cancer Genome Atlas (TCGA) database, and the key genes were constructed as a survival prognostic model. The relationship between the model and immune cell infiltration was studied by the Tumor Immune Estimation Resource (TIMER) and single-sample gene set enrichment analysis (ssGSEA) studies.Droplet Digital polymerase chain reaction (ddPCR) was used to verify the effectiveness of the prognostic model to predict survival using clinical samples.

RESULTS

A comprehensive study showed that CENPA, CENPH, CENPM, CENPN and CENPU were key genes in the development and evolution of LUAD. The constructed survival prognosis model was an independent risk factor for LUAD and can be used to assess the survival of LUAD patients. The risk score was closely related to the infiltration of multiple immune cells. The independent cohorts GSE31210 and GSE50081 further confirmed the validity of the prognostic model, and finally, the model was validated with clinical samples.

CONCLUSIONS

In conclusion, the results of the present study showed that CENPA, CENPH, CENPM, CENPN, and CENPU are a group of potential prognostic markers in LUAD. The constructed model has been confirmed to be applicable in the clinical setting in evaluating the survival of patients with LUAD, and providing more evidence on immunotherapy for LUAD.

Microdissection of the Bulk Transcriptome at Single-Cell Resolution Reveals Clinical Significance and Myeloid Cells Heterogeneity in Lung Adenocarcinoma

Background

Tumor infiltrating myeloid (TIM) cells constitute a vital element of the tumor microenvironment. The cell-type heterogeneity of TIM has yet to be fully investigated.

Methods

We used a time saving approach to generate a single-cell reference matrix, allowing quantification of cell-type proportions and cell-type-specific gene abundances in bulk RNA-seq data.

Results

Two distinct clusters, MSC1 and MSC2 (MSC subtype) were newly identified in lung adenocarcinoma (LUAD) patients, both significantly associated with overall survival and immune blockade therapy responses. Twenty myeloid cell types were detected. Thirteen of these had distinct enrichment patterns between MSC1 and MSC2. LAMP3+ dendritic cells, being a mature and transportable subtype of dendritic cell that may migrate to lymph nodes, were noted as associated with non-responsiveness to immunotargeted therapy. High infiltration level of IFIT3+ neutrophils was strongly related to the response to immune-targeted therapy and was seen to activate CD8+ T cells, partly through inflammasome activation. The infiltration levels of TIMP1+ macrophages and S100A8+ neutrophils were both significantly associated with poor prognosis. TIMP1+ macrophages were noted to recruit S100A8+ neutrophils via the CXCL5-CXCR2 axes and promote LUAD progression.

Conclusion

Altogether, we performed virtual microdissection of the bulk transcriptome at single-cell resolution and provided a promising TIM infiltration landscape that may shed new light on the development of immune therapy.

Identification of Tumor Microenvironment-Related Prognostic lncRNAs in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and is a severe threat to human health. Although many therapies have been applied to LUAD, the long-term survival rate of patients remains unsatisfactory. We aim to find reliable immune microenvironment-related lncRNA biomarkers to improve LUAD prognosis.

Methods

ESTIMATE analysis was performed to evaluate the degree of immune infiltration of each patient in TAGA LUAD cohort. Correlation analysis was used to identify the immune microenvironment-related lncRNAs. Univariate cox regression analysis, LASSO analysis, and Kaplan Meier analysis were used to construct and validate the prognostic model based on microenvironment-related lncRNAs.

Results

We obtained 1,178 immune microenvironment-related lncRNAs after correlation analysis. One hundred and eighty of them are independent prognostic lncRNAs. Sixteen key lncRNAs were selected by LASSO method. This lncRNA-based model successfully predicted patients’ prognosis in validation cohort, and the risk score was related to pathological stage. Besides, we also found that TP53 had the highest frequency mutation in LUAD, and the mutation of TP53 in the high-risk group, which was identified by our survival model, has a poor prognosis. lncRNA-mRNA co-expression network further suggested that these lncRNAs play a vital role in the prognosis of LUAD.

Conclusion

Here, we filtered 16 key lncRNAs, which could predict the survival of LUAD and may be potential biomarkers and therapeutic targets.

Immune Microenvironment Signatures as Biomarkers to Predict Early Recurrence of Stage Ia-b Lung Cancer

Introduction

Approximately 30% of patients diagnosed with stage Ia-b NSCLC die of recurrent disease after surgery. This study aimed to identify immune-related biomarkers that might predict tumor recurrence in stage Ia-b NSCLC within 40 months after curative resection.

Methods

Gene expression data of stage Ia-b NSCLC samples was retrieved from the TCGA database, the GEO databases, and the Second Xiangya hospital (XXEYY) database. 22 types of tumors infiltrating immune cells and the expression of immune-associated genes were investigated using CIBERSORT, immunohistochemical staining, and GSEA analyses in a total of 450 patients (80 in the training cohort and 370 in the validation cohorts). Recurrence-related immune features were selected based on the LASSO Cox regression model.

Results

High density of Tregs, Macrophages M0 and M1 cell could be observed in recurrence group while the memory B cell was more frequently enriched in controls, yet Tregs alone was significantly associated with tumor early recurrence in TCGA cohort, XYEYY cohort and GSE37745 dataset. A handful of immune-related genes were identified in the recurrence group. Based on Lasso regression analysis, the expressions of five immune-related genes, RLTPR, SLFN13, MIR4500HG, HYDIN and TPRG1 were closely correlated with tumor early recurrence. In the training cohort (TCGA), the combination of these five genes has sensitivity and specificity of 85% and 85%, with AUC of 0.91 (95% CI 0.84-0.98) for lung cancer early recurrence prediction, whereas in validation cohorts, the sensitivity and specificity using this panel was 61-89% and 54-82%, with AUC of 0.62-0.84.

Conclusion

Our study demonstrated that the immune microenvironment signatures were closely related to tumor early recurrence. Compared to tumor-infiltrating lymphocytes, the expression of five immune-related genes could be robust biomarkers to predict early recurrence of stage Ia-b NSCLC after curative resection.

Development of a Ferroptosis-Related lncRNA Signature to Predict the Prognosis and Immune Landscape of Bladder Cancer

The tight relationship between ferroptotic cell death and immune response demonstrated by recent studies enlightened us to detect the underlying roles of ferroptosis-related long noncoding RNAs (frlncRNAs) in the tumor microenvironment of bladder cancer (BCa). We collected 121 ferroptosis regulators from previous studies. Based on their expression values, 408 cases with BCa were clustered. The patients in different clusters showed diverse immune infiltration, immunotherapy response, and chemotherapy effectiveness, revalidating the tight correlation with ferroptosis and tumor immunity. Through differential, coexpression, Kaplan-Meier, Lasso, and Cox analysis, we developed a 22-lncRNA-pair signature to predict the prognosis of BCa based on gene-pair strategy, where there is no need for definite expression values. The areas under the curves are all over 0.8. The risk model also helped to predict immune infiltration, immunotherapeutic outcomes, and chemotherapy sensitivity. Totally, the prognostic assessment model indicated a promising predictive value, also providing clues for the interaction between ferroptosis and BCa immunity.

LncRNA SGMS1-AS1 regulates lung adenocarcinoma cell proliferation, migration, invasion, and EMT progression via miR-106a-5p/MYLI9 axis

BACKGROUND

Lung cancer mainly includes non-small cell lung cancer (NSCLC). Lung adenocarcinoma (LUAD) is the main subtype of NSCLC. Long non-coding RNAs (LncRNAs) had been found to exert numerous functions on the progressions of cancers. MicroRNAs often exist as the target of LncRNAs to regulate a series of signaling pathways in human. We explored the effects and molecular mechanism of LncRNA SGMS1-AS1 on the procedures of LUAD cells.

METHODS

The ENCORI and GEPIA databases were used to analyze the differences in SGMS1, miR-106a-5p, and MYLIP between LUAD and normal tissue. Their expression levels were examined by RT-PCR. CCK8, colony formation, migration, and invasion assay were conducted in LUAD cells which had silenced SGMS1-AS1. To verify the relationship between SGMS1-AS1, miR-106a-5p, and MYLIP, we overexpressed miR-106a-5p inhibitor or MYLIP in LUAD cells after decreasing SGMS1-AS1 and repeated the above assays.

RESULTS

SGMS1-AS1 was downregulated in LUAD tissue as well as cells, which was related to good prognosis of patients with lung adenocarcinoma. Additionally, knockdown of SGMS1-AS1 promoted proliferation, migration, invasion, and epithelial mesenchymal transition (EMT) progression of LUAD cells, which meant that SGMS1-AS1 inhibited the progression of LUAD cells. Furthermore, miR-106a-5p was the direct target of SGMS1-AS1 and transfecting miR-106a-5p inhibitor could reversed the impact induced by knockdown of SGMS1-AS1. Subsequently, we found that MYLIP was the target of miR-106a-5p, which was negatively correlated with miR-106a-5p, but had high positive correlation with SGMS1-AS1. Consistently, overexpression MYLIP partly eliminated the effects on A549 cells induced by silencing of SGMS1-AS1.

CONCLUSION

LncRNA SGMS1-AS1 inhibits the proliferation, invasion, migration and EMT progression of LUAD cells via targeting miR-106a-5p/MYLIP axis.

Development and external validation of a composite immune-clinical prognostic model associated with EGFR mutation in East-Asian patients with lung adenocarcinoma

BACKGROUND

EGFR mutation is a common oncogene driver in East Asians with lung adenocarcinoma (LUAD), conferring a favorable prognosis with effective targeted therapy. However, the EGFR mutation is a weak predictor of long-term survival. Therefore, a powerful predictive tool is urgently needed to estimate disease prognosis and patient survival for East-Asian patients with LUAD.

METHODS

In this first systematic analysis of the relationships among EGFR mutation, immunophenotype, and prognosis in LUAD samples from East-Asian patients, we constructed a prognostic signature consisting of EGFR-associated immune-related gene pairs (EIGPs). The predictive performance for overall survival (OS) and the clinical significance of this signature were then comprehensively investigated.

RESULTS

Based on transcriptome data analysis of a training set, we proposed the EIGP index (EIGPI), represented by five EIGPs, which was significantly associated with the OS of East-Asian patients with LUAD. It was also well validated in a test set. Furthermore, the prognostic performance of the EIGPI was further verified using protein levels in an additional independent set. Stratification analysis and multivariate Cox regression analysis revealed that the EIGPI was an independent prognostic factor. When combined with stage, the composite immune-clinical prognostic model index (ICPMI) showed improved prognostic accuracy in all datasets.

CONCLUSION

This study was the first to systematically investigate the relationships among EGFR mutation, immunophenotype, and prognosis in East Asians with LUAD and develop a composite clinical and immune model associated with EGFR mutation. This model may be a reliable and promising prognostic tool and help further personalize patient management.

Seven interferon gamma response genes serve as a prognostic risk signature that correlates with immune infiltration in lung adenocarcinoma

Interferon-gamma (IFN-γ) plays a complex role in modulating tumor microenvironment during lung adenocarcinoma (LUAD) development. In order to define the role of IFN-γ response genes in LUAD progression, we characterized the gene expression, mutation profile, protein-protein interaction of 24 IFN-γ response genes, which exhibited significant hazard ratio in overall survival. Two subgroups of LUAD from the TCGA cohort, which showed significant difference in the survival rate, were identified based on the expression of these genes. Furthermore, LASSO penalized cox regression model was used to derive a risk signature comprising seven IFN-γ response genes, including CD74, CSF2RB, PTPN6, MT2A, NMI, LATS2, and PFKP, which can serve as an independent prognostic predictor of LUAD. The risk signature was validated in an independent LUAD cohort. The high risk group is enriched with genes regulating cell cycle and DNA replication, as well as a high level of pro-tumor immune cells. In addition, the risk score is negatively correlated with the expression of immune metagenes, but positively correlated with DNA damage repair genes. Our findings reveal that seven-gene risk signature can be a valuable prognostic predictor for LUAD, and they are crucial participants in tumor microenvironment of LUAD.

Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.

Methods

All data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan–Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.

Results

Through univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.

Conclusion

The immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

Identification of small proline-rich protein 1B (SPRR1B) as a prognostically predictive biomarker for lung adenocarcinoma by integrative bioinformatic analysis

BACKGROUND

With the ongoing development of targeted therapy and immunotherapy in recent years, the overall five-year survival rate of NSCLC patients has not improved, and the search for novel diagnostic and prognostic markers for lung adenocarcinoma continues.

METHODS

Lung adenocarcinoma (LUAD) gene expression data and relevant clinical information were obtained from the TCGA. Hub genes were identified with weighted gene co-expression network analysis (WGCNA) and protein-protein interaction network (PPI). Survival analyses were also performed using GEPIA. The 536 LUAD patients were divided into two groups according to the SPRR1B expression level and analyzed by gene set enrichment analysis (GSEA) and verified by immunoblotting. The effects of SPRR1B on cell proliferation and cell metastasis and apoptosis were evaluated by 5-ethynyl-2'-deoxyuridine (EdU) staining, colony formation assay, transwell migration and invasion assay, and flow cytometry, respectively.

RESULTS

A total of 2269 DEGs were analyzed by WGCNA and five hub genes (CCK, FETUB, PCSK9, SPRR1B, and SPRR2D) were identified. Among them, SPRR1B was selected as one of the most significant prognostic genes in LUAD. SPRR1B was found to be highly expressed in lung adenocarcinoma cells compared with that in normal bronchial epithelial cells. In addition, silencing of SPRR1B could inhibit the cell proliferation, invasion, and migration of lung adenocarcinoma cells, but induced cell apoptosis and G2/M phase arrest in vitro. The result of GSEA and immunoblotting revealed that SPRR1B activated the MAPK signaling pathway involved in the proliferation and metastasis of lung cancer.

CONCLUSIONS

Our findings demonstrate that SPRR1B may function as a prognosis predictor in lung adenocarcinoma.

Identification of a Signature Comprising 5 Soluble Carrier Family Genes to Predict the Recurrence of Papillary Thyroid Carcinoma

RNA-sequencing data and relevant clinical data in The Cancer Genome Atlas for 502 samples of papillary thyroid cancer (PTC) were analyzed to determine the prognostic value of soluble carrier family genes in PTC. We analyzed soluble carrier family gene expression and function in the samples. Clustering identified 2 clusters in the data. Risk characteristics were identified using LASSO and Univariate Cox regression analysis, which divided the patients into low and high-risk groups. The expression levels of 88 soluble carrier genes were significantly different between tumors and normal tissue. The 2 PTC clusters had different clinical outcomes and distributions of gene expression. The expression levels of SFXN1, SLC12A4, SLC35A1, SLC35E1, and SLCO1C1 were markedly different between the 2 groups. The high risk and low risk groups had significant different prognoses (P < 0.05). Significant differences were identified for disease free survival (DFS), sex and T stage between the 2 subgroups. The risk score was identified as an independent prognostic variable (P < 0.05) and as a predictor of clinicopathological variables. In patients with PTC, solute carrier gene expression showed differential associations with clinicopathological variables. The 5 genes could be used as prognostic factors for PTC, particularly to predict PTC recurrence.