Identification of the novel biomarkers involved in the mitochondrial metabolism-related reactive oxygen species and their role in lung cancer T-cell exhaustion and immunotherapy.

Purpose: To study the role of mitochondrial metabolism and obtain novel biomarkers in immunotherapy for non-small cell lung cancer (NSCLC). Methods: We collected the 188 genes involved in mitochondrial metabolism(MMGs) from the MSIGDB project and then quantified the activity of mitochondrial metabolism. All the NSCLC patients were divided into C1 and C2 clusters based on the 26 prognosis-related MMGs. The differences in biology, differential immune microenvironment, chronic hypoxia and prognosis between C1 and C2 patients were also analyzed. In addition, we validated the results of bioinformatics analysis in lung cancer tissues and cell lines. Results: Patients in the C2 cluster had a higher level of mitochondrial metabolism. Patients in the C2 cluster responded better to immunotherapy and had a lower level of T-cell exclusion. The markers of T-cell failure were upregulated in the C1 patients. Hypoxia can lead to a high percentage of C1 patients. ADH1C might be involved in mitochondrial metabolism and immunotherapy response, which can be affected by hypoxia, making it an underlying biomarker. The expression levels of ADH1C in BEAS-2B, H1299, A549 and H460 cells were detected, revealing that ADH1C is upregulated in lung cancer cells. We observed that patients with low ADH1C expression had a longer survival time. The enzyme activities of HK, PK, LDH and SDH were significantly reduced in H1299 and H460 cells with ADH1C knockdown, along with more ROS. Furthermore, the expression levels of PD-L1 and HHLA2 in tumor tissues were analyzed, which found that ADH1C was significantly positively correlated with the expression of PD-L1 and HHLA2. Conclusions: In summary, our study comprehensively explored the molecules involved in mitochondrial metabolism and their role in immunotherapy and T lymphocyte failure.

Comprehensive analysis of m6A modification patterns and m6A-related lncRNAs as potential biomarkers in lung adenocarcinoma.

BACKGROUND: N6-methyladenosine (m6A) methylation is considered to induce tumor cell proliferation, migration, and apoptosis. Understanding the mechanism of m6A-related lncRNAs in the development of lung adenocarcinoma (LUAD) may help predict prognosis. METHODS: m6A-related lncRNAs related to lung cancer were identified and combined with the MeRIP-Seq dataset. The consensus clustering method was utilized to divide LUAD patients, and prognostic model was constructed using the Lasso Cox algorithm. The cluster profiler package was used for gene ontology and KEGG enrichment. The proportion of immune infiltration was estimated using the CIBERSORT algorithm. The decision tree was constructed by the rpart package, and nomograms were built by the rms package. The Connectivity Map database was analyzed for the therapeutic effects of small molecule drugs for LUAD. In addition, qPCR, colony formation and transwell assays were performed to validate functions of m6A-associated lncRNAs. RESULTS: Nineteen m6A-modified lncRNAs in LUAD were identified. LUAD patients were divided into two categories based on the expression of 19 m6A-related lncRNAs. Cluster 2 patients had better antigen production and expression, while naive B cells, plasma cells, and activated NK cells were lower in cluster 1. Nine m6A-related lncRNAs were selected to establish a risk model for evaluating the prognosis of LUAD patients. The high-risk group had higher tumor mutational burden and lower TIDE scores with more gamma delta T cells and neutrophils. Nomograms showed that the prognostic model had predominant predictive ability for LUAD patients based on the risk score analyzed by the decision tree model. Benzo(a)pyrene and neurodazine might improve the prognosis of LUAD patients. The qRT-PCR results confirmed the reliability of the analytical results. CONCLUSION: The establishment of a prognostic model of m6A-related lncRNAs can independently predict overall survival in LUAD and may help to develop personalized immunotherapy strategies.

Identification of Six Novel Prognostic Gene Signatures as Potential Biomarkers in Small Cell Lung Cancer.

OBJECTIVE: As a subgroup of lung cancer, small cell lung cancer (SCLC) is characterized by a short tumor doubling time, high rates of early occurred distant cancer spread and poor outcomes. Our study aimed to identify novel molecular markers associated with SCLC prognosis. METHODS: Microarray data from the Gene Expression Omnibus (GEO) database of SCLC tumors and paired normal tissues were obtained. In the dataset, Differentially expressed genes (DEGs) which were identified by comparing gene expression between normal lung and SCLC samples, were screened using the R language. The STRING database was used to map protein-protein interaction (PPI) networks, and these were visualized with the Cytoscape software. Go enrichment analysis and prediction were performed using the Metascape database and the results were visualized. Autophagy-related prognostic genes were identified by univariate COX regression analysis. Subsequently, stepwise model selection using the Akaike information criterion (AIC) and multivariate COX regression model was performed to construct DEGs signature. Survival receiver operating characteristic (ROC) analysis was used to assess the performance of survival prediction. At last, we evaluated the differences in drug sensitivity of the two groups of patients to common chemotherapeutic drugs and small-molecule targeted drugs. RESULTS: A total of 441 identified DE genes, including 412 downregulated and 29 upregulated genes were identified. GO enrichment analyses showed that DEGs were significantly enriched in the collagen-containing extracellular matrix and extracellular matrix organization. 16 genes were individually associated with OS in univariate analyses. The high expression of 6 genes (HIST1H4L, RP11-16O9.2, SNORA71A, SELV, FAM66A and BRWD1-AS1)) was associated with the poor prognosis of SCLC patients. To predict patients' outcomes, we developed an individual's risk score model based on the 6 genes. We found that SCLC patients with a low-risk score had significantly better survival than those with a high-risk score. What's more, association analysis between clinicopathological factors and gene signature showed the risk score was higher in patients with higher clinical stage or T stage. What's more, the patients in the high-risk score group had better treatment effects for etoposide and docetaxel. This suggests that our model can guide clinical treatment decisions. CONCLUSION: A novel six-gene signature was determined for prognostic prediction in SCLC. Our findings may provide new insights into the precise treatment and prognosis prediction of SCLC.

Machine learning reveals two heterogeneous subtypes to assist immune therapy based on lipid metabolism in lung adenocarcinoma.

Background: Lipid metabolism pivotally contributes to the incidence and development of lung adenocarcinoma (LUAD). The interaction of lipid metabolism and tumor microenvironment (TME) has become a new research direction. Methods: Using the 1107 LUAD records from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, a comprehensive exploration was performed on the heterogeneous lipid metabolism subtypes based on lipid metabolism genes (LMGs) and immune-related genes (LRGs). The clinical significance, functional status, TME interaction and genomic changes of different subtypes were further studied. A new scoring system, lipid-immune score (LIS), was developed and validated. Results: Two heterogeneous subtypes, which express more LMGs and show the characteristics of tumor metabolism and proliferation, are defined as lipid metabolism phenotypes. The prognosis of lipid metabolism phenotype is poor, and it is more common in patients with tumor progression. Expressing more IRGs, enrichment of immunoactive pathways and infiltration of effector immune cells are defined as immunoactive phenotypes. The immunoactive phenotype has a better prognosis and stronger anti-tumor immunity and is more sensitive to immunotherapy. In addition, KEAP1 is a driving mutant gene in the lipid metabolism subtype. Finally, LIS was developed and confirmed to be a robust predictor of overall survival (OS) and immunotherapy in LUAD patients. Conclusion: Two heterogeneous subtypes of LUAD (lipid metabolism subtype and immune activity subtype) were identified to evaluate prognosis and immunotherapy sensitivity. Our research promotes the understanding of the interaction between lipid metabolism and TME and offers a novel direction for clinical management and precision therapy aimed to LUAD patients.

Identification pyroptosis-related gene signature to predict prognosis and associated regulation axis in colon cancer.

Background: Pyroptosis is an important component of the tumor microenvironment and associated with the occurrence and progression of cancer. As the expression of pyroptosis-related genes and its impact on the prognosis of colon cancer (CC) remains unclear, we constructed and validated a pyroptosis-related genes signature to predict the prognosis of patients with CC. Methods: Microarray datasets and the follow-up clinical information of CC patients were obtained from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. Candidate genes were screened out for further analysis. Various methods were combined to construct a robust pyroptosis-related genes signature for predicting the prognosis of patients with CC. Based on the gene signature and clinical features, a decision tree and nomogram were developed to improve risk stratification and quantify risk assessment for individual patients. Results: The pyroptosis-related genes signature successfully discriminated CC patients with high-risk in the training cohorts. The prognostic value of this signature was further confirmed in independent validation cohort. Multivariable Cox regression and stratified survival analysis revealed this signature was an independent prognostic factor for CC patients. The decision tree identified risk subgroups powerfully, and the nomogram incorporating the gene signature and clinical risk factors performed well in the calibration plots. Conclusion: Pyroptosis-related genes signature was an independent prognostic factor, and can be used to predict the prognosis of patients with CC.

A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung Cancer.

Objectives: This study aimed to identify a molecular marker associated with the prognosis of non-small-cell lung cancer (NSCLC). Materials and Methods: The RNA sequencing data and clinical information of NSCLC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression gene modules and differentially expressed genes (DEGs) by comparing gene expression between NSCLC tumor tissues and normal tissues. Subsequently, the functional enrichment analysis of the DEGs was performed. Kaplan-Meier survival analysis and the GEPIA2 online tool were performed to investigate the relationship between the expression of these genes of interest and the survival of NSCLC patients, and to validate one most survival-relevent hub gene, as well as validated the hub gene using independent datasets from the GEO database. Further analysis was carried out to characterize the relationship between the hub gene and tumor immune cell infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and other known biomarkers of lung cancer. The related genes were screened by analyzing the protein-protein interaction (PPI) network and the survival model was constructed. GEPIA2 was applied in the potential analysis of pan-cancer biomarker of hub gene. Results: 57 hub genes were found to be involved in intercellular connectivity from the 779 identified differentially co-expressed genes. Myeloid-associated differentiation marker (MYADM) was strongly associated with overall survival (OS) and disease-free survival (DFS) of NSCLC patients, and high MYADM expression was associated with poor prognosis. Thus, MYADM was identified as a risk factor. Additionally, MYADM was validated as a survival risk factor in NSCLC patients in two independent datasets. Further analysis showed that MYADM was nagetively associated with TMB, and was positively correlated with macrophages, neutrophils, and dendritic cells, suggesting its role in regulating tumor immunity. The MYADM expression differed across many types of cancer and had the potential to serve as a pan-cancer marker. Conclusion: MYADM is an independent prognostic factor for NSCLC patients, which can predict the progression of cancer and play a role in the tumor immune cell infiltration in NSCLC.

Six MicroRNA Prognostic Models for Overall Survival of Lung Adenocarcinoma.

Objective: The purpose of this study is to screen for microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) and to explore its prognosis and effects on the tumor microenvironment in patients with LUAD. Methods: Gene expression data, miRNA expression data, and clinical data for two different databases, TCGA-LUAD and CPTAC-3 LUAD, were downloaded from the GDC database. The miRNA prognosis of LUAD was filtered by the Cox proportional hazard model and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The performance of the model was validated by time-dependent receiver operating characteristics (ROC) curves. Possible biological processes associated with the miRNAs target gene were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the prognostic model was scored by risk, divided into high- and low-risk groups by median, and the differences in the immersion level of 21 immune cells in the high- and low-risk groups were assessed. To gain a deeper understanding of the underlying mechanism behind the model, the two most important miRNAs in the model, miR-195-3p and miR-5571-5p, were selected for HPA database validation and ceRNA network construction. Results: Of the 209 variance expressions identified in the screening analysis, 145 were upregulated and 64 were downregulated by miRNAs. The prognostic models of six miRNA genes were obtained: miR-195-3p, miR-5571-5p, miR-584-3p, miR-494-3p, miR-4664-3p, and miR-1293. These six genes were significantly associated with survival rates in LUAD patients. In particular, miR-1293, miR-195-3p, and miR-5571-5p are highly correlated with OS. The higher expression of miR-195-3p and miR-5571-5p, the better survival of LUAD OS is, and these two miRNA expressions contribute the most to the model. Finally, after sorting the risk scores calculated from low to high using the prognostic model, the patients with higher scores had shorter survival time and higher frequency of death, and there were significant differences in the immersion levels of 21 immune cells in the high- and low-risk groups. ceRNA network analysis found that TM9SF3 was regulated by miR-195-3p and was highly expressed in the tissues of LUAD patients, and the prognosis of the patients was poor. Conclusions: miR-195-3p, miR-5571-5p, miR-584-3p, miR-494-3p, miR-4664-3p, and miR-1293 may be used as new biomarkers for prognosis prediction of LUAD. Our results also identified a lncRNA MEG3/miR-195-3p/RAB1A/TM9SF3 regulatory axis, which may also play an important role in the progression of LUAD. Further study needs to be conducted to verify this result.

Development and validation of a novel fibroblast scoring model for lung adenocarcinoma.

The interaction between cancer-associated fibroblasts (CAFs) and the tumor microenvironment (TME) is a key factor for promoting tumor progression. In lung cancer, the crosstalk between CAFs and malignant and immune cells is expected to provide new directions for the development of immunotherapy. In this study, we have systematically analyzed a single-cell dataset and identified interacting genes between CAFs and other cells. Subsequently, a robust fibroblast-related score (FRS) was developed. Kaplan-Meier (KM) and ROC analyses showed its good predictive power for patient prognoses in the training set comprising of specimens from the cancer genome atlas (TCGA) and in three external validation sets from the Gene Expression Omnibus (GEO). Univariate and multivariate Cox regression analyses suggested that FRS was a significant prognostic factor independent of multiple clinical characteristics. Functional enrichment and ssGSEA analyses indicated that patients with a high FRS developed "cold" tumors with active tumor proliferation and immunosuppression capacities. In contrast, those with a low FRS developed "hot" tumors with active immune function and cell killing abilities. Genomic variation analysis showed that the patients with a high FRS possessed a higher somatic mutation burden and copy number alterations and were more sensitive to chemotherapy; patients with a low FRS were more sensitive to immunotherapy, particularly anti-PD1 therapy. Overall, these findings advance the understanding of CAFs in tumor progression and we generated a reliable FRS-based model to assess patient prognoses and guide clinical decision-making.

Development and validation of a combined ferroptosis- and pyroptosis-related gene signatures for the prediction of clinical outcomes in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a very heterogeneous cancer with a bad prognosis. Pyroptosis and ferroptosis are two newly discovered forms of regulated cell death, which can trigger inflammation-related immunosuppression in tumor microenvironments, thereby promoting tumor growth. So far, there has been no thorough systematic investigation of the predictive values of ferroptosis and pyroptosis-related genes in LUAD. Therefore, in this study, we conducted a combined analyses in the gene expression of ferroptosis and pyroptosis and identified four distinct subgroups: immobility, ferroptosis, pyroptosis, and mixed. The gene sets most closely associated to both ferroptosis and pyroptosis were utilized to build a risk prediction model based on their variations in survival and biological activities. More importantly, our conclusions from bioinformatics analyses were validated by external experiments in patients with LUAD. In conclusion, the establishment of LUAD subgroups based on the ferroptosis- and pyroptosis-related gene expression profile provided new insights into understanding the roles of programmed cell death in oncogenesis and might contribute to the development of individualized therapy.

Development and Validation of a DNA Methylation-related Classifier of Circulating Tumour Cells to Predict Prognosis and to provide a therapeutic strategy in Lung Adenocarcinoma.

Background: A significant factor influencing the prognosis of lung adenocarcinoma (LUAD) is tumor metastasis. Studies have shown that abnormal DNA methylation in circulating tumor cells (CTCs) is associated with tumour metastasis. Based on the genes expressed in CTCs that play an important role in DNA methylation, we hope to build a risk model to predict prognosis and provide a therapeutic strategy in LUAD. Methods: The CTC sequencing data for LUAD were obtained from GSE74639, which contains 10 CTC samples and 6 primary tumour samples. To carefully assess the clinical value, functional status, involvement of the tumor microenvironment (TME) based on the risk model, and genetic variants based on based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), a reliable risk model was successfully built. Results: Three differentially methylated genes (DMGs) of CTCs for LUAD, including mitochondrial ribosomal protein L51 (MRPL51), STE20-like kinase (SLK), and protein regulator of cytokinesis 1(PRC1), were effectively used to construct a risk model. Both the training and validation cohorts' stability and accuracy of the risk model were evaluated. Each patient in the TCGA-LUAD cohort received a risk score, and based on the median score, they were divided into high- and low-risk groups. The tumors in the high-risk group in this study were classified as "cold" and immunosuppressed, which may be linked to a poor prognosis. The tumors in the low-risk group, however, were deemed "hot" and had immune hyperfunction linked to a positive prognosis. Additionally, patients in the low-risk group showed greater sensitivity to immunotherapy than those in the high-risk group. Conclusions: Based on DMGs of CTCs from LUAD, we successfully developed a predictive risk model and discovered differences in biological function, TME, genetic variation, and clinical outcomes between those at high and low risk group.

The cross-talk of cancer-associated fibroblasts assist in prognosis and immunotherapy in patients with breast carcinoma.

The association between cancer-associated fibroblasts (CAFs) and tumor microenvironment (TME) is a key factor in promoting tumor progression. However, the correlation between CAFs and TME in breast carcinoma has not been elucidated. Thus, further study about the cross-effect between CAFs and TME can provide novel strategies for breast carcinoma treatment, particularly targeted immunotherapy. First, we systematically analyzed cell communication in a single-cell dataset and identified the interacted genes between CAFs and TME components. Then, a robust fibroblast-related score (FRS) model was developed using the LASSO algorithm. The FRS can be a reliable adverse prognostic factor in three cohorts with breast carcinoma. Functional enrichment analysis and single-sample Gene Set Enrichment Analysis showed that patients with a high FRS had cold tumors with active proliferation and immunosuppression. Patients with a low FRS presented with hot tumors with active immune and cell-killing functions. Genomic variation analysis revealed that patients with a low FRS had a higher somatic mutation load and copy number variation burden. Finally, patients with a low FRS were more sensitive to chemotherapy and immunotherapy, particularly anti-PD-1 therapy. In conclusion, a reliable FRS model was constructed not only reliable for predicting prognosis but also competent to estimate clinical immunotherapy and chemotherapy response for patients with BRCA, which might provide significant clinical implications for guiding clinical decision-making for patients with BRCA.

High Expression of TACC3 Is Associated with the Poor Prognosis and Immune Infiltration in Lung Adenocarcinoma Patients.

Background: Lung adenocarcinoma (LUAD) has been recognized as one of the commonest aggressive malignant tumors occurring in humans. The transforming acidic coiled-coil-containing protein 3 (TACC3) seems to be a probable prognostic marker and treatment target for non-small-cell lung cancer (NSCLC). Nevertheless, there exist no reports on the association between TACC3 and immunotherapy or other therapeutic interventions in LUAD. Methods: Premised on the data accessed from The Cancer Genome Atlas- (TCGA-) LUAD, we carried out bioinformatics analysis. The TACC3 expression in LUAD was analyzed utilizing the GEPIA. A survival module was constructed to evaluate the effect of TACC3 on the survival of patients with LUAD. Logistic regression was undertaken to examine the relationship between TACC3 expression and clinical factors. Protein-protein interaction analysis was performed in the GeneMANIA database, and enrichment analysis and identification of predicted signaling pathways were performed using Gene Ontology and Kyoto Encyclopedia of Genes. Additionally, the Cox regression was used to assess the clinicopathologic features linked to the overall survival in TCGA patients. Lastly, we investigated the link between TACC3 and tumor-infiltrating immune cells (TIICs) through CIBERSORT and the "Correlation" module of GEPIA. The association between TACC3 gene expression and drug response was analyzed using the CellMiner database to predict drug sensitivity. Results: The outcomes illustrated that TACC3 was upregulated and considerably correlated with dismal prognosis in LUAD patients. Moreover, the multivariate Cox regression analysis depicted TACC3 as an independent prognostic marker in LUAD patients. It was also revealed that the expression of TACC3 was related to clinical stage (P = 0.014), age (P = 0.002), and T classification (P ≤ 0.018). Moreover, we discovered that the expression of TACC3 was considerably linked to a wide range of TIICs, especially the T cells and NK cells. Single-cell results found that TACC3 was mainly expressed in the immune cells (especially tprolif cells) and malignant cells. TACC3 gene expression was positively correlated with TMB and MSI, and TACC3 may provide a prediction of the efficacy of immunotherapy. Moreover, the correlation analysis between TACC3 gene expression and immune checkpoint gene expression revealed that TACC3 may coordinate the activities of these ICP genes in different signal transduction pathways. TACC3 is related to biological progress (BP), cellular component (CC), and molecular function (MF). The pathways involved in the interaction network involving TACC3 include nonhomologous end-joining, RNA transport, pantothenate and CoA biosynthesis, homologous recombination, and nucleotide excision repair. Furthermore, we investigated the association between the expression of TACC3 and the use of antitumor drugs, and TACC3 was positively correlated with response to most drugs. Conclusion: The findings from this research offer robust proof that the expression of TACC3 could be a prognostic marker correlated with TIICs in LUAD. TACC3 can also provide new ideas for immunotherapy as a potential therapeutic target.

Case-control study on TP73 rs1801173 C > T gene polymorphism and susceptibility to gastric cancer in a Chinese Han population.

BACKGROUND: This study investigated the role of TP73 gene polymorphism, rs1801173on risk of gastric cancer. METHODS: We conducted a case-controlled study including 577 primary gastric cancer and 678 normal control cases. The target gene fragment was amplified using PCR using blood samples collected from patients. Allele analysis and genotyping were performed using snapshot method. RESULTS: The findings showed that the control group had consistent genotype frequency distribution and presented Hardy-Weinberg equilibrium. The results showed no significant differences in sex, drinking history and age distributions between subjects with the polymorphism and subjects in the control group. Smoking status was correlated with incidence of gastric cancer (P = 0.006). The rs1801173 locus of TP73 gene contained 3 genotypes including: TT, CT, and CT. Logistic regression analysis showed that distribution of recessive model and dominant model was comparable between the two groups before (P = 0.688; 0.937) or after (P = 0.703; 0.990) adjusting for confounders. The distribution frequency in case group was not significantly different relative to that of the control group (P = 0.763). CONCLUSION: Smoking can independently influence the risk of gastric cancer. TP73 gene rs1801173 polymorphism was not significantly correlated with risk of gastric cancer.

Low-cost polymer-film spiral inertial microfluidic device for label-free separation of malignant tumor cells.

We developed a low-cost polymer-film spiral inertial microfluidic device for the effective size-dependent separation of malignant tumor cells. The device was fabricated in polymer films by rapid laser cutting and chemical bonding. After fabricating the prototype device, the separation performance of our device was evaluated using particles and cells. The effects of operational flow rate, cell diameter, and cell concentration on the separation performance were explored. Our device successfully separated tumor cells from polydisperse white blood cells according to their different migration modes and lateral positions. Then, the separation of rare cells was carried out using the high-concentration lysed blood spiked with 200 tumor cells. Experimental results showed that 83.90% of the tumor cells could be recovered, while 99.87% of white blood cells could be removed. We successfully employed our device for processing clinical pleural effusion samples from patients with advanced metastatic breast cancer. Malignant tumor cells with an average purity of 2.37% could be effectively enriched, improving downstream diagnostic accuracy. Our device offers the advantages of label-free operation, low cost, and fast fabrication, thus being a potential tool for effective cell separation.

Identification and validation of a muscle failure index to predict prognosis and immunotherapy in lung adenocarcinoma through integrated analysis of bulk and single-cell RNA sequencing data.

Background: It was previously reported that the production of exerkines is positively associated with the beneficial effects of exercise in lung adenocarcinoma (LUAD) patients. This study proposes a novel scoring system based on muscle failure-related genes, to assist in clinical decision making. Methods: A comprehensive analysis of bulk and single cell RNA sequencing (scRNA-seq) of early, advanced and brain metastatic LUAD tissues and normal lung tissues was performed to identify muscle failure-related genes in LUAD and to determine the distribution of muscle failure-related genes in different cell populations. A novel scoring system, named MFI (Muscle failure index), was developed and validated. The differences in biological functions, immune infiltration, genomic alterations, and clinical significance of different subtypes were also investigated. Results: First, we conducted single cell analysis on the dataset GSE131907 and identified eight cell subpopulations. We found that four muscle failure-related genes (BDNF, FNDC5, IL15, MSTN) were significantly increased in tumor cells. In addition, IL15 was widely distributed in the immune cell population. And we have validated it in our own clinical cohort. Then we created the MFI model based on 10 muscle failure-related genes using the LASSO algorithm, and MFI remained an independent prognostic factor of OS in both the training and validation cohorts. Moreover, we generated MFI in the single-cell dataset, in which cells with high MFI received and sent more signals compared to those with low MFI. Biological function analysis of both subtypes revealed stronger anti-tumor immune activity in the low MFI group, while tumor cells with high MFI had stronger metabolic and proliferative activity. Finally, we systematically assessed the immune cell activity and immunotherapy responses in LUAD patients, finding that the low MFI group was more sensitive to immunotherapy. Conclusion: Overall, our study can improve the understanding of the role of muscle failure-related genes in tumorigenesis and we constructed a reliable MFI model for predicting prognosis and guiding future clinical decision making.

Identification of Potential Prognostic and Predictive Biomarkers for Immune-Checkpoint Inhibitor Response in Small Cell Lung Cancer.

BACKGROUND Immune-checkpoint inhibitors have propelled the field of therapeutics for small cell lung cancer (SCLC) treatment, but are only beneficial to some patients. The objective of this study was to identify valid biomarkers for good potential response to immunotherapy. MATERIAL AND METHODS We performed an integrated analysis of the available datasets from the Gene Expression Omnibus (GEO) projects, Cancer Cell Line Encyclopedia (CCLE), TISIDB database, and Lung Cancer Explorer (LCE) database. Six prognosis-related genes (MCM2, EZH2, CENPK, CHEK1, CDKN2A, and EXOSC2) were identified utilizing the meta workflow of data analysis methods. We performed subclass mapping to compare their expression profiles to other datasets of patients who responded to immunotherapy. A drug sensitivity predictive model was used to predict the chemotherapeutic response to cisplatin and etoposide. RESULTS Our results showed that the expression of the 6 key genes was significantly associated with the overall survival of patients with SCLC. Lower expression of these 6 genes was correlated to the response to anti-PD-1 treatment. Additionally, low expression of MCM2, EZH2, CENPK, and CHEK1 was correlated with increased sensitivity to cisplatin, but not etoposide. CONCLUSIONS Overall, our data showed that MCM2, EZH2, CENPK, CHEK1, CDKN2A, and EXOSC2 are potential prognostic and predictive biomarkers for response to immune-checkpoint inhibitor treatment in patients with SCLC. Further studies with large sample sizes are required to validate our findings and to explore the detailed mechanisms underlying the role of these genes in SCLC.

Development and Validation of an E2F-Related Gene Signature to Predict Prognosis of Patients With Lung Squamous Cell Carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) generally correlates with poor clinical prognoses due to the lack of available prognostic biomarkers. This study is designed to identify a potential biomarker significant for the prognosis and treatment of LUSC, so as to provide a scientific basis for clinical treatment decisions. METHODS: Genomic changes in LUSC samples before and after radiation were firstly discussed to identify E2 factor (E2F) pathway of prognostic significance. A series of bioinformatics analyses and statistical methods were combined to construct a robust E2F-related prognostic gene signature. Furthermore, a decision tree and a nomogram were established according to the gene signature and multiple clinicopathological characteristics to improve risk stratification and quantify risk assessment for individual patients. RESULTS: In our investigated cohorts, the E2F-related gene signature we identified was capable of predicting clinical outcomes and therapeutic responses in LUSC patients, besides, discriminative to identify high-risk patients. Survival analysis suggested that the gene signature was independently prognostic for adverse overall survival of LUSC patients. The decision tree identified the strong discriminative performance of the gene signature in risk stractification for overall survival while the nomogram demonstrated a high accuracy. CONCLUSION: The E2F-related gene signature may help distinguish high-risk patients so as to formulate personalized treatment strategy in LUSC patients.

Circular RNA circ_0008274 upregulates granulin to promote the progression of hepatocellular carcinoma via sponging microRNA -140-3p.

Circular RNA (circRNA) features prominently in the progression of hepatocellular carcinoma (HCC), of which the biological function and potential mechanism of circ_0008274 in HCC are obscure. The present study aims to explore circ_ 0008274's biological functions and underlying mechanisms in HCC. The expressions of circ_0008274, miR-140-3p and Granulin (GRN) mRNA in HCC tissues and cells were investigated by quantitative real-time polymerase chain reaction. Besides, GRN protein expression was measured by Western blot. Furthermore, chi-square test was used to probe the interrelation between circ_0008274 expression and clinicopathological parameters. In addition, cell counting kit-8 (CCK-8) and EdU assays were applied to detect cell proliferation. Moreover, transwell assay was used to detect cell migration and invasion. What's more, bioinformatics prediction, dual-luciferase reporter gene assay and RNA Immunoprecipitation experiments were used to corroborate the targeting interrelations among circ_0008274, miR-140-3p and GRN. Herein we reported that circ_0008274 was highly expressed in HCC, and its high expression enhanced the proliferation, migration, and invasion of HCC cells, while depleting circ_0008274 inhibited the malignant biological behaviors of HCC cells. Mechanistically, circ_0008274 upregulates GRN expressions via adsorbing miR-140-3p to expedite the progression of HCC.

Does the Impact of Frailty on All-Cause Mortality in Older Persons Differ between Women and Men? A Meta-Analysis.

OBJECTIVE: Sex-specific impact of frailty on all-cause mortality in older age population remains controversial. This meta-analysis aimed to evaluate the association between frailty and all-cause mortality in older age women vs men from the general population. DESIGN: Meta-analysis. SETTING AND PARTICIPANTS: PubMed and Embase databases were searched until March 25, 2020 for studies reporting sex-specific association of frailty phenotype or index with all-cause mortality among the older general population (age ≥60 years) in the same study. MEASUREMENTS: All-cause mortality for the frail vs robust individuals. RESULTS: Eight studies enrolling a total of 87, 000 individuals were identified. Using the frailty phenotype, the pooled risk ratio of all-cause mortality was 2.41 [95% confidence interval (CI) 2.07-2.80] for frail women and 2.94 (95% CI 2.12-4.09) for frail men. Using the frailty index, the pooled risk ratio of all-cause mortality was 3.23 (95% CI 2.16-4.83) for frail women and 2.63 (95% CI 2.33-2.98) for frail men. The pooled female-to-male ratio of relative risks was 0.93 (95% CI 0.76-1.13) for the frailty phenotype and 1.22 (95% CI 0.79-1.88) for the frailty index. CONCLUSIONS AND IMPLICATIONS: Older men and women with frailty confer a higher risk of all-cause mortality in the general population from the same source. However, there is no significant sex difference in the association between phenotype or index and all-cause mortality.

Research and application of single-cell sequencing in tumor heterogeneity and drug resistance of circulating tumor cells.

Malignant tumor is a largely harmful disease worldwide. The cure rate of malignant tumors increases with the continuous discovery of anti-tumor drugs and the optimisation of chemotherapy options. However, drug resistance of tumor cells remains a massive obstacle in the treatment of anti-tumor drugs. The heterogeneity of malignant tumors makes studying it further difficult for us. In recent years, using single-cell sequencing technology to study and analyse circulating tumor cells can avoid the interference of tumor heterogeneity and provide a new perspective for us to understand tumor drug resistance.