Overall Survival In Non-small Cell Lung Cancer Research Articles

Bridging responses to a human telomerase reverse transcriptase-based peptide cancer vaccine candidate in a mechanism-based model

Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed‐effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage.

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer

Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic. Many past studies have suggested identifying the robust radiomic feature to develop a prediction model. In our earlier study, we identified robust radiomic features for prediction model development. The objective of this study was to develop and validate the robust radiomic signatures for predicting 2-year overall survival in non-small cell lung cancer (NSCLC). This retrospective study included a cohort of 300 stage I–IV NSCLC patients. Institutional 200 patients’ data were included for training and internal validation and 100 patients’ data from The Cancer Image Archive (TCIA) open-source image repository for external validation. Radiomic features were extracted from the CT images of both cohorts. The feature selection was performed using hierarchical clustering, a Chi-squared test, and recursive feature elimination (RFE). In total, six prediction models were developed using random forest (RF-Model-O, RF-Model-B), gradient boosting (GB-Model-O, GB-Model-B), and support vector(SV-Model-O, SV-Model-B) classifiers to predict 2-year overall survival (OS) on original data as well as balanced data. Model validation was performed using 10-fold cross-validation, internal validation, and external validation. Using a multistep feature selection method, the overall top 10 features were chosen. On internal validation, the two random forest models (RF-Model-O, RF-Model-B) displayed the highest accuracy; their scores on the original and balanced datasets were 0.81 and 0.77 respectively. During external validation, both the random forest models’ accuracy was 0.68. In our study, robust radiomic features showed promising predictive performance to predict 2-year overall survival in NSCLC.

Dexamethasone induces cancer mitigation and irreversible senescence in lung cancer cells via damaging cortical actin and sustained hyperphosphorylation of pRb

Activation of the glucocorticoid receptors by its cognate ligand, dexamethasone (DEX) is commonly used as an adjuvant treatment in solid tumors. However, its direct effect on cancerous phenotype is not fully understood. We explored the effect and molecular mechanisms of DEX action in lung cancer. In in vitro experiments, DEX treatment causes decrease in migration, invasion and colony formation ability of A549 cells even at lower doses. DEX also decreased adhesion of A549 cells by reducing the formation of cortical actin. Treatment with RU486, a GR antagonist, indicated that these effects are partially mediated through GR. Further; DEX induces G0/G1 arrest of A549 cells. Mechanistically, DEX induces expression of both CDK inhibitors (p21Cip1, p27Kip1) and cyclin-dependent kinases (CDK4, CDK6). Due to this compensatory activation of CDKs and CDKIs, DEX induces the hyper phosphorylation state of Rb protein (pRb) leading to irreversible senescence as confirmed by β-gal staining. Next, in clinical dataset of NSCLC (Non-small cell lung cancer), GR was lowly expressed in cancer patients as compared to the normal group, where higher expression of GR led to higher overall survival of NSCLC indicating for a protective role of GR. Interestingly, when combined with chemotherapeutic agents, DEX can modulate the drug-sensitivity of cells. Taken together, these data indicate that DEX through GR activation may suppress tumor growth by decreasing proliferation and inducing irreversible senescence and combination of standard chemotherapy and DEX can be a potential treatment for NSCLC.

Outcome analysis of immunotherapy and possible prognostic role of pretreatment neutrophil-lymphocyte ratio in lung cancer: An Appalachian study.

e21053 Background: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of lung cancer resulting in better survival benefits. We are reporting outcome analysis of lung cancer patients treated with ICI in our institution. Methods: We performed retrospective chart review of lung cancer patients treated with ICI at our institution between June 2016 and March 2022. Results: A total of 289 patients were identified- 241 had non-small cell lung cancer (NSCLC) and 48 had small cell lung cancer (SCLC). A total of 109 patients (37.7%) developed immunotherapy related adverse events (IRAE): 92 (38.1%) with NSCLC and 17 (35.4%) with SCLC. Average time to develop IRAE was 3 months in both NSCLC and SCLC. IRAE grades 1,2,3,4 were noted in 33%, 20%, 15%, 31% patients respectively. 34 of 63 (53%) patients who received Ipilimumab-Nivolumab developed more IRAE than any other ICI with fatigue and dermatitis being the most common IRAE associated with this ICI. 79 of 183 (43%) patients who received RT developed IRAE (p = 0.012). Median overall survival (OS) for NSCLC was 35 months, 41 months, 27 months, 15 months for stages 1,2,3,4, respectively (p = 0.03). Median OS for extensive stage SCLC was 14.6 months. Median OS for NSCLC with and without IRAE was 30 and 23 months, respectively (p = 0.02). Median OS for SCLC with and without IRAE was 16.7 and 16.2 months, respectively (p = 0.5). Median OS for NSCLC when steroids were used to treat IRAE was 25 months, and 63 months if no steroids were used (p = 0.027). Better median overall survival rate was noted in patients with low Neutrophil-Lymphocyte ratio (defined as NLR < 5) 29.23 months vs 20.59 months (high NLR) (p = 0.019). Although demographics like male sex, smoking history, higher BMI and poor ECOG performance status had numerically increased the risk of developing IRAE, no statistical significance was noted. Conclusions: Overall results of our analysis are mostly consistent with the current literature on survival rates in Lung Cancer. IRAE may have a predictive role in survival, as we observed that patients with NSCLC developing IRAE had better survival rates. Ipilimumab-Nivolumab combination resulted in more IRAE than any other ICI. Pneumonitis was the most common IRAE among all patients receiving ICI. Also, Radiation-therapy increased the risk of developing IRAE. Our observation that steroid use in treating IRAE was associated with lower survival is concerning. This may be due to higher grade IRAE/toxicities and possible decreased ICI effectiveness. Regardless of IRAE, low pretreatment NLR resulted in better survival rates. The role of pretreatment NLR as a potential prognostic tool to assess survival outcomes requires validation in a randomized clinical trial.

The role of miR-143-3p/FNDC1 axis on the progression of non-small cell lung cancer.

The study aimed to explore the functional role of fibronectin type III domain containing 1 (FNDC1) in nonsmall cell lung cancer (NSCLC), as well as the mechanism governing its expression. The expression levels ofFNDC1 and related genes in tissue and cell samples were detected by qRT-PCR. Kaplan-Meier analysis was employed to analyze the association between FNDC1 level and the overall survival of NSCLC patients.Functional experiments such as CCK-8 proliferation, colony formation, EDU staining, migration and invasion assays were conducted to investigate the functional role of FNDC1 in regulating the malignancy of NSCLCcells. Bioinformatic tools and dual-luciferase reporter assay were used to identify the miRNA regulator of FNDC1 in NSCLC cells. Our data revealed the upregulation of FNDC1 at mRNA and protein levels in NSCLCtumor tissues cancer cell lines, compared with normal counterparts. NSCLC patients with higher FNDC1 expression suffered from a poorer overall survival. FNDC1 knockdown significantly suppressed the proliferation,migration and invasion of NSCLC cells, and had an inhibitory effect on tube formation. We further demonstrated that miR-143-3p was an upstream regulator of FNDC1 and miR-143-3p expression was repressed inNSCLC samples. Similar to FNDC1 knockdown, miR-143-3p overexpression inhibited the growth, migration and invasion of NSCLC cells. FNDC1 overexpression could partially rescue the effect of miR-143-3p overexpression. FNDC1 silencing also suppressed the tumorigenesis of NSCLC cells in mouse model. In conclusion,FNDC1 promotes the malignant prototypes of NSCLC cells. miR-143-3p is a negative regulator of FNDC1 in NSCLC cells, which may serve as a promising therapeutic target in NSCLC.

Spatial characterization and quantification of CD40 expression across cancer types

BackgroundCD40, a TNF receptor family member, is expressed by a variety of immune cells and is involved in the activation of both adaptive and innate immune responses. Here, we used quantitative immunofluorescence (QIF) to evaluate CD40 expression on the tumor epithelium of solid tumors in large patient cohorts of lung, ovarian, and pancreatic cancers.MethodsTissue samples from nine different solid tumors (bladder, breast, colon, gastric, head and neck, non-small cell lung cancer (NSCLC), ovarian, pancreatic and renal cell carcinoma), constructed in tissue microarray format, were initially assessed for CD40 expression by QIF. CD40 expression was then evaluated on the large available patient cohorts for three of the tumor types demonstrating high CD40 positivity rate; NSCLC, ovarian and pancreatic cancer. The prognostic impact of CD40 expression on tumor cells was also investigated.ResultsCD40 expression on tumor cells was found to be common, with 80% of the NSCLC population, 40% of the ovarian cancer population, and 68% of the pancreatic adenocarcinoma population displaying some degree of CD40 expression on cancer cells. All of three of these cancer types displayed considerable intra-tumoral heterogeneity of CD40 expression, as well as partial correlation between expression of CD40 on tumor cells and on surrounding stromal cells. CD40 was not found to be prognostic for overall survival in NSCLC, ovarian cancer, or pancreatic adenocarcinoma.ConclusionsThe high percentage of tumor cells expressing CD40 in each of these solid tumors should be considered in the development of therapeutic agents designed to target CD40.

Prognostic Values and Prospective Pathway Signaling of miR-25 in Non-Small Cell Lung Cancer: A Bioinformatics Analysis Based on Gene Expression Omnibus

MicroRNAs (MiRNAs) are believed to significantly influence the occurrence and development of Non-small cell lung cancer (NSCLC). This study aims to clarify the action of hsa-miR-25-5p (miR-25) in NSCLC. Public datasets related to miR-25 expression from Gene Expression Omnibus (GEO) were retrieved. MiR-25 expression in the retrieved datasets were calculated using a fix-effects model (REM). Statistical analysis was performed using Stata 15.0, with standard mean difference (SMD) as the effect size. Downloaded from GEO database, two mRNA datasets, GSE18842 and GSE101929, was used to obtain differentially expressed genes (DEGs) using R 4.2.1 software. TargetScan and miRwalk 3.0 were used to predict hsa-miR-25-5p target genes. Subsequently, Gene Ontology (GO) enrichment analysis were conducted. We constructed protein–protein interaction (PPI) network using String and Cytoscape. The prognostic values of top 10 HUB genes were analyzed. Four datasets related to miR-25 expression were retrieved from GEO, including GSE27705, GSE29248, GSE63805 and GSE102286. The results of REM suggested that miR-25 expression was obviously increased in NSCLC versus adjacent noncancerous lung tissues (SMD = 0.06, 95% CI: 0.36–0.84, P <0.001). 244 DEGs associated with both NSCLC and hsa-miR-25-5p were identified. Numerous DEGs were significantly enriched in the locus of cell membranes, playing the roles of protein binding, and biological regulation. Based on PPI analysis, among the top 10 HUB genes, the high expressions of eight genes significantly reduced overall survival in NSCLC (CCNB1, RAD51AP1, MELK, NCAPH, MCM10, NUSAP, PRC1, BRCA). miR-25 displays an critical role in the biological process by binding target genes, influencing prognosis in patients with NSCLC.

Concurrent classic driver oncogenes mutation with ROS1 rearrangement predicts superior clinical outcome in NSCLC patients.

There is high mortality rate and poor prognosis in lung cancer, especially non-small-cell lung cancer (NSCLC). Recent study showed that concurrent classic driver oncogene mutation with ROS1 rearrangement was found in NSCLC patients. However, whether this would affect the development and prognosis of NSCLC is still unclear. To explore the clinical characteristics and prognosis of NSCLC patients harboring concurrent classic driver oncogene mutation with ROS1 rearrangement. A retrospective study was conducted on 220 patients diagnosed with NSCLC. All samples were screened for EGFR and KRAS using amplification-refractory mutation system assay, and for ALK, ROS1 using RT-PCR. The clinical characteristics and clinical outcomes of concurrent gene alterations with ROS1 rearrangement were analyzed. In 220 patients, 12 (5.45%) were ROS1 rearrangement, who tend to be younger, non-smokers. The mutation rates of EGFR, KRAS, ALK and ROS1 in NSCLC were 28.64%, 1.82%, 3.64% and 5.45%, respectively. ROS1 rearrangement was identified to co-occur in 5 (2.27%) NSCLC patients. ROS1/EGFR co-alterations were found in 3.17% of NSCLC patients, 16.67% of ROS1-positive NSCLC patients. Concomitant ROS1/ALK rearrangement constituted 37.50% in ALK-positive patients, and 25.00% in ROS1-positive patients. SDC4-ROS1 was the most common fusion partner in concurrent ROS1 rearrangement patients. The median overall survival of NSCLC with concurrent ROS1 rearrangement group and single ROS1 rearrangement group were 25 months and 14 months. Concurrent driver oncogenes mutation with ROS1 rearrangement defines a unique subgroup of NSCLC. Patients with concomitant ROS1 rearrangement might have a better prognosis.

Potentially functional genetic variants of VAV2 and PSMA4 in the immune-activation pathway and non-small cell lung cancer survival.

Lung cancer has the highest mortality among cancers, represented by a low 5-year survival rate. The function of the immune system has a profound influence on the development and progression of lung cancer. Thus genetic variants of the immune-related genes may serve as potential predictors of non-small cell lung cancer (NSCLC) survival. In the present study, we conducted a two-stage survival analysis in 1,531 NSCLC patients and assessed the associations between genetic variants in the immune-activation gene set and the overall survival (OS) of NSCLC patients. The validated variants were further subjected to functional annotation and in vitro experiments. We identified 25 SNPs spanning six loci associated with NSCLC OS after multiple-testing corrections in all datasets, in which two variants, PSMA4 rs12901682 A > C and VAV2 rs12002767 C > T, were shown to potentially affect lung cancer OS by cis-regulating the expression of the corresponding genes [(HR (95% CI) = 0.76 (0.65-0.89) and 1.36 (1.12-1.65), p = 4.29 × 10-4 and 0.002, respectively]. Our findings provide new insights into the role of genetic variants in the immune-activation pathway genes in lung cancer progression.

Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer.

To evaluate the predictive value of intratumoral and peritumoral radiomics and radiomics nomogram for preoperative lymphovascular invasion (LVI) status and overall survival (OS) in patients with non-small cell lung cancer (NSCLC). In total, 240 NSCLC patients from our institution were randomly divided into the training cohort (n = 145) and internal validation cohort (n = 95) with a ratio of 6:4, and 65 patients from the Cancer Imaging Archive were enrolled as the external validation cohort. We extracted 1217 CT-based radiomics features from the gross tumor volume (GTV) and gross tumor volume incorporating peritumoral 3, 6, and 9 mm regions (GPTV3, GPTV6, GPTV9). A radiomics nomogram based on clinical independent predictors and radiomics score (Radscore) of the best radiomics model was constructed. The correlation between factors and OS was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. Compared with GTV, GPTV3, and GPTV6 radiomics models, GPTV9 radiomics model exhibited better prediction performance with the AUCs of 0.82, 0.75, and 0.67 in the training, internal validation, and external validation cohorts, respectively. In the clinical model, smoking and clinical stage were independent predictors. The nomogram incorporating independent predictors and GPTV9-Radscore was clinically useful, with the AUCs of 0.89, 0.83, and 0.66 in three cohorts. Pathological LVI, GPTV9-Radscore-predicted, and Nomoscore-predicted LVI were associated with poor OS (p < 0.05). CT-based radiomics nomogram can predict LVI and OS in patients with NSCLC and may help in making personalized treatment strategies before surgery. • Compared with GTV, GPTV3, and GPTV6 radiomics models, GPTV9 radiomics model showed better prediction performance for LVI status in NSCLC. • The radiomics nomogram based on GPTV9 radiomics features and clinical independent predictors could effectively predict LVI status and OS in NSCLC and outperformed the clinical model. • The radiomics nomogram had a wider scope of clinical application.

The identification of hub biomarkers and pathways in lung cancer and prognostic evaluation.

BackgroundLung cancer is the most frequently diagnosed malignant tumor and the highest mortality worldwide, and can be divided into two differential histologic subtypes, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). However, there are significant differences in diagnosis and prognosis between NSCLC and SCLC. We aimed to identify hub differentially expressed genes (DEGs) and pathways for diagnostic and prognostic prediction in NSCLC and SCLC.MethodsThree expression profiles (GSE43346, GSE40275 and GSE18842) were obtained through GEO2R tools from Gene Expression Omnibus (GEO) database. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to investigate functional enrichment of the DEGs. The protein–protein interaction network was constructed by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Kaplan-Meier analysis was performed using Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA).ResultsWe have identified 84 overlap DEGs that may play an important role in SCLC & NSCLC. However, we also found some genes were only significantly differential expressed in SCLC or NSCLC. There were 87 DEGs unique to SCLC tissues and 28 DEGs unique to NSCLC ones. Functional analysis results indicated that these DEGs had different biological functions and were significantly enriched in different pathways. Hub DEGs were identified via protein-protein interaction network and cross-validated using Kaplan-Meier plotter and GEPIA. The 14 hub DEGs were highly correlated with the overall survival of NSCLC. Kyoto Encyclopedia of Genes and Genome (KEGG) re-analysis of 14 hub DEGs showed that RRM2, CHEK1 and SERPINB5 enriched in the p53 signaling pathway, RRM2 and TYMS enriched in pyrimidine metabolism pathway maybe play a key role in SCLC&NSCLC and were significantly related to overall survival in patients with NSCLC.ConclusionsRRM2, CHEK1, TYMS and SERPINB5, which are mainly enriched in the p53 signaling pathway and pyrimidine metabolism pathway, were significantly associated with the overall survival of NSCLC patients. These genes could serve as potential prognostic markers in NSCLC and therapeutic target in lung cancer for personalized oncology.

ERβ overexpression may not be a direct prognostic factor in patients with NSCLC: A meta-analysis.

Overall survival of non-small cell lung cancer (NSCLC) patients remains disappointingly low. The estrogen receptor (ER) was considered a promising therapeutic target for NSCLC. Numerous studies have linked expression of ERβ to lung cancer outcome. However, results are conflicting regarding the association of ERβ with surviving lung cancer. The aim of this meta-analysis was to evaluate the prognostic aspect of ERβ expression on survival among NSCLC patients. We performed a final analysis of prognostic value of overexpression ERβ on 3500 patients from 18 evaluable studies (from January 1, 2000 to May 1, 2021). The reference category is specified as low ERβ expression levels. Summarized hazard ratios were calculated. Our study showed that the pooled hazard ratios of ERβ overexpression for overall survival in NSCLC was 0.81 (95% confidence interval (CI): 0.64-1.02, P = 0.07) by univariate analysis and 1.06 (95% CI: 0.83-1.36, P = 0.63) by multivariate analysis. Pooled hazard ratio by univariate analysis in Asian studies was 0.73 (95%CI: 0.59-0.89, P = 0.002). Pooled hazard ratio by univariate analysis was 0.75 (95% CI: 0.61-0.93, P = 0.009) from seven studies reported for nuclear ERβ. No significant results were found in subgroups by multivariate analysis. No significant results were found in studies outside Asia or in studies reported for cytoplasmic ERβ. Our results suggested that expression of ERβ might not be a direct prognostic factor for NSCLC patients. More detailed prospective studies are needed to identify direct prognostic factors in these patients.

Genetic variants in DDO and PEX5L in peroxisome-related pathways predict non-small cell lung cancer survival.

Peroxisomes play a role in lipid metabolism and regulation of reactive oxygen species, but its role in development and progression of non-small cell lung cancer (NSCLC) is not well understood. Here, we investigated the associations between 9708 single-nucleotide polymorphisms(SNPs) in 113 genes in the peroxisome-related pathways and survival of NSCLC patients from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and the Harvard Lung Cancer Susceptibility (HLCS) study. In 1185 NSCLC patients from the PLCO trial, we found that 213 SNPs were significantly associated with NSCLC overall survival (OS)(p ≤ 0.05, Bayesian false discovery probability [BFDP] ≤ 0.80), of which eight SNPs were validated in the HLCS data set. In a multivariate Cox proportional hazards regression model, two independent SNPs (rs9384742 DDO and rs9825224 PEX5L) were significantly associated with NSCLC survival (hazards ratios [HR]of 1.17 with 95% CI [confidence interval] of 1.06-1.28and 0.86 with 95% CI of 0.77-0.96,respectively). Patients with one or twoprotective genotypes had a significantly higher OS (HR: 0.787 [95% CI: 0.620-0.998]and 0.691 [95% CI: 0.543-0.879], respectively). Further expression quantitative trait loci analysis using whole blood and lung tissue showed that the minor allele of rs9384742 DDO was significantly associated with decreased messenger RNA (mRNA) expression levels and that DDO expression was also decreased in NSCLC tumor tissue. Additionally, high PEX5L expression levels were significantly associated with lower survival of NSCLC. Our data suggest that variants in these peroxisome-related genes may influence gene regulation and are potential predictors of NSCLC OS, once validated by additional studies.

Screening and Validation of Significant Genes with Poor Prognosis in Pathologic Stage-I Lung Adenocarcinoma.

Background Although more pathologic stage-I lung adenocarcinoma (LUAD) was diagnosed recently, some relapsed or distantly metastasized shortly after radical resection. The study aimed to identify biomarkers predicting prognosis in the pathologic stage-I LUAD and improve the understanding of the mechanisms involved in tumorigenesis. Methods We obtained the expression profiling data for non-small cell lung cancer (NSCLC) patients from the NCBI-GEO database. Differentially expressed genes (DEGs) between early-stage NSCLC and normal lung tissue were determined. After function enrichment analyses on DEGs, the protein-protein interaction (PPI) network was built and analyzed with the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Overall survival (OS) and mRNA levels of genes were performed with Kaplan–Meier analysis and Gene Expression Profiling Interactive Analysis (GEPIA). qPCR and western blot analysis of hub genes in stage-I LUAD patients validated the significant genes with poor prognosis. Results A total of 172 DEGs were identified, which were mainly enriched in terms related to management of extracellular matrix (ECM), receptor signaling pathway, cell adhesion, activity of endopeptidase, and receptor. The PPI network identified 11 upregulated hub genes that were significantly associated with OS in NSCLC and highly expressed in NSCLC tissues compared with normal tissues by GEPIA. Elevated expression of ANLN, EXO1, KIAA0101, RRM2, TOP2A, and UBE2T were identified as potential risk factors in pathologic stage-I LUAD. Except for ANLN and KIAA0101, the hub genes mRNA levels were higher in tumors compared with adjacent non-cancerous samples in the qPCR analysis. The hub genes protein levels were also overexpressed in tumors. In vitro experiments showed that knockdown of UBE2T in LUAD cell lines could inhibit cell proliferation and cycle progression. Conclusions The DEGs can probably be used as potential predictors for stage-I LUAD worse prognosis and UBE2T may be a potential tumor promoter and target for treatment.

Identification of Prognostic Markers of N6-Methylandenosine-Related Noncoding RNAs in Non-Small-Cell Lung Cancer.

Background Non-small-cell lung cancer (NSCLC) is a major type of lung carcinoma that threatens the health and life of humans worldwide. We aimed to establish an n6-methyladenosine (m6A)-relevant ncRNA model to effectively evaluate the outcome of patients. Methods m6A-Related ncRNAs (lncRNA/miRNA) were acquired from the UCSC Xena database. Pearson's correlation analysis among 21 m6A regulatory factors and ncRNAs were implemented to explore m6A-relevant ncRNAs. Weighted gene co-expression network analysis (WGCNA) identified hub modules of gene associated with prognosis of NSCLC patients. Univariate Cox regression analysis identified 80 m6A-related ncRNAs. Least absolute shrinkage and selector operation (LASSO) filtered out redundant factors and established a risk score model (m6A-NSCLC) in the TCGA training data set. Validation of prognostic ability was performed using testing data sets from the TCGA database. We also conducted a correlation analysis among the risk score and different clinical traits. Both univariate and multivariate Cox analyses were combined to verify prognostic factors which have independent value, and a nomogram on the basis of m6A-NSCLC risk scores and clinical traits was constructed to assess the prognosis of patients. In addition, we screened differentially expressed genes (DEGs) based on different risk scores and performed enrichment analysis. Finally, 21 m6A regulators were detected to be differentially expressed between two risk groups. Results An m6A-NSCLC risk model with 18 ncRNAs was constructed. By comparison with low-risk patients, high-risk score patients had poor prognosis. The distribution of risk score in the tumor size and extent (T), number of near lymph nodes (N), clinical stage, sex, and tumor types was significantly different. The risk score could act as an independent prognostic factor with the nomogram assessing overall survival in NSCLC. DEGs inherent to cell movement and immune regulation were involved in NSCLC development. Furthermore, 18 of 21 m6A regulators were differentially expressed, implying their correlation to survival prognosis. Conclusion The m6A-NSCLC could be effectively utilized for evaluation of prognosis of patients.

A two-phase comprehensive NSCLC prognostic study identifies lncRNAs with significant main effect and interaction

Long noncoding RNA (lncRNA) are involved in regulating physiological behaviors for various malignant tumors, including non-small-cell lung cancer (NSCLC). However, few studies comprehensively evaluated both lncRNA–lncRNA interaction effects and main effects of lncRNA on overall survival of NSCLC. Hence, we performed a two-phase designed study of lncRNA expression in tumor tissues using 604 NSCLC patients from The Cancer Genome Atlas as the discovery phase and 839 patients from Gene Expression Omnibus as the validation phase. In the discovery phase, we adopted a two-step strategy, Screening before Testing, for dimension reduction and signal detection. These candidate lncRNAs first screened out by the weighted random forest (Ranger), were then tested through the Cox proportional hazards model adjusted for covariates. Significant lncRNAs with either type of effects aforementioned were carried forward into the validation phase to confirm their significances again. As a result, in the discovery phase, 19 lncRNAs were identified by Ranger, among which five lncRNAs and one pair of lncRNA–lncRNA interaction exhibited significant effects (FDR-q ≤ 0.05) main and interaction effects on NSCLC survival, respectively, through Cox model. After the independent validation, we finally observed that one lncRNA (ENSG00000227403.1) with main effect was robustly associated with NSCLC prognosis (HRdiscovery = 0.90, P = 1.20 × 10–3; HRvalidation = 0.94, P = 4.11 × 10–3) and one pair of lncRNAs (ENSG00000267121.4 and ENSG00000272369.1) had significant interaction effect on NSCLC survival (HRdiscovery = 1.12, P = 3.07 × 10–4; HRvalidation = 1.11, P = 0.0397). Our comprehensive NSCLC prognostic study of lncRNA provided population-level evidence for further functional study.

Identification of TIMELESS and RORA as key clock molecules of non-small cell lung cancer and the comprehensive analysis

BackgroundThe incidence rate of non-small cell lung cancer (NSCLC) has been increasing worldwide, and the correlation of circadian rhythm disruption with a raised risk of cancer and worse prognosis has been shown by accumulating evidences recently. On the other hand, drug resistance and the impact of tumor heterogeneity have been inevitable in NSCLC therapy. These both lead to an urgent need to identify more useful prognostic and predictive markers for NSCLC diagnosis and treatment, especially on the aspect of circadian clock genes.MethodsThe expression of the main clock genes in cancer was probed with TIMER and Oncomine databases. The prognostic value of key clock genes was probed systematically with the Kaplan–Meier estimate and Cox regression on samples from TCGA database. RT-qPCR was performed on patient tissue samples to further validate the results from databases. The functional enrichment analysis was performed using the “ClusterProfiler” R package, and the correlation of key clock genes with tumor mutation burden, immune checkpoint, and immune infiltration levels were also assessed using multiple algorithms including TIDE, TIMER2.0, and XCELL.ResultsTIMELESS was significantly upregulated in lung tissue of clinical lung cancer patients as well as TCGA and Oncomine databases, while RORA was downregulated. Multivariate Cox regression analysis indicated that TIMELESS (P = 0.004, HR = 1.21 [1.06, 1.38]) and RORA (P = 0.047, HR = 0.868 [0.755, 0.998]) has a significant correlation with overall survival in NSCLC. Genes related to TIMELESS were enriched in the cell cycle and immune system, and the function of RORA was mainly focused on oncogenic signaling pathways or glycosylation and protein activation. Also, TIMELESS was positively correlated with tumor mutation burden while RORA was negatively correlated with it. TIMELESS and RORA were also significantly correlated with immune checkpoint and immune infiltration levels in NSCLC. Additionally, TIMELESS showed a significant positive relationship with lipid metabolism.ConclusionsTIMELESS and RORA were identified as key clock genes in NSCLC, and were independent prognostic factors for overall survival in NSCLC. The function of them were assessed in many aspects, indicating the strong potential of the two genes to serve as biomarkers for NSCLC progression and prognosis.

Serum cholinesterase may independently predict prognosis in non-small-cell lung cancer

BackgroundSerum cholinesterase (ChE) was found to be involved in cancer initiation and progression. However, the survival association between serum ChE and non-small cell lung cancer (NSCLC) has not been extensively discussed. In the present study, we aim to elevate the role of ChE in overall survival (OS) of NSCLC patients.MethodsA total of 961 histologically confirmed NSCLC patients diagnosed between 2013 and 2018 in a provincial cancer hospital in southwestern China were retrospectively selected. Relevant information, such as histological type, clinical stage, chemotherapy, smoking status, body mass index (BMI), important serum indicators (albumin, neutrophil-to-lymphocyte ratio, ChE), date of death of the patients was extracted from the computerized hospital information system. Univariate and multivariate Cox proportional hazards models were used to determine the association between baseline serum ChE measured at the diagnosis and the OS of NSCLC patients.ResultsThe median of baseline ChE (7700 units/liter) was used as a cut-off to dichotomize NSCLC patients. After controlling for possible confounding factors, serum ChE at diagnosis was significantly associated with OS of NSCLC: patients with higher level of ChE were observed a better prognosis (hazard ratio, HR: 0.77, 95% CI: 0.67–0.93, p = 0.006). Subgroup analysis revealed significant ChE-OS association for NSCLC patients: with lower systemic inflammation level (baseline NLR < 2.95, HR: 0.71, 95% CI: 0.56–0.89, p = 0.003), of adenocarcinoma (HR: 0.66, 95% CI: 0.54–0.80, p < 0.001), in advanced stage (HR: 0.77, 95% CI: 0.66–0.92, p < 0.01), and received chemotherapy (HR: 0.75, 95% CI: 0.59–0.96, p < 0.02).ConclusionBaseline ChE may have independent prognostic value for NSCLC patients. Longitudinal studies should be performed to corroborate this finding.

Serum Albumin: Early Prognostic Marker of Benefit for Immune Checkpoint Inhibitor Monotherapy But Not Chemoimmunotherapy

Cancer cachexia exhibits decreased albumin and associates with short overall survival (OS) in patients with non-small cell lung cancer (NSCLC), but whether on-treatment albumin changes associate with OS in NSCLC patients treated with immune checkpoint inhibitors (ICIs) and combination chemoimmunotherapy has not been thoroughly evaluated. We conducted a single-center retrospective study of patients with advanced NSCLC who received first-line ICI with or without chemotherapy between 2013 and 2020. The association of pretreatment albumin and early albumin changes with OS was evaluated using Kaplan-Meier method and Cox regression models. A total of 210 patients were included: 109 in ICI cohort and 101 in ICI+Chemo cohort. Within a median of 21 days from treatment initiation, patients with ≥ 10% of albumin decrease had significantly shorter OS compared to patients without albumin decrease in ICI cohort. Pretreatment albumin and albumin decrease within the first or second cycle of treatment were significantly and independently associated with OS in ICI cohort, but not in ICI+Chemo cohort. The lack of association between albumin and OS with the addition of chemotherapy was more pronounced among patients with ≥ 1% PD-L1 expression in subgroup analysis. Pretreatment serum albumin and early albumin decrease in ICI monotherapy was significantly associated with OS in advanced NSCLC. Early albumin change, as a routine lab value tested in clinic, may be combined with established biomarkers to improve outcome predictions of ICI monotherapy. The underlying mechanism of the observed association between decreased albumin and ICI resistance warrants further investigation.

Diversity and heterogeneity of immune states in non-small cell lung cancer and small cell lung cancer

Blood-based biomarkers including systemic inflammation (SI) indicators or circulating factors (cytokines, chemokines, or growth factors) are associated with a poor prognosis for lung cancer patients. Collectively these biomarkers can predict the immune state of a patient. We wanted to define and compare the immune states of small cell and non-small cell lung cancer patients, in the hopes that the information gained could lead to overall improvements in patient care and outcomes. Specimens and data from 235 patients was utilized, 49 surgically resected non-small cell lung cancer (NSCLC) patients with no evidence of disease (DF), 135 advanced non-small cell lung cancer (NSCLC), 51 small cell lung cancer (SCLC). SI markers neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), systemic inflammation index (SII), and systemic inflammation response index (SIRI) were determined from blood counts. Forty-seven plasma cytokines were measured using a multiplex bead-based assay. Progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan-Meier and Cox Proportional Hazards models. NSCLC patients had significantly high levels of SI markers than SCLC and DF patients, while NLR, PLR and SII were also higher in SCLC than DF patients. SI optimized marker values to differentiate SI value were; 6.04 (NLR), 320 (PLR), 1615 (SII), and 7.3 (SIRI). Elevated levels NLR (p<0.001), PLR (p<0.001), and SII (p = 0.018) were associated with a worse PFS and OS in NSCLC, while none of the markers were associated with PFS in SCLC patients. NSCLC patients with a poor outcome displayed heterogeneous immune states relative to systemic inflammation and circulating IL-6 markers. These groups could be distinguished based on the cytokines IL-8, TNFα, and IL-27. We identified heterogeneity of immune states in SCLC and NSCLC patients and in NSCLC patients with the poorest prognosis. This heterogeneity could be exploited to improve outcomes for these patients.