Prognosis Of Non-small Cell Lung Cancer Research Articles

FATP2 regulates non-small cell lung cancer by mediating lipid metabolism through ACSL1

Lipid metabolism is believed to play an important role in cancer. This study aimed to investigate the role and possible mechanism of fatty acid transporter protein 2 (FATP2) in non-small cell lung cancer (NSCLC). FATP2 expression and its relationship with NSCLC prognosis were analyzed using the TCGA database. The si-RNA was used to intervene FATP2 in NSCLC cells, and the effects of si-FATP2 on cell proliferation, apoptosis, lipid deposition, endoplasmic reticulum (ER) morphology, and the proteins expressions of fatty acid metabolism and ER stress were analyzed. In addition, Co-IP analyzed the interaction between FATP2 and ACSL1, and further analyzed the possible mechanism of FATP2 in regulating lipid metabolism using pcDNA-ACSL1. Results found that FATP2 was overexpressed in NSCLC and associated with poor prognosis. Si-FATP2 significantly inhibited the proliferation and lipid metabolism of A549 and HCC827 cells, and induced ER stress to promote apoptosis. Further studies confirmed the protein interaction between FATP2 and ACSL1. Si-FATP2 and pcDNA-ACSL1 co-transfection further inhibit the proliferation and lipid deposition of NSCLS cells, and promote the decomposition of fatty acids. In conclusion, FATP2 promoted the progression of NSCLC by regulating lipid metabolism through ACSL1.

Tumor-expressed B7-H3 promotes vasculogenic mimicry formation rather than angiogenesis in non-small cell lung cancer.

Vasculogenic mimicry (VM), an alternative microvascular circulation independent ofangiogenesis, is formed by aggressive cancer cells. Tumor-expressedB7-H3 has been reported to promote VM formation in hepatocellular carcinoma and modulate angiogenesis in breast cancer and colorectal cancer. However, its effects on VM generation and angiogenesis in non-small cell Lung cancer (NSCLC) remained to be elucidated. CRISPR/Cas9-mediated B7-H3 knockout (KO)was conducted in NSCLC A549 and H3255 cells. The expression of VM-related proteins, including vascular endothelial (VE)-cadherin and matrix metalloproteinase 14 (MMP14), and the secretion of vascular endothelial growth factor (VEGF) were measured by western blotting and chemiluminescence assay in both B7-H3 KO and mock-edited A549 and H3255 cells. To examine VM formation, a three-dimensional (3D)culture model was used for B7-H3 KO and mock A549 and H3255 cells. For in vivo analysis, xenograft mice models were established using B7-H3 KO and mock-edited A549 cells, and immunohistochemical (CD31) and histochemical (periodic acid-Schiff, PAS) double staining were performed to identify VM and endothelial vessels in tumor tissues. Finally, specific signaling inhibitors were used to analyze B7-H3-induced signaling pathway responsible for VE-cadherin and MMP14 expression and VM generation. Higher expression of B7-H3 was associated with a worse prognosis and moreadvanced T-category in NSCLC. CRISPR/Cas9-mediated B7-H3 KOin A549 and H3255 cells led to decreased expression of VE-cadherin and MMP14; however,the secretion of VEGFby the two cell lines remained unchanged. In the3D cell culture model, bothB7-H3 KO A549 and H3255cells showed a significant reduction in the formation of capillary-like tubular structures compared to mock-edited cells. In thein vivo xenograft model, mock-edited A549 cells formed excessive PAS+ CD31-VM channels, while B7-H3 KO restrained VM formation in the xenograft tumors. However, no significant differenceswere found in CD31+ endothelial vessels between xenografts formed by B7-H3 KO and mock-edited A549 cells. Finally, we analyzed the signaling pathway responsible for B7-H3-induced VM formation and found that selective inhibition of the phosphoinositide 3-kinase(PI3K)/protein kinase B(AKT) hyperactivation by LY294002 was associated with decreased expression of MMP14 and VE-cadherin, and in vitro VM formation by both A549 and H3255 cells. Tumor-expressed B7-H3 acts via PI3K/AKT signaling pathway to promote VM formation by NSCLC cells while bears no effects on angiogenesis in NSCLC.

GBP5 Identifies Immuno-Hot Tumors and Predicts the Therapeutic Response to Immunotherapy in NSCLC.

Immunotherapy drugs, immune checkpoint inhibitors (ICIs), have been approved for first- and second-line treatment of non-small cell lung cancer (NSCLC), but only a portion of patients respond to ICIs. It is crucial to screen the beneficiaries of immunotherapy through biomarkers accurately. Several datasets were used to explore the predictive value for immunotherapy and immune relevance of guanylate binding protein 5 (GBP5) in NSCLC, including the GSE126044 dataset, The Cancer Genome Atlas (TCGA) dataset, Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset, the Kaplan-Meier plotter dataset, the HLuA150CS02 cohort, and the HLugS120CS01 cohort. GBP5 was upregulated in tumor tissues but associated with a good prognosis in NSCLC. Moreover, our findings demonstrated that GBP5 was strongly correlated with the expression of many immune-related genes, TIIC levels, and PD-L1 expression based on RNA-seq data onto online databases and validation of the NSCLC tissue microarray using IHC staining. Moreover, pan-cancer analysis has shown that GBP5 was a factor in identifying immuno-hot tumors, except for a few tumor types. In summary, our current research suggests that GBP5 expression is a potential biomarker for predicting the outcome of NSCLC patients treated with ICIs. More research with large-scale samples is needed to determine their value as biomarkers of ICIs benefit.

Systemic inflammation influences the prognosis of patients with radically resected non-small cell lung cancer and correlates with the immunosuppressive microenvironment.

The impact of host condition on prognosis of non-small cell lung cancer (NSCLC) and the interaction between host and NSCLC remain unclear. This study investigated the association between systemic inflammation and prognosis and characteristics of radically resected NSCLC. This study consisted of a cohort study and an exploratory study of institutional prospective databases. All participants underwent video-assisted thoracoscopic lobectomy as the primary treatment. Systemic inflammation was assessed before surgery using the advanced lung cancer inflammation index and the systemic inflammation response index. Next-generation sequencing and multiplex immunofluorescence analysis were conducted to delineate tumor characteristics. In the cohort study including 1507 participants, high inflammation was associated with poor disease-free survival and overall survival before and after propensity score matching and in multivariable analysis. Systemic inflammation showed good prognostic value for stage IA-IB NSCLC, and the prognostic value diminished with upstaging of NSCLC. In the exploratory study including 217 adenocarcinomas, tumor microenvironment of high inflammation group showed a greater abundance of PDL1+ tumor cells and immune cells, which were independent from driver gene mutations and clinicopathological characteristics. Spatial analysis demonstrated a higher frequency of immune-suppressed cellular neighborhood, increased avoidance between immune cells and PDL1- tumor cells and compromised immune killing and presentation in tumor microenvironment of high inflammation group. Systemic inflammation showed limited association with genomic mutations. Systemic inflammation may influence the prognosis of NSCLC at both the systematic level and the local immune response. The correlation between high inflammation and immunosuppressive microenvironment indicates a novel thread for anticancer treatment.

Mutant-allele dispersion correlates with prognosis risk in patients with advanced non-small cell lung cancer.

Intra-tumor heterogeneity (ITH) contributes to lung cancer progression and resistance to therapy. To evaluate ITH and determine whether it may be employed as a predictive biomarker of prognosis in patients with advanced non-small cell lung cancer (NSCLC), we used a novel algorithm called mutant-allele dispersion (MAD). In the study, 103 patients with advanced NSCLC were enrolled. Using a panel of 425 cancer-related genes, next-generation sequencing (NGS) was performed on tumor specimens that had been collected. From NGS data, we derived MAD values, and we next looked into their relationships with clinical variables and different mutation subtypes. The median MAD among 103 NSCLC patients was 0.73. EGFR mutation, tyrosine kinase inhibitor (TKI) therapy, radiotherapy, and chemotherapy cycles were all substantially correlated with the MAD score. In patients with lung adenocarcinoma (LUAD), correlation analysis revealed that the MAD score was substantially linked with Notch pathway mutation (P = 0.021). A significant relationship between high MAD and shorter progression-free survival (PFS) was found (HR = 2.004, 95%CI 1.269-3.163, P = 0.003). In patients with advanced NSCLC, histological type (P = 0.004), SMARCA4 mutation (P = 0.038), and LRP1B mutation (P = 0.006) were all independently associated with prognosis. The disease control rate was considerably greater in the low MAD group compared to the high MAD group in 19 LUAD patients receiving immunotherapy (92.9% vs. 40%, P = 0.037). TKI-PFS was longer in 37 patients with low MAD who received first-line TKI therapy (P = 0.014). Our findings suggested that MAD is a practical and simple algorithm for assessing ITH, and populations with high MAD values are more likely to have EGFR mutations. MAD can be used as a potential biomarker to predict not only the prognosis of NSCLC but also the efficacy of immunotherapy and TKI therapy in patients with advanced NSCLC.

Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial

We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478). Molecular and genomic features associated with immune phenotypes in NSCLC have not been explored in detail. We developed a machine learning (ML)-based model to classify tumors into one of three categories: inflamed, altered, and desert, based on the spatial distribution of CD8+ T cells in two prospective (n= 453; TNM-I trial) and retrospective (n= 481) stage I-IIIA NSCLC surgical cohorts. NanoString assays and targeted gene panel sequencing were used to evaluate the association of gene expression and mutations with immune phenotypes. Among the total of 934 patients, 24.4% of tumors were classified as inflamed, 51.3% as altered, and 24.3% as desert. There were significant associations between ML-derived immune phenotypes and adaptive immunity gene expression signatures. We identified a strong association of the nuclear factor-κB pathway and CD8+ T-cell exclusion through a positive enrichment in the desert phenotype. KEAP1 [odds ratio (OR) 0.27, Q= 0.02] and STK11 (OR 0.39, Q= 0.04) were significantly co-mutated in non-inflamed lung adenocarcinoma (LUAD) compared to the inflamed phenotype. In the retrospective cohort, the inflamed phenotype was an independent prognostic factor for prolonged disease-specific survival and time to recurrence (hazard ratio 0.61, P= 0.01 and 0.65, P= 0.02, respectively). ML-based immune phenotyping by spatial distribution of T cells in resected NSCLC is able to identify patients at greater risk of disease recurrence after surgical resection. LUADs with concurrent KEAP1 and STK11 mutations are enriched for altered and desert immune phenotypes.

Loss of BLK expression as a potential predictor of poor prognosis and immune checkpoint blockade response in NSCLC and contribute to tumor progression

BackgroundImmune checkpoint blockade (ICB) has been proved to have significant anti-tumor effect in the clinical treatment of non-small cell lung cancer (NSCLC). Therefore, biomarkers predicting ICB response can provide better treatment for patients with NSCLC. MethodsDifferential expression genes (DEGs) were identified by ImmuCellAI database. Copy number alteration (CNA) was analyzed by cBioPortal. The predicted efficiency of 4 genes on cancer immunotherapy was assessed by ROC analysis. The survival value of BLK was analyzed by Kaplan-Meier plotter and Prognoscan analysis. Clinical significance of BLK IHC-TMA score in NSCLC was also explored. The CCK-8 assay, wound healing assay, western blot assay in vitro and subcutaneous xenograft experiments in vivo were used for investigating the functions of BLK. The RNA-sequencing were performed to screen BLK regulated genes and conducted for GO/KEGG enrichment analysis. The transcriptional regulatory factor of BLK promoter region was predicted by ChIP-seq analysis. Results39 common DEGs between ICB Response (R) group and No Response (NR) group with NSCLC were identified, in which the CNA frequency of BLK deletion (> 6%) was found. The predicted efficiency of BLK on immunotherapy was performed best in NSCLC (AUC>0.7). Low expression of BLK was related to NSCLC with significantly poor prognosis. BLK overexpression can inhibit growth of NSCLC via activating apoptosis pathway, inhibiting the G2M checkpoint and Glycolysis pathway. The enrichment analysis indicated that BLK regulated genes related to oncogenic potential in NSCLC. Besides, BLK expression was inhibited via H3K27me3 modification in A549 and H1299 cells. BLK mRNA level was negatively correlated with methylation and positively correlated with the tumor purity in NSCLC. ConclusionOur study provides strong evidence that low expression of BLK may serve as a biomarker for poor prognosis in NSCLC, while response to ICB therapy and contributes to NSCLC tumor progression.

Prognostic predictors of radical resection of stage I-IIIB non-small cell lung cancer: the role of preoperative CT texture features, conventional imaging features, and clinical features in a retrospectively analyzed

BackgroundTo investigate the value of preoperative computed tomography (CT) texture features, routine imaging features, and clinical features in the prognosis of non-small cell lung cancer (NSCLC) after radical resection.MethodsDemographic parameters and clinically features were analyzed in 107 patients with stage I-IIIB NSCLC, while 73 of these patients received CT scanning and radiomic characteristics for prognosis assessment. Texture analysis features include histogram, gray size area matrix and gray co-occurrence matrix features. The clinical risk features were identified using univariate and multivariate logistic analyses. By incorporating the radiomics score (Rad-score) and clinical risk features with multivariate cox regression, a combined nomogram was built. The nomogram performance was assessed by its calibration, clinical usefulness and Harrell’s concordance index (C-index). The 5-year OS between the dichotomized subgroups was compared using Kaplan–Meier (KM) analysis and the log-rank test.ResultsConsisting of 4 selected features, the radiomics signature showed a favorable discriminative performance for prognosis, with an AUC of 0.91 (95% CI: 0.84 ~ 0.97). The nomogram, consisting of the radiomics signature, N stage, and tumor size, showed good calibration. The nomogram also exhibited prognostic ability with a C-index of 0.91 (95% CI, 0.86–0.95) for OS. The decision curve analysis indicated that the nomogram was clinically useful. According to the KM survival curves, the low-risk group had higher 5-year survival rate compared to high-risk.ConclusionThe as developed nomogram, combining with preoperative radiomics evidence, N stage, and tumor size, has potential to preoperatively predict the prognosis of NSCLC with a high accuracy and could assist to treatment for the NSCLC patients in the clinic.

HnRNPAB is an independent prognostic factor in non‑small cell lung cancer and is involved in cell proliferation and metastasis.

Heterogeneous nuclear ribonucleoprotein A/B (hnRNPAB) is an RNA binding protein that is closely associated with the biological function and metabolism of RNA, which is involved in the malignant transformation of various tumor cells. However, the role and mechanisms of hnRNPAB in non-small cell lung cancer (NSCLC) are still unclear. In the present study, the expression levels of hnRNPAB in NSCLC and normal tissues were analyzed using the human protein atlas database and UALCAN database. The clinical significance of hnRNPAB was assayed using the data of NSCLC cases from The Cancer Genome Atlas database. Subsequently, two stable NSCLC cell lines with hnRNPAB knockdown were constructed and the effects of hnRNPAB silencing on cell viability, migration, invasion and epithelial-mesenchymal transition (EMT) were identified. Genes associated with hnRNPAB expression in NSCLC were screened using the Linked Omics database and verified by quantitative real-time PCR (RT-qPCR). The database analysis indicated that hnRNPAB was mainly expressed in the nucleus of NSCLC cells. Compared with the normal tissues, hnRNPAB expression was overexpressed in NSCLC tissues and was closely associated with the overall survival, sex, tumor-node-metastases classification, and poor prognosis of patients with lung adenocarcinoma. Functionally, knockdown of hnRNPAB inhibited the proliferation, migration, invasion and EMT of NSCLC cells and arrested the cell cycle at G1 phase. Mechanistically, the bioinformatics analysis and RT-qPCR verification demonstrated that hnRNPAB knockdown led to a significant expression change of genes associated with tumorigenesis. In conclusion, the present study indicated that hnRNPAB played an important role in the malignant transformation of NSCLC, supporting the significance of hnRNPAB as a novel potential therapeutic target for the early diagnosis and prognosis of NSCLC.

CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients.

This study aimed to elucidate a computed tomography (CT) image-based biopsy with a radiogenomic signature to predict homeodomain-only protein homeobox (HOPX) gene expression status and prognosis in patients with non-small cell lung cancer (NSCLC). Patients were labeled as HOPX-negative or positive based on HOPX expression and were separated into training (n = 92) and testing (n = 24) datasets. In correlation analysis between genes and image features extracted by Pyradiomics for 116 patients, eight significant features associated with HOPX expression were selected as radiogenomic signature candidates from the 1218 image features. The final signature was constructed from eight candidates using the least absolute shrinkage and selection operator. An imaging biopsy model with radiogenomic signature was built by a stacking ensemble learning model to predict HOPX expression status and prognosis. The model exhibited predictive power for HOPX expression with an area under the receiver operating characteristic curve of 0.873 and prognostic power in Kaplan-Meier curves (p = 0.0066) in the test dataset. This study's findings implied that the CT image-based biopsy with a radiogenomic signature could aid physicians in predicting HOPX expression status and prognosis in NSCLC.

PLCG2 can exist in eccDNA and contribute to the metastasis of non-small cell lung cancer by regulating mitochondrial respiration

Extrachromosomal circular DNAs (eccDNAs) participate in tumorigenesis and tumor progression. However, the role and mechanism of eccDNAs have yet to be elucidated in non-small cell lung cancer (NSCLC). In our research, three surgically matched NSCLC tissue samples, NSCLC cell lines (H1299, A549, and H460), and a normal lung cell line (MRC-5) were used as study objects. High-throughput eccDNA sequencing and bioinformatics analysis were performed to study the distribution pattern and level of eccDNA expression. The upregulated candidate eccDNA-encoding PLCG2 was validated by routine PCR. Plasmid transfection, RNA interference, qRT‒PCR and western blotting experiments were used to verify the expression level of PLCG2. Our results showed that the chromosome distribution, length distribution, and genomic annotation of the eccDNAs were comparable between the NSCLC and normal groups. Nevertheless, there were no significant differences in eccDNAs between NSCLC tissues and matched normal lung tissues. The eccDNA derived from PLCG2 was upregulated in NSCLC cells. TCGA analysis and immunohistochemistry showed that PLCG2 was highly expressed in lung cancer tissues and tended to be associated with poor outcome. We also demonstrated that PLCG2 can promote metastasis through the regulation of mitochondrial respiration. These results suggested that PLCG2 identified by eccDNA sequencing acts as an oncogene and might be a new biomarker for NSCLC diagnosis and prognosis evaluation.

Abstract 5373: Autoencoder-based multimodal prediction of survival for non-small cell lung cancer

Abstract Most non-small cell lung cancer (NSCLC) prognosis prediction approaches use one data type and do not take advantage of the large amount of multimodal data available. To evaluate and explore the benefits of multimodal data integration, we present a combined feature selection and denoising autoencoder pipeline for NSCLC survival prediction and survival subtype identification using microRNA (miRNA), mRNA, DNA methylation, long non-coding RNA (lncRNA) and clinical data. Survival performance for both lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients was compared across modality combinations, data integration time and training data types. Multimodal data combinations outperformed single data modalities, with the early integration of all data modalities achieving concordance indexes (C-indexes) of 0.67 (±.04) and 0.63 (±.02) for LUAD and LUSC, respectively versus corresponding C-index of 0.64 (±.02) and 0.59 (±.03) for the best single cell modality (clinical). Notably, combining just lncRNA and clinical data facilitated effective survival discrimination, with C-indexes of 0.69 (±.03) for LUAD and 0.62 (±.03) for LUSC. Overall, higher performance was achieved by using a single denoising autoencoder for all biological data (early integration) and by training on both LUSC and LUAD patient data together. Two survival subtypes (log rank test p-value=1e-9) were identified, with 991 differentially expressed transcripts in the poorer survival group. Our analysis shows the value of multimodal data integration for predicting NSCLC progression, with especially good performance using the combination of lncRNA and clinical data. Early integration of biological data, with an initial linear feature selection technique and a denoising autoencoder for dimensionality reduction, showed effective survival performance and survival subtype identification. Further research is underway to expand analysis to different cancer types and data modalities and extract more biological interpretability from autoencoder models. Citation Format: Jacob G. Ellen, Etai Jacob, Nikos Nikolaou, Natasha Markuzon. Autoencoder-based multimodal prediction of survival for non-small cell lung cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5373.

Abstract 918: Real-world clinical characteristics and treatment (tx) outcomes by co-mutation status in patients (pts) with KRAS G12C-mutated non-small cell lung cancer (NSCLC)

Abstract Background: KRAS mutations occur in ~30% of NSCLC and KRAS G12C is the most common subtype (~40%). Co-mutations can impact NSCLC prognosis and tx response. Methods: This retrospective study used the nationwide (US-based) de-identified Flatiron Health-Foundation Medicine clinico-genomic database (FH-FMI CGDB; January 1, 2011 to March 31, 2022). De-identified data originated from ~280 US cancer clinics (~800 sites of care). This study is based on tissue-based assay (FoundationOne CDx). Pts were aged ≥18 years; had KRAS G12C-mutated, advanced/metastatic NSCLC, and had received ≥1 line of tx, with 1L tx initiated after October 1, 2016. Pts were categorized by presence (m) or absence (wt) of single co-mutations in STK11, KEAP1, or TP53; pts with EGFR, ALK, ROS1, BRAF, MET, or NTRK alterations were excluded. Key endpoints included overall survival (OS) and real-world progression-free survival (rwPFS). Statistical methods included a univariate Cox hazard model. Results: Among 847 pts with KRAS G12C-mutated NSCLC, co-mutations of STK11 were seen in 24%, KEAP1 in 14%, and TP53 in 51%. Based on non-missing records (82%), low tumor mutational burden (<10 mut/Mb) was more frequent in the STK11m vs wt group (72% vs 55%; p<0.001), not significantly different in the KEAP1m vs wt group (53% vs 60%; p=0.145), and less frequent in pts with TP53m vs wt (50% vs 69%; p<0.001). Negative PD-L1 status (<1%) was more frequent in pts with STK11 (50% vs 18%; p<0.001) or KEAP1 (41% vs 23%; p<0.001) mutations, and less frequent in pts with TP53 mutations (15% vs 37%; p<0.001). STK11 or KEAP1 co-mutations were associated with shorter rwPFS and OS, particularly in pts treated with immunotherapy (IO)-based 1L tx (Table 1). TP53 status did not significantly impact pt outcomes in the 1L setting. Conclusions: In this real-world analysis, STK11 or KEAP1 co-mutations were associated with PD-L1-negative tumors and poor outcomes with IO-based tx in KRAS G12C-mutated NSCLC. Table 1. rwPFS and OS by co-mutation status, and OS by 1L treatment and co-mutation status rwPFS and OS by co-mutation status rwPFS All (N=846) STK11m (n=206) STK11wt (n=640) KEAP1m (n=121) KEAP1wt (n=725) TP53m (n=429) TP53wt (n=417) Median, months (95% CI) 5.0 (4.5-5.7) 4.0 (3.1-4.9) 5.6 (4.9-6.2) 3.8 (2.7-4.8) 5.6 (4.7-6.2) 5.3 (4.6-6.1) 4.7 (4.0-5.7) HR (95% CI); p value - 1.38 (1.16-1.64); <0.001 1.46 (1.18-1.80); <0.001 0.87 (0.75-1.01); 0.07 OS All (N=847) STK11m (n=206) STK11wt (n=641) KEAP1m (n=121) KEAP1wt (n=726) TP53m (n=429) TP53wt (n=418) Median, months (95% CI) 11.9 (10.2-14.3) 9.4 (7.2-13.8) 12.4 (10.8-15.0) 7.6 (5.4-9.4) 13.5 (11.0-15.1) 11.0 (9.2-14.4) 12.7 (10.4-15.3) HR (95% CI); p value - 1.40 (1.15-1.70); <0.001 1.59 (1.26-2.00); <0.001 0.98 (0.83-1.17); 0.85 OS by 1L treatment and co-mutation status IO + chemotherapy STK11m (n=106) STK11wt (n=228) KEAP1m (n=53) KEAP1wt (n=281) TP53m (n=162) TP53wt (n=172) Median, months (95% CI) 6.9 (4.5-9.4) 11.3 (9.0-14.8) 7.9 (4.5-10.3) 10.2 (7.7-12.9) 9.3 (6.6-11.9) 10.9 (7.2-14.0) HR (95% CI); p value 1.75 (1.33-2.31); <0.0001 1.60 (1.13-2.26); 0.01 1.04 (0.80-1.36); 0.75 IO monotherapy STK11m (n=33) STK11wt (n=181) KEAP1m (n=29) KEAP1wt (n=185) TP53m (n=111) TP53wt (n=103) Median, months (95% CI) 9.7 (2.8-16.9) 16.1 (10.7-23.3) 5.7 (3.9-14.3) 16.6 (10.8-23.0) 15.1 (9.1-22.4) 14.6 (8.9-23.0) HR (95% CI); p value 1.54 (0.97-2.46); 0.07 1.81 (1.12-2.90); 0.01 0.96 (0.68-1.37); 0.83 Chemotherapy only STK11m (n=56) STK11wt (n=188) KEAP1m (n=28) KEAP1wt (n=216) TP53m (n=130) TP53wt (n=114) Median, months (95% CI) 18.6 (10.2-22.8) 13.5 (9.5-15.3) 7.8 (5.0-17.4) 14.5 (11.1-16.3) 12.0 (8.9-16.0) 15.0 (11.1-19.5) HR (95% CI); p value 0.84 (0.57-1.25); 0.40 1.31 (0.82-2.10); 0.26 1.12 (0.81-1.54); 0.49 CI, confidence interval; HR, hazard ratio. Citation Format: Marcelo V. Negrao, Wen-Hsing Wu, Colin R. Lindsay, Rafael Caparica, Vincent Prêtre, Yehrim Kang, Nydia Caro, Anna Farago, Fen Ye, Gilberto de Castro Jr. Real-world clinical characteristics and treatment (tx) outcomes by co-mutation status in patients (pts) with KRAS G12C-mutated non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 918.

Nomogram Based on Preoperative Absolute Lymphocyte Count to Predict Local Recurrence in Patients with Non-Small Cell Lung Cancer After Microwave Ablation.

This study aims to investigate the prognostic value of preoperative absolute lymphocyte count (preALC) for non-small cell lung cancer (NSCLC) after microwave ablation (MWA) and build a combined nomograph with clinical features to predict the local recurrence. A total of 118 NSCLC patients who underwent microwave ablation were enrolled in this study. The median local recurrence-free survival (LRFS) was 35.5 months. Independent prognostic factors obtained by multivariate analysis were included in the prediction model. The prognostic value of the model was assessed by the area under the time-dependent receiver operating characteristic curve (T-AUC). Histological subtype and preALC were independent risk factors for local relapse-free survival. According to the time-dependent receiver operating characteristic curve (T-ROC), the optimal cut-off value of preALC was 1.965×109/L, the sensitivity was 0.837, and the specificity was 0.594. The area under the T-ROC curve (AUC) of preALC was 0.703. To establish a nomogram to predict the local recurrence rate of NSCLC after MWA based on the prognostic factors revealed by Cox regression. Preoperative lymphocyte count reduction is associated with poor prognosis of NSCLC. The nomogram model combined with preALC can provide a good individualized prediction of local recurrence after microwave ablation.

HNRNPD is a prognostic biomarker in non-small cell lung cancer and affects tumor growth and metastasis via the PI3K-AKT pathway

ABSTRACT Heterogeneous nuclear ribonucleoprotein D (HNRNPD) can regulate expression of key proteins in various cancers. However, the prognostic predictive value and biology function of HNRNPD in non-small cell lung cancer (NSCLC) is unknown. First, we used the TCGA and GEO datasets to determine that HNRNPD predicts the prognosis of NSCLC patients. Following that, we knocked down HNRNPD in NSCLC cell lines in vitro and validated its biological function using CCK-8, transwell assays, wound healing tests, and Western blotting. Finally, we constructed tissue microarrays (TMAs) from 174 NSCLC patients and verified our findings using immunohistochemistry staining for HNRNPD from public databases. In both the public datasets, NSCLC tissues with elevated HNRNPD expression had shorter overall survival (OS). In addition, HNRNPD knockdown NSCLC cell lines showed significantly reduced proliferation, invasion, and metastatic capacity via the PI3K-AKT pathway. Finally, elevated HNRNPD expression in NSCLC TMAs was linked to a poorer prognosis and decreased PD-L1 expression levels. HNRNPD is associated with a poorer prognosis in NSCLC and affects tumor growth and metastasis via the PI3K-AKT pathway.

Role of ALOX5 in non-small cell lung cancer: A potential therapeutic target associated with immune cell infiltration.

The efficacy of immunotherapy for lung cancer is closely related to immune cell infiltration. Arachidonic acid 5-lipoxygenase (ALOX5) can activate inflammatory responses and trigger various cell death patterns; however, the relevance of ALOX5 to immune cell infiltration in lung cancer is unclear. The expression of ALOX5 in non-small cell lung cancer (NSCLC) is analyzed using an online database to explore the correlation between ALOX5 and immune cell infiltration in NSCLC and its relationship with prognosis. Differences in ALOX5 expression in NSCLC and normal lung tissues were analyzed by online databases such as TIMER, GEPIA and HPA; the UALCAN database was used to reveal the relationship between ALOX5 and clinical features; Kaplan-Meier database was applied to explore the prognostic value of ALOX5; GeneMANIA and String Website was used to explore genes and proteins associated with ALOX5 expression, respectively; the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to analyze ALOX5 differential genes which were picked up through the TCGA database; GSEA software was applied to predict the signal pathways that ALOX5 may be involved in; and the TIMER database was used to analyze the effect of ALOX5 expression on the level of immune cell infiltration. Compared with the normal lung tissues, the ALOX5 expression was low in NSCLC tissues (P<0.05), and which affected the prognosis of lung cancer patients. The expression level of ALOX5 was related to clinical features such as sex, age, metastasis, and pathological staging in NSCLC patients (all P<0.05). The gene interaction network analysis found that the genes interacting with ALOX5 mainly included the genes related to lipid oxidation and pro-inflammatory mediators such as coactosin like protein 1 (COTL1), leukotriene C4 synthase (LTC4S), and prostaglandin endoperoxide synthase 2 (PTGS2), and the protein-protein interaction analysis results were consistent. GO and KEGG analysis found that ALOX5 was involved in the biological process of activation of immune cell function and was involved in immune response function pathways. The GSEA analysis showed that ALOX5 may activate immune responses and mediate immune-related prognosis by affecting the cytokine-cytokine receptor interactions, natural killer-mediated cytotoxicity, and T cell receptor signaling pathways. The ALOX5 mRNA expressions in lung adenocarcinoma and lung squamous cell carcinoma were positively correlated with the tumor infiltration immune cells (B cells, CD8+ T cells, CD4+ T cells, etc.) (all P<0.05), and the ALOX5 mRNA expression was positively correlated with the expression of classic T cell immune checkpoint inhibitor genes (P<0.001). The ALOX5 gene expression in NSCLC is significantly downregulated, and which can affect NSCLC prognosis and immune cell infiltration levels. ALOX5 gene may be a potential biomarker of NSCLC prognosis associated with immune cell infiltration.

Ratiometric electrochemical OR gate assay for NSCLC-derived exosomes

Non-small cell lung cancer (NSCLC) is the most common pathological type of LC and ranks as the leading cause of cancer deaths. Circulating exosomes have emerged as a valuable biomarker for the diagnosis of NSCLC, while the performance of current electrochemical assays for exosome detection is constrained by unsatisfactory sensitivity and specificity. Here we integrated a ratiometric biosensor with an OR logic gate to form an assay for surface protein profiling of exosomes from clinical serum samples. By using the specific aptamers for recognition of clinically validated biomarkers (EpCAM and CEA), the assay enabled ultrasensitive detection of trace levels of NSCLC-derived exosomes in complex serum samples (15.1 particles μL−1 within a linear range of 102–108 particles μL−1). The assay outperformed the analysis of six serum biomarkers for the accurate diagnosis, staging, and prognosis of NSCLC, displaying a diagnostic sensitivity of 93.3% even at an early stage (Stage I). The assay provides an advanced tool for exosome quantification and facilitates exosome-based liquid biopsies for cancer management in clinics.Graphical

Differentiation-related genes in tumor-associated macrophages as potential prognostic biomarkers in non-small cell lung cancer.

The purpose of this study was to evaluate the role of differentiation-related genes (DRGs) in tumor-associated macrophages (TAMs) in non-small cell lung cancer (NSCLC). Single cell RNA-seq (scRNA-seq) data from GEO and bulk RNA-seq data from TCGA were analyzed to identify DRGs using trajectory method. Functional gene analysis was carried out by GO/KEGG enrichment analysis. The mRNA and protein expression in human tissue were analyzed by HPA and GEPIA databases. To investigate the prognostic value of these genes, three risk score (RS) models in different pathological types of NSCLC were generated and predicted NSCLC prognosis in datasets from TCGA, UCSC and GEO databases. 1,738 DRGs were identified through trajectory analysis. GO/KEGG analysis showed that these genes were predominantly related to myeloid leukocyte activation and leukocyte migration. 13 DRGs (C1QB, CCL4, CD14, CD84, FGL2, MS4A6A, NLRP3, PLEK, RNASE6, SAMSN1, SPN, TMEM176B, ZEB2) related to prognosis were obtained through univariate Cox analysis and Lasso regression. C1QB, CD84, FGL2, MS4A6A, NLRP3, PLEK, SAMSN1, SPN, and ZEB2 were downregulated in NSCLC compared to non-cancer tissue. The mRNA of 13 genes were significantly expressed in pulmonary macrophages with strong cell specificity. Meanwhile, immunohistochemical staining showed that C1QB, CCL4, SPN, CD14, NLRP3, SAMSN1, MS4A6A, TMEM176B were expressed in different degrees in lung cancer tissues. ZEB2 (HR=1.4, P<0.05) and CD14 (HR=1.6, P<0.05) expression were associated with a worse prognosis in lung squamous cell carcinoma; ZEB2 (HR=0.64, P<0.05), CD84 (HR=0.65, P<0.05), PLEK (HR=0.71, P<0.05) and FGL2 (HR=0.61, P<0.05) expression were associated with a better prognosis in lung adenocarcinoma. Three RS models based on 13 DRGs both showed that the high RS was significantly associated with poor prognosis in different pathological types of NSCLC. This study highlights the prognostic value of DRGs in TAMs in NSCLC patients, providing novel insights for the development of therapeutic and prognostic targets based on TAM functional differences.

Precision Surgery in NSCLC

Non-small cell lung cancer (NSCLC) is still one of the leading causes of death worldwide. This is mostly because the majority of lung cancers are discovered in advanced stages. In the era of conventional chemotherapy, the prognosis of advanced NSCLC was grim. Important results have been reported in thoracic oncology since the discovery of new molecular alterations and of the role of the immune system. The advent of new therapies has radically changed the approach to lung cancer for a subset of patients with advanced NSCLC, and the concept of incurable disease is still changing. In this setting, surgery seems to have developed a role of rescue therapy for some patients. In precision surgery, the decision to perform surgical procedures is tailored to the individual patient; taking into consideration not only clinical stage, but also clinical and molecular features. Multimodality treatments incorporating surgery, immune checkpoint inhibitors, or targeted agents are feasible in high volume centers with good results in terms of pathologic response and patient morbidity. Thanks to a better understanding of tumor biology, precision thoracic surgery will facilitate optimal and individualized patient selection and treatment, with the goal of improving the outcomes of patients affected by NSCLC.

A prognostic model based on ferroptosis-related long non-coding RNA signatures and immunotherapy responses for non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) ranks high in the incidence of malignant tumors, with limited treatment options and poor prognosis. Ferroptosis is a newly discovered cell death mechanism based on iron and reactive oxygen species (ROS). The role of ferroptosis-related long non-coding RNAs (lncRNAs) and associated prognostic mechanisms in NSCLC require investigation. We constructed a prognostic multi-lncRNA signature based on ferroptosis-related differentially expressed lncRNAs in NSCLC. The levels of ferroptosis-related lncRNA in normal lung cells and lung adenocarcinoma cells were verified by RT-PCR. We identified eight differentially expressed lncRNAs associated with NSCLC prognosis. The expression of AC125807.2, AL365181.3, AL606489.1, LINC02320, and AC099850.3 was upregulated, while SALRNA1, AC026355.1, and AP002360.1 were downregulated in NSCLC cell lines. Kaplan-Meier analysis showed that a high-risk patient group was associated with poor NSCLC prognosis. A risk assessment model based on ferroptosis-related lncRNAs was superior to NSCLC prognosis based on traditional clinicopathological features. Gene Set Enrichment Analysis (GSEA) identified immune- and tumor-related pathways in low-risk group patients. In addition, The Cancer Genome Atlas (TCGA) showed that T cell function during APC co-inhibition, APC co-stimulation, chemokine receptor (CCR), MHC class I, parainflammation, T cell co-inhibition, and check-point expression differed significantly between low- and high-risk groups. M6A-related mRNA comparisons between these groups also revealed significant differences in ZC3H13, RBM15, and METTL3 expression. Our new model of lncRNA-associated ferroptosis effectively predicted NSCLC prognoses.