Immunogenomic features of radiologically distinctive nodules in multiple primary lung cancer.

OBJECTIVES: To provide molecular and immunological attributes mechanistic insights for the management of radiologically distinctive multiple primary lung cancer (MPLC). METHODS: The Bulk RNA-seq data of MPLC were obtained from our center. The Bulk RNA-seq data and CT images of patients with single primary lung cancer (SPLC) were obtained from GSE103584. Immune infiltration algorithms were performed to investigate the disparities in the immunological microenvironment between the two groups. Single-cell gene analysis was used to explore immune cells composition and communication relationships between cells in MPLC. RESULTS: In MPLC, 11 pure ground-glass opacity nodules (pGGN) and 10 mixed GGN (mGGN) were identified, while in SPLC, the numbers were 18 pGGN and 22 mGGN, respectively. In MPLC, compared to pGGN, mGGN demonstrated a significantly elevated infiltration of CD8+ T cells. Single-cell gene analysis demonstrated that CD8+ T cells play a central role in the signaling among immune cells in MPLC. The transcription factors including MAFG, RUNX3, and TBX21 may play pivotal roles in regulation of CD8+ T cells. Notably, compared to SPLC nodules for both mGGN and pGGN, MPLC nodules demonstrated a significantly elevated degree of tumor-infiltrating immune cells, with this difference being particularly pronounced in mGGN. There was a positive correlation between the proportion of immune cells and consolidation/tumor ratio (CTR). CONCLUSIONS: Our findings provided a comprehensive description about the difference in the immune microenvironment between pGGN and mGGN in early-stage MPLC, as well as between MPLC and SPLC for both mGGN and pGGN. The findings may provide evidence for the design of immunotherapeutic strategies for MPLC.

Exploring the relationship between the interleukin family and lung adenocarcinoma through Mendelian randomization and RNA sequencing analysis.

BACKGROUND: Lung adenocarcinoma (LUAD) is still one of the most prevalent malignancies. Interleukin factors are closely associated with the initiation and progression of cancer. However, the relationship between interleukin factors and LUAD has not been fully elucidated. This study aimed to use Mendelian randomization (MR) and RNA sequencing (RNA-seq) analyses to identify the interleukin factors associated with the onset and progression of LUAD. METHODS: Exposure-related instrumental variables were selected from interleukin factor summary datasets. The LUAD summary dataset from FINGENE served as the outcome. MR and sensitivity analyses were conducted to screen for interleukin factors associated with LUAD occurrence. Transcriptome analyses revealed the role of interleukin factors in lung tissues. The results were validated through Western blotting and further confirmed with driver gene-negative patients from multiple centers. Potential mechanisms influencing LUAD occurrence and development were explored using bulk RNA-seq and single-cell RNA-seq data. RESULTS: MR analysis indicated that elevated plasma levels of IL6RB, IL27RA, IL22RA1, and IL16 are causally associated with increased LUAD risk, while IL18R1 and IL11RA exhibit the opposite effect. Transcriptome analyses revealed that IL11RA, IL18R1, and IL16 were downregulated in tumor tissues compared with normal lung tissue, but only higher expression of IL11RA correlated with improved prognosis in patients with LUAD from different centers and persisted even in driver-gene negative patients. The IL11RA protein level was lower in various LUAD cell lines than in human bronchial epithelial cells. The genes co-expressed with IL11RA were enriched in the Ras signaling pathway and glycosylation processes. Fibroblasts were the primary IL11RA-expressing cell population, with IL11RA+fibroblasts exhibiting a more immature state. The genes differentially expressed between IL11RA+and IL11RA- fibroblasts were involved in the PI3K-Akt/TNF signaling pathway. CONCLUSION: According to the MR and transcriptome analyses, the downregulation of IL11RA was closely related to the occurrence and development of LUAD.

Hypothyroidism reduces the risk of lung cancer through oxidative stress response and the PI3K/Akt signaling pathway: An RNA-seq and Mendelian randomization study.

Hypothyroidism has been suggested to play a role in tumor progression. However, the causal association between hypothyroidism and lung cancer remains unknow. To elucidate the potential association between hypothyroidism and lung cancer risk, we employ a Mendelian randomization (MR) approach. MR was performed to analyze pooled data from the International Lung Cancer Consortium (11,348 cases and 15,861 controls; European ancestry) to determine the causal relationship between hypothyroidism and lung cancer. We used 36, 83, and 14 single nucleotide polymorphisms as instrumental variables for hypothyroidism/myxoedema, hypothyroidism, and exercise, respectively. We further investigated the mechanisms involved in transcriptome analysis using data from The Cancer Genome Atlas and Genotype-Tissue Expression database. We conducted an initial validation of intermediary factor using a two-step MR analysis. Genetically predicted hypothyroidism was significantly related to the risk of overall lung cancer, specifically the risk of lung squamous cell cancer (LSCC) but not with the risk of lung adenocarcinoma (LUAD) as assessed using the inverse-variance weighted (IVM) method. A similar causal association was found between hypothyroidism/myxoedema and the risk of lung cancer, LSCC, and LUAD. Transcriptome analysis showed that genes associated with hypothyroidism, lung cancer, and LSCC were enriched in the PI3K/Akt signaling pathway and oxidative stress response. However, genes related to hypothyroidism and LUAD did not exhibit enrichment in these pathways. Hypothyroidism was significantly associated with strenuous sports or other exercises. Moreover, genetically predicted exercise was significantly related to the risk of overall lung cancer, and LSCC, but not LUAD. We detected no horizontal pleiotropy using the MR-PRESSO and MR Egger regression intercept. Hypothyroidism was causally associated with a lower risk of lung cancer, and these effects might be mediated by the oxidative stress response and the PI3K/Akt signaling pathway. Therefore, our study suggests that the potential factors and viable etiologies of hypothyroidism that contributed to lung cancer risk deserve further investigation.

Macrophage migration inhibitory factor may contribute to the occurrence of multiple primary lung adenocarcinomas.

BACKGROUND: This study aimed to identify the key genes involved in the development of multiple primary lung cancers. METHODS: Differential expression analysis was performed, followed by comparing the infiltration levels of 22 immune cell types between multiple and single primary lung adenocarcinomas. Marker genes for epithelial cells with different proportions between the two types of lung adenocarcinomas were identified. The common genes between the marker genes and differentially expressed genes were identified. Finally, the effects of the key genes were tested on the in vitro proliferation, migration and morphology. RESULTS: The infiltration levels of helper follicular T cells, resting NK cells, activated NK cells, M2 macrophages and resting mast cells were higher in the patients with multiple than in those with single primary lung adenocarcinomas. A total of 1553 differentially expressed genes and 4414 marker genes of epithelial cells were identified. Logistic regression analysis was performed on the 164 resulting genes. The macrophage migration inhibitory factor expression was positively associated with the occurrence of multiple primary lung adenocarcinomas. Moreover, its signalling pathway was the key pathway among the epithelial cells and multiple and single primary lung adenocarcinoma cells, and it was upregulated in lung adenocarcinoma cells. It also increased the expression of lung cancer markers, including NES and CA125, induced morphological changes in alveolar epithelial type II cells, and promoted their proliferation, migration and invasion. CONCLUSIONS: Multiple and single primary lung adenocarcinomas have different tumour immune microenvironments, and migration inhibitory factor may be a key factor in the occurrence of multiple primary lung adenocarcinomas.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

A radiomics nomogram prediction for survival of patients with "driver gene-negative" lung adenocarcinomas (LUAD).

BACKGROUND: To study the role of computed tomography (CT)-derived radiomics features and clinical characteristics on the prognosis of "driver gene-negative" lung adenocarcinoma (LUAD) and to explore the potential molecular biological which may be helpful for patients' individual postoperative care. METHODS: A total of 180 patients with stage I-III "driver gene-negative" LUAD in the First Affiliated Hospital of Sun Yat-Sen University from September 2003 to June 2015 were retrospectively collected. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was used to screen radiomics features and calculated the Rad-score. The prediction performance of the nomogram model based on radiomics features and clinical characteristics was validated and then assessed with respect to calibration. Gene set enrichment analysis (GSEA) was used to explore the relevant biological pathways. RESULTS: The radiomics and the clinicopathological characteristics were combined to construct a nomogram resulted in better performance for the estimation of OS (C-index: 0.815; 95% confidence interval [CI]: 0.756-0.874) than the clinicopathological nomogram (C-index: 0.765; 95% CI: 0.692-0.837). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinicopathological nomogram. The clinical prognostic risk score of each patient was calculated based on the radiomics nomogram and divided by X-tile into high-risk (> 65.28) and low-risk (≤ 65.28) groups. GSEA results showed that the low-risk score group was directly related to amino acid metabolism, and the high-risk score group was related to immune and metabolism pathways. CONCLUSIONS: The radiomics nomogram was promising to predict the prognosis of patients with "driver gene-negative" LUAD. The metabolism and immune-related pathways may provide new treatment orientation for this genetically unique subset of patients, which may serve as a potential tool to guide individual postoperative care for those patients.

Methylation of N6 adenosine-related long noncoding RNA: effects on prognosis and treatment in 'driver-gene-negative' lung adenocarcinoma.

The improvement of treatment for patients with 'driver-gene-negative' lung adenocarcinoma (LUAD) remains a critical problem to be solved. We aimed to explore the role of methylation of N6 adenosine (m6A)-related long noncoding RNA (lncRNA) in stratifying 'driver-gene-negative' LUAD risk. Patients negative for mutations in EGFR, KRAS, BRAF, HER2, MET, ALK, RET, and ROS1 were identified as 'driver-gene-negative' cases. RNA sequencing was performed in 46 paired tumors and adjacent normal tissues from patients with 'driver-gene-negative' LUAD. Twenty-three m6A regulators and relevant lncRNAs were identified using Pearson's correlation analysis. K-means cluster analysis was used to stratify patients, and a prognostic nomogram was developed. The CIBERSORT and pRRophetic algorithms were employed to quantify the immune microenvironment and chemosensitivity. We identified two clusters highly consistent with the prognosis based on their unique expression profiles for 46 m6AlncRNAs. A risk model constructed from nine m6A lncRNAs could stratify patients into high- and low-risk groups with promising predictive power (C-index = 0.824), and the risk score was an independent prognostic factor. The clusters and risk models were closely related to immune characteristics and chemosensitivity. Additional pan-cancer analysis using the nine m6AlncRNAs showed that the expression of DIO3 opposite strand upstream RNA (DIO3OS) is closely related to the immune/stromal score and tumor stemness in a variety of cancers. Our results show that m6AlncRNAs are a reliable prognostic tool and can aid treatment decision-making in 'driver-gene-negative' LUAD. DIO3OS is associated with the development of various cancers and has potential clinical applications.

Oxidative stress genes in patients with esophageal squamous cell carcinoma: construction of a novel prognostic signature and characterization of tumor microenvironment infiltration.

BACKGROUND: Oxidative stress plays an important role in the progression of various types of tumors. However, its role in esophageal squamous cell carcinoma (ESCC) has seldom been explored. This study aimed to discover prognostic markers associated with oxidative stress in ESCC to improve the prediction of prognosis and help in the selection of effective immunotherapy for patients. RESULTS: A consensus cluster was constructed using 14 prognostic differentially expressed oxidative stress-related genes (DEOSGs) that were remarkably related to the prognosis of patients with ESCC. The infiltration levels of neutrophils, plasma cells, and activated mast cells, along with immune score, stromal score, and estimated score, were higher in cluster 1 than in cluster 2. A prognostic signature based on 10 prognostic DEOSGs was devised that could evaluate the prognosis of patients with ESCC. Calculated risk score proved to be an independent clinical prognostic factor in the training, testing, and entire sets. P53 signaling pathway was highly enriched in the high-risk group. The calculated risk score was positively related to the infiltration levels of resting mast cells, memory B cells, and activated natural killer (NK) cells and negatively associated with the infiltration levels of M1 and M2 macrophages. The relationship between clinical characteristics and risk score has not been certified. The half-maximal inhibitory concentration (IC50) values for sorafenib and gefitinib were lower for patients in the low-risk group. CONCLUSION: Our prognostic signature based on 10 prognostic DEOSGs could predict the disease outcomes of patients with ESCC and had strong clinical value. Our study improves the understanding of oxidative stress in tumor immune microenvironment (TIME) and provides insights for developing improved and efficient immunotherapy strategies.

Thymic epithelial tumors: examining the GTF2I mutation and developing a novel prognostic signature with LncRNA pairs to predict tumor recurrence.

BACKGROUND: General transcription factor IIi (GTF2I) mutations are very common in thymic epithelial tumors (TETs) and are related to a more favorable prognosis in TET patients. However, limited research has been conducted on the role of GTF2I in the tumor immune microenvironment (TIME). Further, long non-coding RNAs (lncRNAs) have been associated with the survival of patients with TETs. Therefore, this study aimed to explore the relationship between GTF2I mutations and TIME and build a new potential signature for predicting tumor recurrence in the TETs. Research data was downloaded from The Cancer Genome Atlas database and the CIBERSORT algorithm was used to evaluate TIME differences between GTF2I mutant and wild-type TETs. Relevant differentially expressed lncRNAs based on differentially expressed immune-related genes were identified to establish lncRNA pairs. We constructed a signature using univariate and multivariate Cox regression analyses. RESULTS: GTF2I is the most commonly mutated gene in TETs, and is associated with an increased number of early-stage pathological types, as well as no history of myasthenia gravis or radiotherapy treatment. In the GTF2I wild-type group, immune score and immune cell infiltrations with M2 macrophages, activated mast cells, neutrophils, plasma, T helper follicular cells, and activated memory CD4 T cells were higher than the GTF2I mutant group. A risk model was built using five lncRNA pairs, and the 1-, 3-, and 5-year area under the curves were 0.782, 0.873, and 0.895, respectively. A higher risk score was related to more advanced histologic type. CONCLUSION: We can define the GTF2I mutant-type TET as an immune stable type and the GTF2I wild-type as an immune stressed type. A signature based on lncRNA pairs was also constructed to effectively predict tumor recurrence.

A Novel Ras--Related Signature Improves Prognostic Capacity in Oesophageal Squamous Cell Carcinoma.

Oesophageal squamous cell carcinoma (ESCC) remains a clinically challenging disease with high morbidity rates and poor prognosis. ESCC is also the most common pathological type of oesophageal cancer (EC) in China. Ras-related genes are one of the most frequently mutated gene families in cancer and regulate tumour development and progression. Given this, we investigated the Ras-related gene expression profiles and their values in ESCC prognosis, using data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. We found that we could identify three distinct oesophageal cancer clusters based on their unique expression profile for 11 differentially expressed Ras-related genes with each of these demonstrating some prognostic value when, evaluated using univariate Cox analysis. We then used multivariate Cox analysis to identify relevant independent prognostic indicators and used these to build a new prognostic prediction model for oesophageal cancer patients using these three Ras-related genes. These evaluations produced an area under the curve (AUC) of 0.932. We found that our Ras-related signatures could also act as independent factors in ESCC prognosis and that patients with low Ras scores showed a higher overall expression levels of various immune checkpoint genes, including TNFSF4, TNFRSF8, TNFRSF9, NRP1, CD28, CD70, CD200, CD276, METTL16, METTL14, ZC3H13, YTHDF3, VIRMA, FTO, and RBM15, as well as a higher CSMD3, FLG, DNAH5, MUC4, PLCO, EYS, and ZNF804B mutation rates, and better sensitivity to drugs such as erlotinib, paclitaxel, and gefitinib. In conclusion, we were able to use the unique expression profiles of several Ras-related genes to produce a novel disease signature which might facilitate improved prognosis in ESCC, providing new insight into both diagnosis and treatment in these cancers.

A novel signature based on immune-related gene pairs and clinical features to predict prognosis and treatment effect in "driver gene negative" lung adenocarcinoma.

OBJECTIVE: Examining the role of immune-related genes (IRGs) in "driver gene negative" lung adenocarcinoma (LUAD) may provide new ideas for the treatment and study for this LUAD subgroup. We aimed to find the hub immune-related gene pairs can stratify the risk of "driver-gene-negative" LUAD. MATERIALS AND METHODS: IRGs were identified according to ImmPort database based on RNA sequencing results of tumors and normal tissues from 46 patients with "driver gene negative" LUAD at The First Affiliated Hospital of Sun Yat-sen University and cyclically singly paired as immune-related gene pairs (IRGPs). Multivariate Cox analysis was used to construct an immune risk model and a prognostic nomogram combining was also been developed. Immune microenvironment landscape described by CIBERSORT and drug sensitivity calculated by pRRophetic algorithm were used to explore possible treatment improvements. RESULTS: A novel immune risk model with 5-IRGPs (CD1A|CXCL135, CD1A|S100A7L2, IFNA7|CMTM2, IFNA7|CSF3, CAMP|TFR2) can accurately distinguish patients in the high- and low-risk groups. Risk score act as an independent prognostic factor and is related to clinical stage. There are significant differences in tumor immune microenvironment and PD-1/PD-L1/CTLA-4 expression between groups. The low-risk patient may benefit more from the commonly used chemotherapy regimens such as gemcitabine and paclitaxel. CONCLUSION: This study constructed 5-IRGPs as a reliable prognostic tool and may represent genes pairs that are potential rationale for choice of treatment for "driver gene negative" LUAD.