Molecular characterization based on tumor microenvironment-related signatures for guiding immunotherapy and therapeutic resistance in lung adenocarcinoma.

Background: Although the role of tumor microenvironment in lung adenocarcinoma (LUAD) has been explored in a number of studies, the value of TME-related signatures in immunotherapy has not been comprehensively characterized. Materials and Methods: Consensus clustering was conducted to characterize TME-based molecular subtypes using transcription data of LUAD samples. The biological pathways and immune microenvironment were assessed by CIBERSORT, ESTIMATE, and gene set enrichment analysis. A TME-related risk model was established through the algorithms of least absolute shrinkage and selection operator (Lasso) and stepwise Akaike information criterion (stepAIC). Results: Four TME-based molecular subtypes including C1, C2, C3, and C4 were identified, and they showed distinct overall survival, genomic characteristics, DNA methylation pattern, immune microenvironment, and biological pathways. C1 had the worst prognosis and high tumor proliferation rate. C3 and C4 had higher enrichment of anti-tumor signatures compared to C1 and C2. C4 had evidently low enrichment of epithelial-mesenchymal transition (EMT) signature and tumor proliferation rate. C3 was predicted to be more sensitive to immunotherapy compared with other subtypes. C1 is more sensitive to chemotherapy drugs, including Docetaxel, Vinorelbine and Cisplatin, while C3 is more sensitive to Paclitaxel. A five-gene risk model was constructed, which showed a favorable performance in three independent datasets. Low-risk group showed a longer overall survival, more infiltrated immune cells, and higher response to immunotherapy than high-risk group. Conclusion: This study comprehensively characterized the molecular features of LUAD patients based on TME-related signatures, demonstrating the potential of TME-based signatures in exploring the mechanisms of LUAD development. The TME-related risk model was of clinical value to predict LUAD prognosis and guide immunotherapy.

Jie Y ，Wu J ，An D ，Li M ，He H ，Wang D ，Gu A ，E M ... -  《Frontiers in Pharmacology》

Molecular subtyping for lung adenocarcinoma and a novel prognostic model based on ligand-receptor pairs.

Lung adenocarcinoma (LUAD) is a leading cause of cancer death worldwide. Ligands and receptors play important roles in cell communication. This study aimed to demonstrate the importance of ligand-receptor (LR) pairs in LUAD development through constructing molecular subtypes and a prognostic model based on LR pairs. A total of 1110 LUAD samples with clinical and expression data were obtained from public databases. Unsupervised consensus clustering was applied to construct molecular subtypes based on LR pairs. Least absolute shrinkage and selection operator (LASSO) Cox regression and stepwise Akaike information criterion (stepAIC) were conducted to build a prognostic model. Three molecular subtypes (C1, C2 and C3) were constructed based on 17 prognosis-related LR pairs. C1 subtype had the worst prognosis, while C3 subtype had the optimal prognosis. Oncogenic pathways such as epithelial-mesenchymal transition (EMT) were activated in C1 subtype. A prognostic model was built based on 8 LR pairs, and could classify samples into high- and low-LR score groups. Two groups had distinct overall survival and tumor microenvironment (TME). High-LR score group was more sensitive to chemotherapeutic drugs, while low-LR score group could benefit much from anti-PD-1/PD-L1 therapy. The study showed that LR pairs played critical roles in LUAD development. The prognostic model could predict prognosis and guide personalized therapy for LUAD patients.

Li D ，Ma X ，Ou S 《-》

Characterization of tumor microenvironment and programmed death-related genes to identify molecular subtypes and drug resistance in pancreatic cancer.

Background: Immunotherapy has been a key option for the treatment of many types of cancer. A positive response to immunotherapy is heavily dependent on tumor microenvironment (TME) interaction. However, in pancreatic adenocarcinoma (PAAD), the association between TME mode of action and immune cell infiltration and immunotherapy, clinical outcome remained unknown. Methods: We systematically evaluated 29 TME genes in PAAD signature. Molecular subtypes of distinct TME signatures in PAAD were characterized by consensus clustering. After this, we comprehensively analyzed their clinical features, prognosis, and immunotherapy/chemotherapy response using correlation analysis, Kaplan-Meier curves analysis, ssGSEA analysis. 12 programmed cell death (PCD) patterns were acquired from previous study. Differentially expressed genes (DEGs) were acquired based on differential analysis. Key genes affecting overall survival (OS) of PAAD were screened by COX regression analysis and used to develop a RiskScore evaluation model. Finally, we assessed the value of RiskScore in predicting prognosis and treatment response in PAAD. Results: We identified 3 patterns of TME-associated molecular subtypes (C1, C2, C3), and observed that clinicopathological characteristics, prognosis, pathway features and immune features, immunotherapy/chemosensitivity of patients were correlated with the TME related subtypes. C1 subtype was more sensitive to the four chemotherapeutic drugs. PCD patterns were more likely to occur at C2 or C3. At the same time, we also detected 6 key genes that could affect the prognosis of PAAD, and 5 genes expressions were closely associated to methylation level. Low-risk patients with high immunocompetence had favorable prognostic results and high immunotherapy benefit. Patients in the high-risk group were more sensitive to chemotherapeutic drugs. RiskScore related to TME was an independent prognostic factor for PAAD. Conclusion: Collectively, we identified a prognostic signature of TME in PAAD patients, which could help elucidate the specific mechanism of action of TME in tumors and help to explore more effective immunotherapy strategies.

Yu L ，He R ，Cui Y 《-》

Development and validation of polyamines metabolism-associated gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma.

Polyamines metabolism is closely related to tumor development and progression, as well as tumor microenvironment (TME). In this study, we focused on exploring whether polyamines metabolism-associated genes would provide prognosis and immunotherapy response prediction in lung adenocarcinoma (LUAD). The expression profile data of polyamines metabolism-associated genes were acquired from the Cancer Genome Atlas (TCGA) database. Utilizing the least absolute shrinkage and selection operator (LASSO) algorithm, we created a risk score model according to polyamines metabolism-associated gene signatures. Meanwhile, an independent cohort (GSE72094) was employed to validate this model. Through the univariate and multivariate Cox regression analyses, the independent prognostic factors were identified. Subsequently, quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect their expression in LUAD cells. By consensus clustering analysis, polyamines metabolism-associated subgroups were determined in LUAD patients, with differential gene expression, prognosis, and immune characteristics analyses explored. A total of 59 polyamines metabolism genes were collected for this study, of which 14 genes were identified for the construction of risk score model using LASSO method. High- and low- risk groups of LUAD patients in TCGA cohort were distinguished via this model, and high-risk group presented dismal clinical outcomes. The same prognostic prediction of this model had been also validated in GSE72094 cohort. Meanwhile, three independent prognostic factors (PSMC6, SMOX, SMS) were determined for constructing the nomogram, and they were all upregulated in LUAD cells. In addition, two distinct subgroups (C1 and C2) were identified in LUAD patients. Comparing the two subgroups, 291 differentially expressed genes (DEGs) were acquired, mainly enriching in organelle fission, nuclear division, and cell cycle. Comparing to C1 subgroup, the patients in C2 subgroup had favorable clinical outcomes, increased immune cells infiltration, and effective immunotherapy response. This study identified polyamines metabolism-associated gene signatures for predicting the patients' survival, and they were also linked to immune cells infiltration and immunotherapy response in LUAD patients.

Wang N ，Chai M ，Zhu L ，Liu J ，Yu C ，Huang X ... -  《Frontiers in Immunology》

Integration of multiple key molecules in lung adenocarcinoma identifies prognostic and immunotherapeutic relevant gene signatures.

Although multiple key molecules in lung adenocarcinoma (LUAD) have been identified in recent years, the overall tumor microenvironment (TME) immune cell infiltration characterizations mediated by multiple key molecules remain little known. This study aimed to integrate the roles of multiple key molecules to evaluate patient prognosis and TME cell infiltration characterization as well as responses to immunotherapy. Using combined LUAD cancer cohorts with 228 normal samples and 913 tumor samples, we comprehensively dissected the differences of genomic and TME cell infiltration landscapes between normal lung tissues and tumor tissues. The single-sample gene-set enrichment analysis (ssGSEA) was used to quantify the relative abundance of 24 cell infiltration. The riskScore signature was constructed using a least absolute shrinkage and selection operator (LASSO) Cox regression mode. Seven novel key molecules with significantly up-regulated expression in LUAD were determined. Survival analyses revealed their important prognostic values. LUAD microenvironment presented a markedly decreased infiltration of immune cells compared to normal lung tissues. We found tumors with up-regulated expression of these key molecules exhibited a significantly decreased TME cell infiltration and increased immune checkpoint molecule expression. The high riskScore subtype was characterized by decreased innate and adaptive immune cell infiltration. Activation of p53 signaling pathway and regulator T cells were observed in the high riskScore subtype, which were regarded as T-cell suppressive and could be responsible for poorer prognosis in this subtype (HR 1.83(1.27-2.63)). Multivariate analyses demonstrated the riskScore was a robust and independent prognostic biomarker, and its value in predicting immunotherapeutic outcomes was also confirmed (HR 1.70(1.22-2.37)). This study reveal a novel gene signature significantly related to patient prognosis and TME cell infiltration in LUAD. We demonstrated the integrated roles of multiple key molecules played a crucial role in shaping TME cell infiltration diversity and complexity. Evaluating the integrated characterization of multiple key molecules could contribute to predicting patients' response to immunotherapy and guiding more effective immunotherapy strategies.

Wu Q ，Wang L ，Wei H ，Li B ，Yang J ，Wang Z ，Xu J ，Zhou YL ，Zhang B ... -  《-》