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 ... -  《-》

Pan-Cancer Analysis of Immune Cell Infiltration Identifies a Prognostic Immune-Cell Characteristic Score (ICCS) in Lung Adenocarcinoma.

Background: The tumor microenvironment (TME) consists of heterogeneous cell populations, including malignant cells and nonmalignant cells that support tumor proliferation, invasion, and metastasis through extensive cross talk. The intra-tumor immune landscape is a critical factor influencing patient survival and response to immunotherapy. Methods: Gene expression data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Immune cell infiltration was determined by single-sample Gene Set Enrichment Analysis (ssGSEA) depending on the integrated immune gene sets from published studies. Univariate analysis was used to determine the prognostic value of the infiltrated immune cells. Least absolute shrinkage and selection operator (LASSO) regression was performed to screen for the most survival-relevant immune cells. An immune-cell characteristic score (ICCS) model was constructed by using multivariate Cox regression analysis. Results: The immune cell infiltration patterns across 32 cancer types were identified, and patients in the high immune cell infiltration cluster had worse overall survival (OS) but better progression-free interval (PFI) compared to the low immune cell infiltration cluster. However, immune cell infiltration showed inconsistent prognostic value depending on the cancer type. High immune cell infiltration (High CI) indicated a worse prognosis in brain lower grade glioma (LGG), glioblastoma multiforme (GBM), and uveal melanoma (UVM), and favorable prognosis in adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), sarcoma (SARC), and skin cutaneous melanoma (SKCM). LUAD prognosis was significantly influenced by the infiltration of 13 immune cell types, with high infiltration of all but Type 2 T helper (Th2) cells correlating with a favorable prognosis. The ICCS model based on six most survival-relevant immune cell populations was generated that classified patients into low- and high-ICCS groups with good and poor prognoses, respectively. The multivariate and stratified analyses further revealed that the ICCS was an independent prognostic factor for LUAD. Conclusions: The infiltration of immune cells in 32 cancer types was quantified, and considerable heterogeneity was observed in the prognostic relevance of these cells in different cancer types. An ICCS model was constructed for LUAD with competent prognostic performance, which can further deepen our understanding of the TME of LUAD and can have implications for immunotherapy.

Zuo S ，Wei M ，Wang S ，Dong J ，Wei J ... -  《Frontiers in Immunology》

Identification of immune-related gene signature predicting survival in the tumor microenvironment of lung adenocarcinoma.

Zhao M ，Li M ，Chen Z ，Bian Y ，Zheng Y ，Hu Z ，Liang J ，Huang Y ，Yin J ，Zhan C ，Feng M ，Wang Q ... -  《-》

Autophagy-related gene expression classification defines three molecular subtypes with distinct clinical and microenvironment cell infiltration characteristics in colon cancer.

Multiple molecular subtypes with distinct clinical outcomes in colon cancer have been identified in recent years. Nonetheless, the autophagy-related molecular subtypes as well as its mediated tumor microenvironment (TME) cell infiltration characteristics have not been fully understood. Based on the seven colon cancer cohorts with 1580 samples, we performed a comprehensive genomic analysis to explore the molecular subtypes mediated by autophagy-related genes. The single-sample gene-set enrichment analysis (ssGSEA) was used to quantify the relative abundance of each cell infiltration in the TME. Unsupervised methods were used to perform autophagy subtype clustering. Least absolute shrinkage and selection operator regression (LASSO) was used to construct autophagy characterization score (APCS) signature. We determined three distinct autophagy-related molecular subtypes in colon cancer. The three autophagy subtypes presented significant survival differences. Microenvironment analyses revealed the heterogeneous TME immune cell infiltration characterization between three subtypes. Cluster 1 autophagy subtype was characterized by abundant innate and adaptive immune cell infiltration. This subtype exhibited an enhanced stromal activity including activated pathways of epithelial-mesenchymal transition, TGF-β and angiogenesis, and an increased infiltration of fibroblasts and endothelial cells. The expression of immune checkpoint molecules was also significantly up-regulated, which may mediate immune escape in Cluster 1 subtype. Cluster 2 subtype was characterized by relatively lower TME immune cell infiltration and enhanced DNA damage repair pathways. Cluster 3 subtype was characterized by the suppression of immunity. Patients with high APCS, with poorer survival, presented a significantly positive correlation with TME stromal activity. Low APCS, relevant to activated damage repair pathways, showed enhanced responses to anti-PD-1/PD-L1 immunotherapy. Two immunotherapy cohorts confirmed patients with low APCS exhibited prominently enhanced clinical response and treatment advantages. This study may help understand the molecular characterization of autophagy-related subtypes. We demonstrated the autophagy genes in colon cancer could drive the heterogeneity of TME immune cell infiltration. Our study represented a step toward personalized immunotherapy in colon cancer.

Zhu S ，Wu Q ，Zhang B ，Wei H ，Li B ，Shi W ，Fang M ，Zhu S ，Wang L ，Lang Zhou Y ，Dong Y ... -  《-》

Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major subtype of non-small cell lung cancer (NSCLC) with a highly heterogeneous tumor microenvironment. Immune checkpoint inhibitors (ICIs) are more effective in tumors with a pre-activated immune status. However, the potential of the immune activation-associated gene (IAG) signature for prognosis prediction and immunotherapy response assessment in LUAD has not been established. Therefore, it is critical to explore such gene signatures. RNA sequencing profiles and corresponding clinical parameters of LUAD were extracted from the TCGA and GEO databases. Unsupervised consistency clustering analysis based on immune activation-related genes was performed on the enrolled samples. Subsequently, prognostic models based on genes associated with prognosis were built using the last absolute shrinkage and selection operator (LASSO) method and univariate Cox regression. The expression levels of four immune activation related gene index (IARGI) related genes were validated in 12 pairs of LUAD tumor and normal tissue samples using qPCR. Using the ESTIMATE, TIMER, and ssGSEA algorithms, immune cell infiltration analysis was carried out for different groups, and the tumor immune dysfunction and rejection (TIDE) score was used to evaluate the effectiveness of immunotherapy. Based on the expression patterns of IAGs, the TCGA LUAD cohort was classified into two clusters, with those in the IAG-high pattern demonstrating significantly better survival outcomes and immune cell infiltration compared to those in the IAG-low pattern. Then, we developed an IARGI model that effectively stratified patients into different risk groups, revealing differences in prognosis, mutation profiles, and immune cell infiltration within the tumor microenvironment between the high and low-risk groups. Notably, significant disparities in TIDE score between the two groups suggest that the low-risk group may exhibit better responses to ICIs therapy. The IARGI risk model was validated across multiple datasets and demonstrated exceptional performance in predicting overall survival in LUAD, and an IARGI-integrated nomogram was established as a quantitative tool for clinical practice. The IARGI can serve as valuable biomarkers for evaluating the tumor microenvironment and predicting the prognosis of LUAD patients. Furthermore, these genes probably provide valuable guidance for establishing effective immunotherapy regimens for LUAD patients.

Zeng W ，Wang J ，Yang J ，Chen Z ，Cui Y ，Li Q ，Luo G ，Ding H ，Ju S ，Li B ，Chen J ，Xie Y ，Tong X ，Liu M ，Zhao J ... -  《Frontiers in Immunology》