Multi-Institutional Prospective Validation of Prognostic mRNA Signatures in Early Stage Squamous Lung Cancer (Alliance)

A ferroptosis-related signature predicts the clinical diagnosis and prognosis, and associates with the immune microenvironment of lung cancer

Targeting ferroptosis-related pathway is a potential strategy for treatment of lung cancer (LC). Consequently, exploration of ferroptosis-related markers is important for treating LC. We collected LC clinical data and mRNA expression profiles from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained through FerrDB database. Expression analysis was performed to obtain differentially expressed FRGs. Diagnostic and prognostic models were constructed based on FRGs by LASSO regression, univariate, and multivariate Cox regression analysis, respectively. External verification cohorts GSE72094 and GSE157011 were used for validation. The interrelationship between prognostic risk scores based on FRGs and the tumor immune microenvironment was analyzed. Immunocytochemistry, Western blotting, and RT-qPCR detected the FRGs level. Eighteen FRGs were used for diagnostic models, 8 FRGs were used for prognostic models. The diagnostic model distinguished well between LC and normal samples in training and validation cohorts of TCGA. The prognostic models for TCGA, GSE72094, and GSE157011 cohorts significantly confirmed lower overall survival (OS) in high-risk group, which demonstrated excellent predictive properties of the survival model. Multivariate Cox regression analysis further confirmed risk score was an independent risk factor related with OS. Immunoassays revealed that in high-risk group, a significantly higher proportion of Macrophages_M0, Neutrophils, resting Natural killer cells and activated Mast cells and the level of B7H3, CD112, CD155, B7H5, and ICOSL were increased. In conclusion, diagnostic and prognostic models provided superior diagnostic and predictive power for LC and revealed a potential link between ferroptosis and TIME.

Construction of a prognostic model with CAFs for predicting the prognosis and immunotherapeutic response of lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is one of the subtypes of lung cancer (LC) that contributes to approximately 25%-30% of its prevalence. Cancer-associated fibroblasts (CAFs) are key cellular components of the TME, and the large number of CAFs in tumour tissues creates a favourable environment for tumour development. However, the function of CAFs in the LUSC is complex and uncertain. First, we processed the scRNA-seq data and classified distinct types of CAFs. We also identified prognostic CAFRGs using univariate Cox analysis and conducted survival analysis. Additionally, we assessed immune cell infiltration in CAF clusters using ssGSEA. We developed a model with a significant prognostic correlation and verified the prognostic model. Furthermore, we explored the immune landscape of LUSC and further investigated the correlation between malignant features and LUSC. We identified CAFs and classified them into three categories: iCAFs, mCAFs and apCAFs. The survival analysis showed a significant correlation between apCAFs and iCAFs and LUSC patient prognosis. Kaplan-Meier analysis showed that patients in CAF cluster C showed a better survival probability compared to clusters A and B. In addition, we identified nine significant prognostic CAFRGs (CLDN1, TMX4, ALPL, PTX3, BHLHE40, TNFRSF12A, VKORC1, CST3 and ADD3) and subsequently employed multivariate Cox analysis to develop a signature and validate the model. Lastly, the correlation between CAFRG and malignant features indicates the potential role of CAFRG in promoting tumour angiogenesis, EMT and cell cycle alterations. We constructed a CAF prognostic signature for identifying potential prognostic CAFRGs and predicting the prognosis and immunotherapeutic response for LUSC. Our study may provide a more accurate prognostic assessment and immunotherapy targeting strategies for LUSC.

Clonal gene signatures predict prognosis in mesothelioma and lung adenocarcinoma

Malignant pleural mesothelioma (MPM) is a rare but lethal pleural cancer with high intratumor heterogeneity (ITH). A recent study in lung adenocarcinoma has developed a clonal gene signature (ORACLE) from multiregional transcriptomic data and demonstrated high prognostic values and reproducibility. However, such a strategy has not been tested in other types of cancer with high ITH. We aimed to identify biomarkers from multi-regional data to prognostically stratify MPM patients. We generated a multiregional RNA-seq dataset for 78 tumor samples obtained from 26 MPM patients, each with one sample collected from a superior, lateral, and inferior region of the tumor. By integrating this dataset with the Cancer Genome Atlas MPM RNA-seq data, we selected 29 prognostic genes displaying high variability across different tumors but low ITH, which named PRACME (Prognostic Risk Associated Clonal Mesothelioma Expression). We evaluated PRACME in two independent MPM datasets and demonstrated its prognostic values. Patients with high signature scores are associated with poor prognosis after adjusting established clinical factors. Interestingly, the PRACME and the ORACLE signatures defined respectively from MPM and lung adenocarcinoma cross-predict prognosis between the two cancer types. Further investigation indicated that the cross-prediction ability might be explained by the high similarity between the two cancer types in their genomic regions with copy number variation, which host many clonal genes. Overall, our clonal signature PRACME provided prognostic stratification in MPM and this study emphasized the importance of multi-regional transcriptomic data for prognostic stratification based on clonal genes.

Effects of methionine deficiency on B7H3-DAP12-CAR-T cells in the treatment of lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is a subtype of lung cancer for which precision therapy is lacking. Chimeric antigen receptor T-cells (CAR-T) have the potential to eliminate cancer cells by targeting specific antigens. However, the tumor microenvironment (TME), characterized by abnormal metabolism could inhibit CAR-T function. Therefore, the aim of this study was to improve CAR-T efficacy in solid TME by investigating the effects of amino acid metabolism. We found that B7H3 was highly expressed in LUSC and developed DAP12-CAR-T targeting B7H3 based on our previous findings. When co-cultured with B7H3-overexpressing LUSC cells, B7H3-DAP12-CAR-T showed significant cell killing effects and released cytokines including IFN-γ and IL-2. However, LUSC cells consumed methionine (Met) in a competitive manner to induce a Met deficiency. CAR-T showed suppressed cell killing capacity, reduced cytokine release and less central memory T phenotype in medium with lower Met, while the exhaustion markers were up-regulated. Furthermore, the gene NKG7, responsible for T cell cytotoxicity, was downregulated in CAR-T cells at low Met concentration due to a decrease in m5C modification. NKG7 overexpression could partially restore the cytotoxicity of CAR-T in low Met. In addition, the anti-tumor efficacy of CAR-T was significantly enhanced when co-cultured with SLC7A5 knockdown LUSC cells at low Met concentration. In conclusion, B7H3 is a prospective target for LUSC, and B7H3-DAP12-CAR-T cells are promising for LUSC treatment. Maintaining Met levels in CAR-T may help overcome TME suppression and improve its clinical application potential.

Prediction of lymph node metastasis of lung squamous cell carcinoma by machine learning algorithm classifiers

BACKGROUND

Lymph node metastasis (LNM) is an essential factor affecting the prognosis of patients with lung squamous cell carcinoma (LUSC), as well as a critical consideration for the choice of treatment strategy. Exploring effective methods for predicting LNM in LUSC may benefit clinical decision making.

MATERIALS AND METHODS

We used data collected from the Surveillance, Epidemiology, and End Results (SEER) database to develop machine learning algorithm classifiers, including boosted trees (BTs), based on the primary clinical parameters of patients to predict LNM in LUSC. Training on a large-sample training cohort (n = 8,063) allowed for the construction of several concise classifiers for LNM prediction in LUSC, which were then validated using test and in-house cohorts (n = 2,017 and 57, respectively).

RESULTS

The six classifiers established in this research enabled distinction between patients with and without LNM. Among these classifiers, the BT classifier was the top performer, with accuracy, F1 scores, precision, recall, sensitivity, and specificity values of 0.654, 0.621, 0.654, 0.592, 0.592, and 0.711, respectively. The precision recall (PR) and receiver operating characteristic (ROC) (with area under the curve = 0.714) curves also supported this result, which was validated by the in-house cohort. Notably, the tumor stage was a critical factor in determining LNM in patients with LUSC.

CONCLUSIONS

The use of classifiers, especially the BT classifier, may serve as a useful tool for improving clinical precision and individualized treatment of patients with LUSC.

Identifying the cancer-associated fibroblast signature to predict the prognosis and immunotherapy response in patients with lung squamous cell carcinoma

Cancer-associated fibroblasts (CAFs) are an important component of the tumor microenvironment that contribute toward the development of tumors. This study aimed to establish a new algorithm based on CAF scores to predict the prognosis and immunotherapy response in patients with lung squamous cell carcinoma (LUSC). The RNA-seq data of LUSC patients were obtained from two databases and merged after removing inter-batch differences. The CAF-related data for each sample were obtained through three different algorithms. Consistency cluster analysis was performed to obtain different CAF clusters, which were analyzed to identify differentially expressed genes. These were subjected to uniform cluster analysis to obtain different gene clusters. The Boruta algorithm was used to calculate the CAF score. Three CAF clusters and two gene clusters were obtained, all of which differed in their patient prognoses and the content of infiltrating immune cells. Patients with high CAF scores exhibited worse overall survival, higher expression of biomarkers related to immune checkpoints and immune activity, and lower tumor mutation burden. The CAF score could also predict the immunotherapy response of patients. This study suggests that the CAF score can accurately predict the prognosis and immunotherapy response of LUSC patients.

Identification of a cancer-associated fibroblast classifier for predicting prognosis and therapeutic response in lung squamous cell carcinoma

Reliable prognostic gene signatures for cancer-associated fibroblasts (CAFs) in lung squamous cell carcinoma (LUSC) are still lacking, and the underlying genetic principles remain unclear. Therefore, the 2 main aims of our study were to establish a reliable CAFs prognostic gene signature that can be used to stratify patients with LUSC and to identify promising potential targets for more effective and individualized therapies. Clinical information and mRNA expression were accessed of the cancer genome atlas-LUSC cohort (n = 501) and GSE157011 cohort (n = 484). CAFs abundance were quantified by the multi-estimated algorithms. Stromal CAF-related genes were identified by weighted gene co-expression network analysis. The least absolute shrinkage and selection operator Cox regression method was utilized to identify the most relevant CAFs candidates for predicting prognosis. Chemotherapy sensitivity scores were calculated using the "pRRophetic" package in R software, and the tumor immune dysfunction and exclusion algorithm was employed to evaluate immunotherapy response. Gene set enrichment analysis and the Search Tool for Interaction of Chemicals database were applied to clarify the molecular mechanisms. In this study, we identified 288 hub CAF-related candidate genes by weighted gene co-expression network analysis. Next, 34 potential prognostic CAFs candidate genes were identified by univariate Cox regression in the cancer genome atlas-LUSC cohort. We prioritized the top 8 CAFs prognostic genes (DCBLD1, SLC24A3, ILK, SMAD7, SERPINE1, SNX9, PDGFA, and KLF10) by a least absolute shrinkage and selection operator Cox regression model, and these genes were used to identify low- and high-risk subgroups for unfavorable survival. In silico drug screening identified 6 effective compounds for high-risk CAFs-related LUSC: TAK-715, GW 441756, OSU-03012, MP470, FH535, and KIN001-266. Additionally, search tool for interaction of chemicals database highlighted PI3K-Akt signaling as a potential target pathway for high-risk CAFs-related LUSC. Overall, our findings provide a molecular classifier for high-risk CAFs-related LUSC and suggest that treatment with PI3K-Akt signaling inhibitors could benefit these patients.

THOC3 interacts with YBX1 to promote lung squamous cell carcinoma progression through PFKFB4 mRNA modification

The THO complex (THOC) is ubiquitously involved in RNA modification and various THOC proteins have been reported to regulate tumor development. However, the role of THOC3 in lung cancer remains unknown. In this study, we identified that THOC3 was highly expressed in lung squamous cell carcinoma (LUSC) and negatively associated with prognosis. THOC3 knockdown inhibited LUSC cell growth, migration, and glycolysis. THOC3 expression was regulated by TRiC proteins, such as CCT8 and CCT6A, which supported protein folding. Furthermore, THOC3 could form a complex with YBX1 to promote PFKFB4 transcription. THOC3 was responsible for exporting PFKFB4 mRNA to the cytoplasm, while YBX1 ensured the stability of PFKFB4 mRNA by recognizing m5C sites in its 3'UTR. Downregulation of PFKFB4 suppressed the biological activities of LUSC. Collectively, these findings suggest that THOC3, folded by CCT proteins can collaborate with YBX1 to maintain PFKFB4 expression and facilitate LUSC development. Therefore, THOC3 could be considered as a novel promising therapeutic target for LUSC.

Machine learning-based risk model incorporating tumor immune and stromal contexture predicts cancer prognosis and immunotherapy efficacy

The immune and stromal contexture within the tumor microenvironment (TME) interact with cancer cells and jointly determine disease process and therapeutic response. We aimed at developing a risk scoring model based on TME-related genes of squamous cell lung cancer to predict patient prognosis and immunotherapeutic response. TME-related genes were identified through exploring genes that correlated with immune scores and stromal scores. LASSO-Cox regression model was used to establish the TME-related risk scoring (TMErisk) model. A TMErisk model containing six genes was established. High TMErisk correlated with unfavorable OS in LUSC patients and this association was validated in multiple NSCLC datasets. Genes involved in pathways associated with immunosuppressive microenvironment were enriched in the high TMErisk group. Tumors with high TMErisk showed elevated infiltration of immunosuppressive cells. High TMErisk predicted worse immunotherapeutic response and prognosis across multiple carcinomas. TMErisk model could serve as a robust biomarker for predicting OS and immunotherapeutic response.

Single-cell analysis reveals prognostic fibroblast subpopulations linked to molecular and immunological subtypes of lung cancer

Fibroblasts are poorly characterised cells that variably impact tumour progression. Here, we use single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry (CIBERSORTx) to identify and characterise three major fibroblast subpopulations in human non-small cell lung cancer: adventitial, alveolar and myofibroblasts. Alveolar and adventitial fibroblasts (enriched in control tissue samples) localise to discrete spatial niches in histologically normal lung tissue and indicate improved overall survival rates when present in lung adenocarcinomas (LUAD). Trajectory inference identifies three phases of control tissue fibroblast activation, leading to myofibroblast enrichment in tumour samples: initial upregulation of inflammatory cytokines, followed by stress-response signalling and ultimately increased expression of fibrillar collagens. Myofibroblasts correlate with poor overall survival rates in LUAD, associated with loss of epithelial differentiation, TP53 mutations, proximal molecular subtypes and myeloid cell recruitment. In squamous carcinomas myofibroblasts were not prognostic despite being transcriptomically equivalent. These findings have important implications for developing fibroblast-targeting strategies for cancer therapy.

DLX2 Is a Potential Immune-Related Prognostic Indicator Associated with Remodeling of Tumor Microenvironment in Lung Squamous Cell Carcinoma: An Integrated Bioinformatical Analysis

BACKGROUND

It is still an unmet clinical need to identify potent biomarkers for immunotherapy on patients with lung squamous cell carcinoma (LUSC).

METHODS

In this study, we explored the differentially expressed genes (DEGs) that were simultaneously correlated with four pathways (i.e. CD8+ αβT cell proliferation/differentiation/activation pathways and ferroptosis pathway) and possibly related to the remodeling of tumor microenvironment via the TCGA-LUSC dataset. Besides, four GEO datasets (GSE157009, GSE157010, GSE19188, and GSE126045) and IMvigor210 dataset were utilized for confirmation and validation.

RESULTS

The co-downregulated DEG DLX2 was selected for further analysis. Function enrichment analysis revealed that low-expression of DLX2 was closely related to various immune-related pathways like T/B/NK cell mediated immunity, interferon gamma/alpha response, and various autoimmune disease. DLX2-downregulated group was enriched in more immune-activating cells and lower tumor immune dysfunction and exclusion (TIDE) score. Via the Cancer Immunome Atlas (TCIA) database, lower expression of DLX2 was also found to be associated with better IPS score of PD-1/PD-L1 blockade (p < 0.001) as well as CTLA-4 combined with PD-1/PD-L1 blockade (p < 0.001). Furthermore, patients in DLX2-low group were found to have significant longer median OS than those in DLX2-high group in IMvigor210 dataset (10.8 vs 7.4 months; hazard ratio [HR]=0.74, 95% confidence interval [95%CI] 0.57-0.96; p = 0.024).

CONCLUSIONS

Our study on an integrated bioinformatical analysis implied that DLX2 could be served as a promising indicator for remodeling tumor microenvironment status and predicting ICI response of patients with LUSC.

Replicability in cancer omics data analysis: measures and empirical explorations

In biomedical research, the replicability of findings across studies is highly desired. In this study, we focus on cancer omics data, for which the examination of replicability has been mostly focused on important omics variables identified in different studies. In published literature, although there have been extensive attention and ad hoc discussions, there is insufficient quantitative research looking into replicability measures and their properties. The goal of this study is to fill this important knowledge gap. In particular, we consider three sensible replicability measures, for which we examine distributional properties and develop a way of making inference. Applying them to three The Cancer Genome Atlas (TCGA) datasets reveals in general low replicability and significant across-data variations. To further comprehend such findings, we resort to simulation, which confirms the validity of the findings with the TCGA data and further informs the dependence of replicability on signal level (or equivalently sample size). Overall, this study can advance our understanding of replicability for cancer omics and other studies that have identification as a key goal.

Identification and Validation of a Four-Gene Ferroptosis Signature for Predicting Overall Survival of Lung Squamous Cell Carcinoma

Background

Lung squamous cell carcinoma (LUSC) represents 30% of all non-small cell lung carcinoma. Targeted therapy is not sufficient for LUSC patients because of the low frequency of targeted-effective mutation in LUSC whereas immunotherapy offers more options for patients with LUSC. We explored a ferroptosis-related prognostic signature that can potentially assess the prognosis and immunotherapy efficacy of LUSC patients.

Methods

A total of 502 LUSC patients were downloaded from The Cancer Genome Atlas (TCGA). The external validation data were obtained from the Gene Expression Omnibus (GEO): GSE73403. Then, we identified the candidate genes and constructed the prognostic signature through the Cox survival regression analyses and least absolute shrinkage and selection operator (LASSO). Risk score plot, Kaplan–Meier curve, and ROC curve were used to assess the prognostic power and performance of the model. The CIBERSORT algorithm estimated the fraction of immune cell types. TIDE was utilized to predict the response to immunotherapy. IMvigor210 was used to explore the association between the risk scores and immunotherapy outcomes. A nomogram combined selected clinical characteristics, and the risk scores were constructed.

Results

We screened 132 differentially expressed ferroptosis-related genes. According to KEGG and GO pathway analyses, these genes were mainly engaged in the positive regulation of cytokine production, cytokine metabolic process, and oxidoreductase activity. We then constructed a prognostic model via LASSO regression. The proportions of CD8+ T cells, CD4+ activated T cells, and follicular helper T cells were significantly different between low-risk and high-risk groups. TIDE algorithm indicated that low-risk LUSC patients might profit more from immune checkpoint inhibitors. The predictive value of the ferroptosis gene model in immunotherapy response was further confirmed in IMvigor210. Finally, we combined the clinical characteristics with a LASSO regression model to construct a nomogram that could be easily applied in clinical practice.

Conclusion

We identified a prognostic model that provides an accurate and objective basis for guiding individualized treatment decisions for LUSC.

Augmentation of the RNA m6A reader signature is associated with poor survival by enhancing cell proliferation and EMT across cancer types

N⁶-Methyladenosine (m6A) RNA modification plays a critical role in the posttranscriptional regulation of gene expression. Alterations in cellular m6A levels and m6A-related genes have been reported in many cancers, but whether they play oncogenic or tumor-suppressive roles is inconsistent across cancer types. We investigated common features of alterations in m6A modification and m6A-related genes during carcinogenesis by analyzing transcriptome data of 11 solid tumors from The Cancer Genome Atlas database and our in-house gastric cancer cohort. We calculated m6A writer (W), eraser (E), and reader (R) signatures based on corresponding gene expression. Alterations in the W and E signatures varied according to the cancer type, with a strong positive correlation between the W and E signatures in all types. When the patients were divided according to m6A levels estimated by the ratio of the W and E signatures, the prognostic effect of m6A was inconsistent according to the cancer type. The R and especially the R2 signatures (based on the expression of IGF2BPs) were upregulated in all cancers. Patients with a high R2 signature exhibited poor prognosis across types, which was attributed to enrichment of cell cycle- and epithelial–mesenchymal transition-related pathways. Our study demonstrates common features of m6A alterations across cancer types and suggests that targeting m6A R proteins is a promising strategy for cancer treatment.

Studying the effects of a chemical modification of messenger RNA molecules (mRNA), which carry genetic information from DNA to the cell’s protein-making machinery, reveals new insights into the role of these modifications in cancer, suggesting potential therapeutic approaches. Researchers in Seoul, South Korea, led by Joon-Yong An at Korea University and Sung-Yup Cho at Seoul National University investigated the most common modifications of mRNA involving methyl groups (CH₃): addition (‘writing’), having a regulatory effect on the cell (‘reading’) or removal (‘erasing’). The molecular activities involved in reading the modifications were increased in all 11 types of cancer in cancer-sampling databases and their own patient cohort. Changes in writing and erasing of the modifications varied with cancer type. The proteins that mediate the reading responses to RNA methylation are possible targets for new anti-cancer drugs.

Cancer Stem Cells (CSCs), Circulating Tumor Cells (CTCs) and Their Interplay with Cancer Associated Fibroblasts (CAFs): A New World of Targets and Treatments

Simple Summary

The world of small molecules in solid tumors as cancer stem cells (CSCs), circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) continues to be under-debated, but not of minor interest in recent decades. One of the main problems in regard to cancer is the development of tumor recurrence, even in the early stages, in addition to drug resistance and, consequently, ineffective or an incomplete response against the tumor. The findings behind this resistance are probably justified by the presence of small molecules such as CSCs, CTCs and CAFs connected with the tumor microenvironment, which may influence the aggressiveness and the metastatic process. The mechanisms, connections, and molecular pathways behind them are still unknown. Our review would like to represent an important step forward to highlight the roles of these molecules and the possible connections among them.

Abstract

The importance of defining new molecules to fight cancer is of significant interest to the scientific community. In particular, it has been shown that cancer stem cells (CSCs) are a small subpopulation of cells within tumors with capabilities of self-renewal, differentiation, and tumorigenicity; on the other side, circulating tumor cells (CTCs) seem to split away from the primary tumor and appear in the circulatory system as singular units or clusters. It is becoming more and more important to discover new biomarkers related to these populations of cells in combination to define the network among them and the tumor microenvironment. In particular, cancer-associated fibroblasts (CAFs) are a key component of the tumor microenvironment with different functions, including matrix deposition and remodeling, extensive reciprocal signaling interactions with cancer cells and crosstalk with immunity. The settings of new markers and the definition of the molecular connections may present new avenues, not only for fighting cancer but also for the definition of more tailored therapies.

Updated Prognostic Factors in Localized NSCLC

Lung cancer is the most common cause of cancer mortality worldwide, and non-small cell lung cancer (NSCLC) represents 80% of lung cancer subtypes. Patients with localized non-small cell lung cancer may be considered for upfront surgical treatment. However, the overall 5-year survival rate is 59%. To improve survival, adjuvant chemotherapy (ACT) was largely explored and showed an overall benefit of survival at 5 years < 7%. The evaluation of recurrence risk and subsequent need for ACT is only based on tumor stage (TNM classification); however, more than 25% of patients with stage IA/B tumors will relapse. Recently, adjuvant targeted therapy has been approved for EGFR-mutated resected NSCLC and trials are evaluating other targeted therapies and immunotherapies in adjuvant settings. Costs, treatment duration, emergence of resistant clones and side effects stress the need for a better selection of patients. The identification and validation of prognostic and theranostic markers to better stratify patients who could benefit from adjuvant therapies are needed. In this review, we report current validated clinical, pathological and molecular prognosis biomarkers that influence outcome in resected NSCLC, and we also describe molecular biomarkers under evaluation that could be available in daily practice to drive ACT in resected NSCLC.

Multi-omics analysis to identify lung squamous carcinoma lactate metabolism-related subtypes and establish related index to predict prognosis and guide immunotherapy

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Multi-omics analysis to analyze the effect of lactate metabolism on LUSC phenotype.

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Lactate metabolism and immunogenomics crosstalk analysis to identify LUSC subtypes.

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Lactate metabolism can reflect the level of LUSC metabolic reprogramming.

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LMRPI can predict the prognosis of LUSC patients and guide individualized treatment.

Lung squamous carcinoma (LUSC) is a malignant tumor of the respiratory system with highly heterogeneous characteristics. Lactate is the main product of aerobic glycolysis during the metabolic reprogramming of tumors. There is growing evidence that lactate metabolic processes have a broad and sophisticated impact on tumor phenotypic plasticity and tumor microenvironment (TME). However, the pattern of lactate metabolism in patients with LUSC and its impact on TME, phenotype, prognosis, and treatment have not been fully elucidated. In this study, we identified two subtypes with different lactate metabolism patterns in LUSC by non-negative matrix factorization and explored their multi-omics features. We observed that lactate metabolism levels in LUSC extensively influenced tumor immune infiltration patterns, adaptation to the hypoxia environment, and energy metabolic reprogramming. Subsequently, we constructed the lactate metabolism-related prognostic index (LMRPI) using Cox stepwise regression analysis. LMRPI showed excellent stability and accuracy, and based on the median value of LMRPI, LUAD were divided into two subgroups. The two subgroups have different patterns of immune infiltration and somatic mutations. Meanwhile, the two subgroups had different responsiveness to immune checkpoint inhibitor (ICI) therapies and different sensitivity to various chemotherapeutic and molecular targeting agents. In conclusion, we defined two subtypes with different lactate metabolism patterns in LUSC and extensively characterized their multi-omics profile. Furthermore, we developed LMRPI that predicts the prognosis of LUSC patients while also predicting their response to various adjuvant therapies, including immunotherapy, to guide their individualized treatment.

Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma

Background

Early-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.

Methods

Gene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm.

Results

A signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group.

Conclusion

A signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.

The bioinformatics analysis of RIOX2 gene in lung adenocarcinoma and squamous cell carcinoma

Lung cancer is characterized by high morbidity and mortality rates, and it has become an important public health issue worldwide. The occurrence and development of tumors is a multi-gene and multi-stage complex process. As an oncogene, ribosomal oxygenase 2 (RIOX2) has been associated with a variety of cancers. In this article, we analyzed the correlation between RIOX2 expression and methylation in lung cancer based on the databases including the cancer genome atlas (TCGA) (https://portal.gdc.cancer.gov/) and the gene expression omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). It was found that RIOX2 is highly expressed in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tissues, whose expression is negatively correlated with its methylation level. In this regard, methylation at cg09716038, cg14773523, cg14941179, and cg22299097 had a significant negative correlation with RIOX2 expression in LUAD, whereas in LUSC, methylation at cg09716038, cg14773523, cg14941179, cg22299097, cg05451573, cg10779801, and cg23629183 is negatively correlated with RIOX2 expression. According to the analysis based on the databases, RIOX2 gene could not be considered as the independent prognostic biomarker in lung adenocarcinoma or squamous cell lung cancer. However, the molecular mechanism of RIOX2 gene in the development of lung cancer may be helpful in improving lung cancer therapy.

Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer

Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.