GTSE1: A potential prognostic and diagnostic biomarker in various tumors including lung adenocarcinoma.

OBJECTIVE: This research was aimed to comprehensively investigate the expression levels, diagnostic and prognostic implications, and the relationship with immune infiltration of G2 and S phase-expressed-1 (GTSE1) across 33 tumor types, including lung adenocarcinoma (LUAD), through gene expression profiling. METHODS: GTSE1 mRNA expression data together with clinical information were acquired from Xena database of The Cancer Genome Atlas (TCGA), ArrayExpress, and Gene Expression Omnibus (GEO) database for this study. The Wilcoxon rank-sum test was used to detect differences in GTSE1 expression between groups. The ability of GTSE1 to accurately predict cancer status was evaluated by calculating the area under the curve (AUC) value for the receiver operating characteristic curve. Additionally, we investigated the predictive value of GTSE1 in individuals diagnosed with neoplasms using univariate Cox regression analysis as well as Kaplan-Meier curves. Furthermore, the correlation between GTSE1 expression and levels of immune infiltration was assessed by utilizing the Tumor Immune Estimate Resource (TIMER) database to calculate the Spearman rank correlation coefficient. Finally, the pan-cancer analysis findings were validated by examining the association between GTSE1 expression and prognosis among patients with LUAD. RESULTS: GTSE1 exhibited significantly increased expression levels in a wide range of tumor tissues in contrast with normal tissues (p < 0.05). The expression of GTSE1 in various tumors was associated with clinical features, overall survival, and disease-specific survival (p < 0.05). In immune infiltration analyses, a strong correlation of the level of immune infiltration with the expression of GTSE1 was observed. Furthermore, GTSE1 demonstrated good discriminative and diagnostic value for most tumors. Additional experiments confirmed the relationship between elevated GTSE1 expression and unfavorable prognosis in individuals diagnosed with LUAD. These findings indicated the crucial role of GTSE1 expression level in influencing the development and immune infiltration of different types of tumors. CONCLUSIONS: GTSE1 might be a potential biomarker for the prognosis of pan-cancer. Meanwhile, it represented a promising target for immunotherapy.

Death associated protein kinase 1 predicts the prognosis and the immunotherapy response of various cancers.

BACKGROUND: Death Associated Protein Kinase 1 (DAPK1) is a calcium/calmodulin-dependent serine/threonine kinase, which has been reported to be a tumor suppressor with unbalanced expression in various tissues. However, its function in tumor immunotherapy is still unclear. METHODS: The online GEPIA2 database was used to support TCGA results. We explored the DAPK1 pan-cancer genomic alteration analysis using the cBioPortal web tool. The Human Protein Atlas (HPA) was employed to mine DAPK1 protein information. We verified the expression of DAPK1 in lung adenocarcinoma samples using RT-qPCR. Subsequently, the relationship between the expression of DAPK1 and the clinical stage was analyzed. We used TIMER2.0 as the primary platform for studying DAPK1-related immune cell infiltration. Associations between DAPK1 and immunotherapy biomarkers were analyzed using Spearman correlation analysis. TMB and MSI expression was also examined. Finally, we used Kaplan-Meier Plots to evaluate the relationship between DAPK1 expression and the efficacy of immunotherapy. RESULTS: DAPK1 is aberrantly expressed in most cancer types and has prognostic power in various cancers. Gene mutation was the most common DAPK1 alteration across pan-cancers. The DAPK1 protein was mainly localized to tumor cell centrosomes. DAPK1 was also significantly associated with immune-activated hallmarks, immune cell infiltration, and the expression of immunomodulators. Notably, DAPK1 can also significantly predict responses to anti-PD1 and anti-CTLA-4 therapy in cancer patients. CONCLUSIONS: Our findings suggest that DAPK1 may not only be an effective prognostic factor in cancer patients but may also function as a promising predictive immunotherapy biomarker for cancer patients treated with immune checkpoint inhibitors.

Machine learning-based integration develops an immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy response in lung adenocarcinoma.

Accumulating evidence demonstrates that lncRNAs are involved in the regulation of the immune microenvironment and early tumor development. Immunogenic cell death occurs mainly through the release or increase of tumor-associated antigen and tumor-specific antigen, exposing "danger signals" to stimulate the body's immune response. Given the recent development of immunotherapy in lung adenocarcinoma, we explored the role of tumor immunogenic cell death-related lncRNAs in lung adenocarcinoma for prognosis and immunotherapy benefit, which has never been uncovered yet. Based on the lung adenocarcinoma cohorts from the TCGA database and GEO database, the study developed the immunogenic cell death index signature by several machine learning algorithms and then validated the signature for prognosis and immunotherapy benefit of lung adenocarcinoma patients, which had a more stable performance compared with published signatures in predicting the prognosis, and demonstrated predictive value for benefiting from immunotherapy in multiple cohorts of multiple cancers, and also guided the utilization of chemotherapy drugs.

Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures.

In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model's utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model.

Identification and analysis of prognostic immune cell homeostasis characteristics in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most invasive malignant tumor of the respiratory system. It is also the common pathological type leading to the death of LUAD. Maintaining the homeostasis of immune cells is an important way for anti-tumor immunotherapy. However, the biological significance of maintaining immune homeostasis and immune therapeutic effect has not been well studied. METHODS: We constructed a diagnostic and prognostic model for LUAD based on B and T cells homeostasis-related genes. Minimum absolute contraction and selection operator (LASSO) analysis and multivariate Cox regression are used to identify the prognostic gene signatures. Based on the overall survival time and survival status of LUAD patients, a 10-gene prognostic model composed of ABL1, BAK1, IKBKB, PPP2R3C, CCNB2, CORO1A, FADD, P2RX7, TNFSF14, and ZC3H8 was subsequently identified as prognostic markers from The Cancer Genome Atlas (TCGA)-LUAD to develop a prognostic signature. This study constructed a gene prognosis model based on gene expression profiles and corresponding survival information through survival analysis, as well as 1-year, 3-year, and 5-year ROC curve analysis. Enrichment analysis attempted to reveal the potential mechanism of action and molecular pathway of prognostic genes. The CIBERSORT algorithm calculated the infiltration degree of 22 immune cells in each sample and compared the difference of immune cell infiltration between high-risk group and low-risk group. At the cellular level, PCR and CKK8 experiments were used to verify the differences in the expression of the constructed 10-gene model and its effects on cell viability, respectively. The experimental results supported the significant biological significance and potential application value of the molecular model in the prognosis of lung cancer. Enrichment analyses showed that these genes were mainly related to lymphocyte homeostasis. CONCLUSION: We identified a novel immune cell homeostasis prognostic signature. Targeting these immune cell homeostasis prognostic genes may be an alternative for LUAD treatment. The reliability of the prediction model was confirmed at bioinformatics level, cellular level, and gene level.

Implications of GCLC in prognosis and immunity of lung adenocarcinoma and multi-omics regulation mechanisms.

BACKGROUND: Ferroptosis is an iron-dependent type of regulated cell death, and has been implicated in lung adenocarcinoma (LUAD). Evidence has proved the key role of glutamate-cysteine ligase catalytic subunit (GCLC) in ferroptosis, but its role in LUAD remains unclear. Herein, we explored the implications of GCLC and relevant genes in LUAD prognosis and immunity as well as underlying molecular mechanisms. METHODS: This work gathered mRNA, miRNA, DNA methylation, somatic mutation and copy-number variation data from TCGA-LUAD. WGCNA was utilized for selecting GCLC-relevant genes, and a GCLC-relevant prognostic signature was built by uni- and multivariate-cox regression analyses. Immune compositions were estimated via CIBERSORT, and two immunotherapy cohorts of solid tumors were analyzed. Multi-omics regulatory mechanisms were finally assessed. RESULTS: Our results showed that GCLC was overexpressed in LUAD, and potentially resulted in undesirable survival. A prognostic model was generated, which owned accurate and independent performance in prognostication. GCLC, and relevant genes were notably connected with immune compositions and immune checkpoints. High GCLC expression was linked with better responses to anti-PD-L1 and anti-CTLA-4 treatment. Their possible DNA methylation sites were inferred, e.g., hypomethylation in cg19740353 might contribute to GCLC up-regulation. Frequent genetic mutations also affected their expression. Upstream transcription factors (E2F1/3/4, etc.), post-transcriptional regulation of miRNAs (hsa-mir-30c-1, etc.), lncRNAs (C8orf34-AS1, etc.), and IGF2BP1-mediated m6A modification were identified. It was also found NOP58-mediated SUMOylation post-translational modification. CONCLUSIONS: Together, we show that GCLC and relevant genes exert crucial roles in LUAD prognosis and immunity, and their expression can be controlled by complex multi-omics mechanisms.

Single-cell sequencing of tumour infiltrating T cells efficiently identifies tumour-specific T cell receptors based on the T cell activation score.

Adoptively transferred T cell receptor-engineered T cells are a promising cancer treatment strategy, and the identification of tumour-specific TCRs is essential. Previous studies reported that tumour-reactive T cells and TCRs could be isolated based on the expression of activation markers. However, since T cells with different cell states could not respond uniformly to activation but show a heterogeneous expression profile of activation and effector molecules, isolation of tumour-reactive T cells based on single activation or effector molecules could result in the absence of tumour-reactive T cells; thus, combinations of multiple activation and effector molecules could improve the efficiency of isolating tumour-specific TCRs. We enrolled two patients with lung adenocarcinoma and obtained their tumour infiltrating lymphocytes (TILs) and autologous tumour cells (ATCs). TILs were cocultured with the corresponding ATCs for 12 h and subjected to single-cell RNA sequencing. First, we identified three TCRs with the highest expression levels of IFNG and TNFRSF9 mRNA for each patient, yet only the top one or two recognized the corresponding ATCs in each patient. Next, we defined the activation score based on normalized expression levels of IFNG, IL2, TNF, IL2RA, CD69, TNFRSF9, GZMB, GZMA, GZMK, and PRF1 mRNA for each T cell and then identified three TCRs with the highest activation score for each patient. We found that all three TCRs in each patient could specifically identify corresponding ATCs. In conclusion, we established an efficient approach to isolate tumour-reactive TCRs based on combinations of multiple activation and effector molecules through single-cell RNA sequencing.

Comprehensive molecular analyses of cuproptosis-related genes with regard to prognosis, immune landscape, and response to immune checkpoint blockers in lung adenocarcinoma.

BACKGROUND: Recent studies have emphasized the importance of the biological processes of different forms of cell death in tumor heterogeneity and anti-tumor immunity. Nonetheless, the relationship between cuproptosis and lung adenocarcinoma (LUAD) remains largely unexplored. METHODS: Data for 793 LUAD samples and 59 normal lung tissues obtained from TCGA-LUAD cohort GEO datasets were used in this study. A total of 165 LUAD tissue samples and paired normal lung tissue samples obtained from our hospital were used to verify the prognostic value of dihydrolipoamide S-acetyltransferase (DLAT) and dihydrolipoamide branched chain transacylase E2 (DBT) for LUAD. The cuproptosis-related molecular patterns of LUAD were identified using consensus molecular clustering. Recursive feature elimination with random forest and a tenfold cross-validation method was applied to construct the cuproptosis score (CPS) for LUAD. RESULTS: Bioinformatic and immunohistochemistry (IHC) analyses revealed that 13 core genes of cuproptosis were all significantly elevated in LUAD tissues, among which DBT and DLAT were associated with poor prognosis (DLAT, HR = 6.103; DBT, HR = 4.985). Based on the expression pattern of the 13 genes, two distinct cuproptosis-related patterns have been observed in LUAD: cluster 2 which has a relatively higher level of cuproptosis was characterized by immunological ignorance; conversely, cluster 1 which has a relatively lower level of cuproptosis is characterized by TILs infiltration and anti-tumor response. Finally, a scoring scheme termed the CPS was established to quantify the cuproptosis-related pattern and predict the prognosis and the response to immune checkpoint blockers of each individual patient with LUAD. CONCLUSION: Cuproptosis was found to influence tumor microenvironment (TME) characteristics and heterogeneity in LUAD. Patients with a lower CPS had a relatively better prognosis, more abundant immune infiltration in the TME, and an enhanced response to immune checkpoint inhibitors.

NGEF is a potential prognostic biomarker and could serve as an indicator for immunotherapy and chemotherapy in lung adenocarcinoma.

BACKGROUND: Neuronal guanine nucleotide exchange factor (NGEF) plays a key role in several cancers; however, its role in lung adenocarcinoma (LUAD) remains unclear. The aim of this study was to evaluate the efficacy of NGEF as a prognostic biomarker and potential therapeutic target for LUAD. METHODS: NGEF expression data for multiple cancers and LUAD were downloaded from multiple databases. The high- and low-NGEF expression groups were constructed based on median NGEF expression in LUAD samples, and then performed Kaplan-Meier survival analysis. Differentially expressed genes (DEGs) from the two NGEF expression groups were screened and applied to construct a protein-protein interaction network. The primary pathways were obtained using gene set enrichment analysis. The associations between NGEF expression and clinical characteristics, immune infiltration, immune checkpoint inhibitors (ICIs), sensitivity to chemotherapy, and tumor mutation burden (TMB) were investigated using R. Levels of NGEF expression in the lung tissue was validated using single-cell RNA sequencing, quantitative polymerase chain reaction (qPCR), immunohistochemical staining, and western blot analysis. RESULTS: The expression of NGEF mRNA was upregulated in multiple cancers. mRNA and protein expression levels of NGEF were higher in patients with LUAD than in controls, as validated using qPCR and western blot. High NGEF expression was an independent prognostic factor for LUAD and was associated with advanced tumor stage, large tumor size, more lymph node metastasis, and worse overall survival (OS). A total of 182 overlapping DEGs were screened between The Cancer Genome Atlas and GSE31210, among which the top 20 hub genes were identified. NGEF expression was mainly enriched in the pathways of apoptosis, cell cycle, and DNA replication. Moreover, elevated NGEF expression were associated with a high fraction of activated memory CD4+ T cells and M0 macrophages; elevated expression levels of the ICIs: programmed cell death 1 and programmed cell death 1 ligand 1 expression; higher TMB; and better sensitivity to bortezomib, docetaxel, paclitaxel, and parthenolide, but less sensitivity to axitinib and metformin. CONCLUSION: NGEF expression is upregulated in LUAD and is significantly associated with tumor stages, OS probability, immune infiltration, immunotherapy response, and chemotherapy response. NGEF may be a potential diagnostic and prognostic biomarker and therapeutic target in LUAD.

Prognostic and immunotherapeutic implications of bilirubin metabolism-associated genes in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major subtype of non-small-cell lung cancer and accompanies high mortality rates. While the role of bilirubin metabolism in cancer is recognized, its specific impact on LUAD and patient response to immunotherapy needs to be elucidated. This study aimed to develop a prognostic signature of bilirubin metabolism-associated genes (BMAGs) to predict outcomes and efficacy of immunotherapy in LUAD. We analysed gene expression data from The Cancer Genome Atlas (TCGA) to identify survival-related BMAGs and construct a prognostic model in LUAD. The prognostic efficacy of our model was corroborated by employing TCGA-LUAD and five Gene Expression Omnibus datasets, effectively stratifying patients into risk-defined cohorts with marked disparities in survival. The BMAG signature was indeed an independent prognostic determinant, outperforming established clinical parameters. The low-risk group exhibited a more favourable response to immunotherapy, highlighted by increased immune checkpoint expression and immune cell infiltration. Further, somatic mutation profiling differentiated the molecular landscapes of the risk categories. Our screening further identified potential drug candidates preferentially targeting the high-risk group. Our analysis of critical BMAGs showed the tumour-suppressive role of FBP1, highlighting its suppression in LUAD and its inhibitory effects on tumour proliferation, migration and invasion, in addition to its involvement in cell cycle and apoptosis regulation. These findings introduce a potent BMAG-based prognostic indicator and offer valuable insights for prognostication and tailored immunotherapy in LUAD.

Down-regulation of KLRB1 is associated with increased cell growth, metastasis, poor prognosis, as well as a dysfunctional immune microenvironment in LUAD.

Killer cell lectin-like receptor B1 (KLRB1) is implicated in cancer progression and immunity. In this study, we aimed to evaluate the expression levels of KLRB1 in lung adenocarcinoma (LUAD) and analyze the relationship between KLRB1 expression levels, LUAD progression, and the tumor immune microenvironment. KLRB1 levels in LUAD were analyzed using data from the TCGA and XENA databases. Additionally, the diagnostic values of KLRB1 were analyzed in patients with LUAD. Survival and meta-analyses were employed to investigate the relationship between KLRB1 levels and other prognostic factors in patients with LUAD. Bioinformatics and cellular experiments were used to understand the functions and mechanisms of KLRB1. In addition, correlation analysis was used to investigate the relationship between KLRB1 levels and the immune microenvironment in LUAD. Reduced KLRB1 expression in LUAD was found to positively correlate with tumor size, distant metastasis, pathological stage, age, overall survival, diagnostic value, and disease-specific survival in patients with LUAD (P < 0.05). Conversely, increased KLRB1 expression was found to positively correlate with the overall survival and disease-specific survival in patients with LUAD (P < 0.05). We also found that the overexpression of KLRB1 can inhibit the proliferation, migration, and invasion of LUAD cells and promote apoptosis. KLRB1 was involved in immune cell differentiation, NF-kB, PD-L1, and PD-1 checkpoint pathways and others. Additionally, KLRB1 expression was linked to tumor purity, stromal, immune, and estimate scores, the levels of immune cells including B cells, CD8+ T cells, and CD4+ T cells, and immune cell markers in LUAD. Reduced KLRB1 expression has a significant positive correlation with diagnosis, poor prognosis, and immunity to cancer in patients with LUAD. KLRB1 inhibited cell proliferation and migration in patients with LUAD. These results suggest that KLRB1 may serve as a potential therapeutic target in patients with LUAD.

Associations between immune cell phenotypes and lung cancer subtypes: insights from mendelian randomization analysis.

INTRODUCTION: Lung cancer is a common malignant tumor, and different types of immune cells may have different effects on the occurrence and development of lung cancer subtypes, including lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). However, the causal relationship between immune phenotype and lung cancer is still unclear. METHODS: This study utilized a comprehensive dataset containing 731 immune phenotypes from the European Bioinformatics Institute (EBI) to evaluate the potential causal relationship between immune phenotypes and LUSC and LUAD using the inverse variance weighted (IVW) method in Mendelian randomization (MR). Sensitivity analyses, including MR-Egger intercept, Cochran Q test, and others, were conducted for the robustness of the results. The study results were further validated through meta-analysis using data from the Transdisciplinary Research Into Cancer of the Lung (TRICL) data. Additionally, confounding factors were excluded to ensure the robustness of the findings. RESULTS: Among the final selection of 729 immune cell phenotypes, three immune phenotypes exhibited statistically significant effects with LUSC. CD28 expression on resting CD4 regulatory T cells (OR 1.0980, 95% CI: 1.0627-1.1344, p < 0.0001) and CD45RA + CD28- CD8 + T cell %T cell (OR 1.0011, 95% CI: 1.0007; 1.0015, p < 0.0001) were associated with increased susceptibility to LUSC. Conversely, CCR2 expression on monocytes (OR 0.9399, 95% CI: 0.9177-0.9625, p < 0.0001) was correlated with a decreased risk of LUSC. However, no significant causal relationships were established between any immune cell phenotypes and LUAD. CONCLUSION: This study demonstrates that specific immune cell types are associated with the risk of LUSC but not with LUAD. While these findings are derived solely from European populations, they still provide clues for a deeper understanding of the immunological mechanisms underlying lung cancer and may offer new directions for future therapeutic strategies and preventive measures.

An exosomes-related lncRNA prognostic model correlates with the immune microenvironment and therapy response in lung adenocarcinoma.

Recent research highlights the significance of exosomes and long noncoding RNAs (lncRNAs) in cancer progression and drug resistance, but their role in lung adenocarcinoma (LUAD) is not fully understood. We analyzed 121 exosome-related (ER) mRNAs from the ExoBCD database, along with mRNA and lncRNA expression profiles of TCGA-LUAD using "DESeq2", "survival," "ConsensusClusterPlus," "GSVA," "estimate," "glmnet," "clusterProfiler," "rms," and "pRRophetic" R packages. This comprehensive approach included univariate cox regression, unsupervised consensus clustering, GSEA, functional enrichment analysis, and prognostic model construction. Our study identified 134 differentially expressed ER-lncRNAs, with 19 linked to LUAD prognosis. These ER-lncRNAs delineated two patient subtypes, one with poorer outcomes. Additionally, 286 differentially expressed genes were related to these ER-lncRNAs, 261 of which also correlated with LUAD prognosis. We constructed an ER-lncRNA-related prognostic model and calculated an ER-lncRNA-related risk score (ERS), revealing that a higher ERS correlates with poor overall survival in both the Meta cohort and two validation cohorts. The ERS potentially serves as an independent prognostic factor, and the prognostic model demonstrates superior predictive power. Notably, significant differences in the immune landscape were observed between the high- and low-ERS groups. Drug sensitivity analysis indicated varying responses to common chemotherapy drugs based on ERS stratification, with the high-ERS group showing greater sensitivity, except to rapamycin and erlotinib. Experimental validation confirmed that thymidine kinase 1 enhances lung cancer invasion, metastasis, and cell cycle progression. Our study pioneers an ER-lncRNA-related prognostic model for LUAD, proposing that ERS-based risk stratification could inform personalized treatment strategies to improve patient outcomes.

Anlotinib may enhance the efficacy of anti-PD1 therapy by inhibiting the AKT pathway and promoting the apoptosis of CAFs in lung adenocarcinoma.

Although PD-1 inhibitors have revolutionized the treatment paradigm of non-small cell lung cancer (NSCLC), their efficacy in treating NSCLC has remained unsatisfactory. Targeting cancer-associated fibroblasts (CAFs) is a potential approach for improving the immunotherapy response. Multitarget antiangiogenic tyrosine kinase receptor inhibitors (TKIs) can enhance the efficacy of PD-1 inhibitors in NSCLC patients. However, the effects and mechanisms of antiangiogenic TKIs on CAFs have not been elucidated. In this study, we first compared anlotinib with other antiangiogenic TKIs and confirmed the superior efficacy of anlotinib. Furthermore, we established NSCLC-associated CAF models and found that anlotinib impaired CAF viability and migration capacity and contributed to CAF apoptosis and cell cycle arrest in the G2/M phase. Moreover, anlotinib treatment attenuated the capacity of CAFs to recruit lung cancer cells and macrophages. Experiments in animal models suggested that anlotinib could enhance the efficacy of anti-PD1 therapy in NSCLC and affect CAF proliferation and apoptosis. Anlotinib increased the abundance of tumor-infiltrating CD8 + T cells, and PD-1 inhibitor-induced cytotoxicity to tumor cells was achieved through the transformation of the tumor microenvironment (TME) caused by anlotinib, which may partly explain the synergistic antitumor effect of anlotinib and PD-1 inhibitors. Mechanistically, anlotinib affects CAF apoptosis and cell viability at least in part by inhibiting the AKT pathway. In conclusion, our study suggested that anlotinib could regulate the TME, inhibit the AKT pathway and promote CAF apoptosis, providing new insights into the antitumor effect of anlotinib and improving the efficacy of immunotherapy.

Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD.

Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081. The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters). Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan-Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively.

Implications of intestinal microecology and immune function alterations for immunotherapy outcomes in advanced unresectable lung adenocarcinoma.

OBJECTIVE: This investigation aims to explore alterations in intestinal microecology and immune function among patients with advanced, unresectable lung adenocarcinoma undergoing different outcomes from immunotherapy. METHODS: A cohort of 30 patients diagnosed with advanced unresectable lung adenocarcinoma received sintilimab immunotherapy as a monotherapy. Post four treatment cycles, efficacy was assessed, leading to the segregation of patients into two distinct cohorts: those responsive to treatment and those nonresponsive. Analysis involved observing variations in the abundance, distribution, and composition of fecal intestinal microorganisms pretreatment and posttreatment via 16S rRNA gene sequencing. RESULTS: In this study involving 30 advanced lung adenocarcinoma patients, significant observations were made regarding the impact of immunotherapy on immune function and the gut microbiome composition. Patients were divided into treatment and control groups, revealing that immunotherapy led to a significant increase in CD4+ T cells and a decrease in CD8+ T cells among the treatment-responsive individuals, indicating an enhanced immune response. Furthermore, an in-depth analysis of the gut microbiome showed an increase in diversity and abundance of beneficial bacteria such as Faecalibacterium and Subdoligranulum in the treatment group. These findings highlight the dual effect of immunotherapy on modulating immune function and altering gut microbiome diversity, suggesting its potential therapeutic benefits in improving the health status of patients with advanced lung adenocarcinoma. CONCLUSION: The structuring of gut flora plays a pivotal role in augmenting the efficacy of anti-tumor immunotherapy, underscoring the interplay between intestinal microecology and immune response in cancer treatment outcomes.

Local therapy combined with anti-PD-1 immunotherapy for advanced lung adenocarcinoma: A case report.

ABSTRACT: Lung adenocarcinoma (LUAD) encompasses all lung epithelial cancers except small-cell lung cancer. Although programmed cell death protein 1 (PD-1) inhibitors, such as pembrolizumab, and other Food and Drug Administration-approved immune checkpoint inhibitors, offer new hope for LUAD treatment, LUAD's overall efficacy remains limited. Thus, the combination of immunotherapy with other therapeutic approaches has gained widespread attention. Local therapy is an optimal method for treating many advanced unresectable lung cancers. Herein, we present a case of a patient with multiple metastases from LUAD, who attained complete response for more than 3 years until present through local therapy combined with a PD-1 inhibitor.

Stem-like CD8 T cells in stage I lung adenocarcinoma as a prognostic biomarker: A preliminary study.

OBJECTIVES: This study aimed to investigate the presence of stem-like CD8 T (CD8 TSL) cells in lung adenocarcinoma (LUAD) and explore their relationships with the clinical outcomes. METHODS: Multiplex immunofluorescence (mIF) was performed to identify CD8 TSL and antigen-presenting cells (APC) in 76 LUAD patients. Differences in the number of CD8 TSL cells based on tumor stage and the spatial relationships between CD8 TSL cells and APC niches were determined. The optimal cutoff value of CD8 TSL cells for predicting survival in patients with stage I LUAD was calculated. RESULTS: CD8 TSL cells were present in all tumors, and their numbers were significantly higher in stage I patients than in stage III patients (P = 0.010); CD8 TSL cells located in the APC niches accounted for 69.7% (53/76) of the hotspot fields. The optimal cutoff value for the number of CD8 TSL cells required to predict the overall survival (OS) in patients with stage I LUAD was 2.5 per 10000 µm2. The median OS and progression-free survival (PFS) in the high-level group (>2.5) were significantly (P < 0.001) longer than those in the low-level group (≤2.5). The number of CD8 TSL cells was an independent prognostic factor for stage I LUAD. Patients with more CD8 TSL cells had a lower risk of death and disease progression than those with less CD8 TSL cells. CONCLUSION: CD8 TSL cells were observed in patients with stages I-III LUAD and might serve as prognostic biomarkers for stage I LUAD.

Prognostic Cell Death Index for Lung Adenocarcinoma: A Comprehensive Transcriptome-Based Analysis of Twelve Programmed Cell Death Pattern Genes.

OBJECTIVE: Lung adenocarcinoma (LUAD) is a prominent contributor to global cancer mortality, characterized by constrained prognosis. This study aimed to develop a novel prognostic indicator, the Cell Death Index (CDI), utilizing twelve programmed cell death (PCD) pattern genes, to predict the immune infiltration and prognosis in LUAD patients. METHODS: We collected PCD-related genes and identified prognostic PCD genes in the Cancer Genome Atlas (TCGA)-LUAD dataset, and made rigorous validation in the Clinical Proteomic Tumor Analysis Consortium (CPTAC)-LUAD cohorts. CDI was calculated using a multivariable Cox regression model. Functional enrichment and tumor microenvironment were evaluated. Drug sensitivity prediction and nomogram development were performed to assess CDI's potential value. RESULTS: The results revealed 10 PCD genes (ERO1A, CDK5R1, TRIM6, DNASE2B, ITPRIP, MRGPRX2, FGA, NDUFA13, NLRP2, and CD68) significantly associated with LUAD prognosis. The CDI was constructed and showed high accuracy in predicting patient survival with C-index values of 0.801 and 0.794 in the prognosis cohort and validation cohort, respectively. CDI is also indicative of variations in biological functions, tumor microenvironment, and immune cell infiltration including neutrophils, activated mast cells, activated dendritic cells, M0 macrophages, resting natural killer cells, γδT cells, and activated memory CD4+T cells. Furthermore, drug sensitivity analysis indicated potential targeted strategies. CONCLUSIONS: The CDI, based on PCD genes, serves as a robust prognostic tool for LUAD, offering profound insights into tumor biology, immune response, and personalized treatment strategies. This study underscores the pivotal role of PCD mechanisms in LUAD pathogenesis and identifies potential therapeutic targets.

Identifying an immunogenic cell death-related gene signature contributes to predicting prognosis, immunotherapy efficacy, and tumor microenvironment of lung adenocarcinoma.

BACKGROUND: Immunogenic cell death (ICD) is a regulated form of cell death that triggers an adaptive immune response. The objective of this study was to investigate the correlation between ICD-related genes (ICDGs) and the prognosis and the immune microenvironment of patients with lung adenocarcinoma (LUAD). METHODS: ICD-associated molecular subtypes were identified through consensus clustering. Subsequently, a prognostic risk model comprising 5 ICDGs was constructed using Lasso-Cox regression in the TCGA training cohort and further tested in the GEO cohort. Enriched pathways among the subtypes were analyzed using GO, KEGG, and GSVA. Furthermore, the immune microenvironment was assessed using ESTIMATE, CIBERSORT, and ssGSEA analyses. RESULTS: Consensus clustering divided LUAD patients into three ICDG subtypes with significant differences in prognosis and the immune microenvironment. A prognostic risk model was constructed based on 5 ICDGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of tumor purity. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor. The five hub genes were verified by TCGA database, cell sublocalization immunofluorescence analysis, IHC images and qRT-PCR, which were consistent with bioinformatics analysis. CONCLUSIONS: The molecular subtypes and a risk model based on ICDGs proposed in our study are both promising prognostic classifications in LUAD, which may provide novel insights for developing accurate targeted cancer therapies.