Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi-omics consensus clusters (MOCs). These data were then amalgamated with ten machine learning approaches to develop a robust model capable of reliably identifying patient prognosis and predicting immunotherapy outcomes. Through ten clustering algorithms, two prognostically relevant MOCs were identified, with MOC2 showing more favourable outcomes. We subsequently constructed a MOCs-associated machine learning model (MOCM) based on eight MOCs-specific hub genes. Patients characterized by a lower MOCM score exhibited better overall survival and responses to immunotherapy. These findings were consistent across multiple datasets, and compared to many previously published LUAD biomarkers, our MOCM score demonstrated superior predictive performance. Notably, the low MOCM group was more inclined towards 'hot' tumours, characterized by higher levels of immune cell infiltration. Intriguingly, a significant positive correlation between GJB3 and the MOCM score (R = 0.77, p < 0.01) was discovered. Further experiments confirmed that GJB3 significantly enhances LUAD proliferation, invasion and migration, indicating its potential as a key target for LUAD treatment. Our developed MOCM score accurately predicts the prognosis of LUAD patients and identifies potential beneficiaries of immunotherapy, offering broad clinical applicability.

Revealing the role of regulatory T cells in the tumor microenvironment of lung adenocarcinoma: a novel prognostic and immunotherapeutic signature.

Background: Regulatory T cells (Tregs), are a key class of cell types in the immune system. In the tumor microenvironment (TME), the presence of Tregs has important implications for immune response and tumor development. Relatively little is known about the role of Tregs in lung adenocarcinoma (LUAD). Methods: Tregs were identified using but single-cell RNA sequencing (scRNA-seq) analysis and interactions between Tregs and other cells in the TME were investigated. Next, we used multiple bulk RNA-seq datasets to construct risk models based on marker genes of Tregs and explored differences in prognosis, mutational landscape, immune cell infiltration and immunotherapy between high- and low-risk groups, and finally, qRT-PCR and cell function experiments were performed to validate the model genes. Results: The cellchat analysis showed that MIF-(CD74+CXCR4) pairs play a key role in the interaction of Tregs with other cell subpopulations, and the Tregs-associated signatures (TRAS) could well classify multiple LUAD cohorts into high- and low-risk groups. Immunotherapy may offer greater potential benefits to the low-risk group, as indicated by their superior survival, increased infiltration of immune cells, and heightened expression of immune checkpoints. Finally, the experiment verified that the model genes LTB and PTTG1 were relatively highly expressed in cancer tissues, while PTPRC was relatively highly expressed in paracancerous tissues. Colony Formation assay confirmed that knockdown of PTTG1 reduced the proliferation ability of LUAD cells. Conclusion: TRAS were constructed using scRNA-seq and bulk RNA-seq to distinguish patient risk subgroups, which may provide assistance in the clinical management of LUAD patients.

Preoperative systemic immune-inflammation index for predicting the prognosis of thymoma with radical resection.

BACKGROUND: To investigate the clinical significance of preoperative systemic immune-inflammation index (SII) in patients with thymoma who underwent radical resection. METHODS: This retrospective study involved 425 patients with thymoma who underwent radical resection at the First Affiliated Hospital of Nanjing Medical University between September 1, 2008 and December 30, 2019. Data regarding routine preoperative blood tests and clinical features were collected to calculate and analyze the SII, platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR). RESULTS: Univariate analysis indicated that age (p = 0.021), tumor size (p = 0.003), extended resection (p < 0.001), Masaoka-Koga stage (p < 0.001), PLR (p = 0.012), NLR (p = 0.041), and SII (p = 0.003) were related to patient prognosis. A higher SII (>345.83) was a significant independent prognostic factor in this cohort (p = 0.001, HR = 5.756, 95% CI: 2.144-15.457). Multivariate analysis showed that a high PLR was significantly associated with overall survival (OS) (p = 0.008, HR = 3.29, 95% CI: 1.371-7.896), while a high NLR was a significant independent prognostic factor for shorter OS (p = 0.024, HR = 2.654, 95% CI: 1.138-6.19). SII had an area under the curve (AUC) of 70.6% (AUC = 0.706) exceeding the predictive value for PLR (AUC = 0.678) and NLR (AUC = 0.654). CONCLUSION: Preoperative SII can predict the prognosis of thymoma patients who have undergone radical resection but further multicenter prospective studies are needed to investigate the role of SII in thymoma.

Immune Landscape and Classification in Lung Adenocarcinoma Based on a Novel Cell Cycle Checkpoints Related Signature for Predicting Prognosis and Therapeutic Response.

Lung adenocarcinoma (LUAD) is one of the most common malignancies with the highest mortality globally, and it has a poor prognosis. Cell cycle checkpoints play a central role in the entire system of monitoring cell cycle processes, by regulating the signalling pathway of the cell cycle. Cell cycle checkpoints related genes (CCCRGs) have potential utility in predicting survival, and response to immunotherapies and chemotherapies. To examine this, based on CCCRGs, we identified two lung adenocarcinoma subtypes, called cluster1 and cluster2, by consensus clustering. Enrichment analysis revealed significant discrepancies between the two subtypes in gene sets associated with cell cycle activation and tumor progression. In addition, based on Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we have developed and validated a cell cycle checkpoints-related risk signature to predict prognosis, tumour immune microenvironment: (TIME), immunotherapy and chemotherapy responses for lung adenocarcinoma patients. Results from calibration plot, decision curve analysis (DCA), and time-dependent receiver operating characteristic curve (ROC) revealed that combining age, gender, pathological stages, and risk score in lung adenocarcinoma patients allowed for a more accurate and predictive nomogram. The area under curve for lung adenocarcinoma patients with 1-, 3-, 5-, and 10-year overall survival was: 0.74, 0.73, 0.75, and 0.81, respectively. Taken together, our proposed 4-CCCRG signature can serve as a clinically useful indicator to help predict patients outcomes, and could provide important guidance for immunotherapies and chemotherapies decision for lung adenocarcinoma patients.

Identification of a Pyroptosis-Related Gene Signature for Predicting the Immune Status and Prognosis in Lung Adenocarcinoma.

Background: Pyroptosis is a form of programmed cell death triggered by the rupture of cell membranes and the release of inflammatory substances; it is essential in the occurrence and development of cancer. A considerable number of studies have revealed that pyroptosis is closely associated to the biological process of several cancers. However, the role of pyroptosis in lung adenocarcinoma (LUAD) remains elusive. The purpose of this study was to explore the prognostic role of pyroptosis-related genes (PRGs) and their relationship with the tumor immune microenvironment (TIME) in LUAD. Methods: Gene expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prognostic PRG signature was established in the training set and verified in the validation sets. Functional enrichment and immune microenvironment analyses related to PRGs were performed and a nomogram based on the risk score and clinical characteristics was established. What is more, quantitative real-time PCR (qRT-PCR) analysis was applied in order to verify the potential biomarkers for LUAD. Results: A prognostic signature based on five PRGs was constructed to separate LUAD patients into two risk groups. Patients in the high-risk group had worse prognoses than those in the low-risk group. The signature was identified as independent via Cox regression analyses and obtained the largest area under the curve (AUC = 0.677) in the receiver operating characteristic (ROC). Functional enrichment and immune microenvironment analyses demonstrated that the immune status was significantly different in the two subgroups and that immunotherapy may be effective for the high-risk group. Furthermore, qRT-PCR analysis verified that serum PRKACA and GPX4 could serve as diagnostic biomarkers for LUAD. Conclusion: Overall, a risk signature based on five PRGs was generated, providing a novel perspective on the determinants of prognosis and survival in LUAD, as well as a basis for the development of individualized regimes.

A novel signature based on autophagy-related lncRNA for prognostic prediction and candidate drugs for lung adenocarcinoma.

Background: Autophagy inhibits tumorigenesis by limiting inflammation. Various lncRNAs are associated with tumour biological processes, including lung adenocarcinoma (LUAD), but the role of autophagy-related lncRNAs (ARlncRNAs) in LUAD has not been fully elucidated. Thus, this study aimed to construct a prognostic signature based on ARlncRNAs for LUAD. Methods: The RNA-seq (FPKM) data and clinical information of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database. After differentially expressed lncRNAs in tumour and normal groups were identified, cox regression analyses were performed to construct a prognostic signature which was then assessed through independent prognostic analysis and functional enrichment analysis. Moreover, based on the mRNAs co-expressed with the ARlncRNAs, several potential small molecule drugs were explored in the Connectivity map (Cmap). Results: A signature consisting of seven ARlncRNAs (FAM83A-AS1, LINC01116, ILF3-DT, EBLN3P, AL161785.1, AC092279.1 and AC026355.2) was constructed to predict overall survival (OS) for LUAD. The signature was identified to be independent by the cox regression analysis and obtained the largest area under the curve (AUC =0.721) in the receiver operating characteristic (ROC). Six small molecule drugs (MS-275, methotrexate, desipramine, benzbromarone, rifampicin and doxazosin) were selected from Cmap. Conclusions: A novel ARlncRNA signature for LUAD prognostic prediction was constructed, which had better efficacy than the TNM stage and used to propose potential therapeutic regimens for LUAD patients.

A pan-cancer analysis revealed the role of the SLC16 family in cancer.

Cancer is one of the serious diseases that endanger human health and bring a heavy burden to world economic development. Although the current targeted therapy and immunotherapy have achieved initial results, the emergence of drug resistance shows that the existing research is far from enough. In recent years, the tumor microenvironment has been found to be an important condition for tumor development and has profound research value. The SLC16 family is a group of monocarboxylic acid transporters involved in cancer metabolism and the formation of the tumor microenvironment. However, there have been no generalized cancer studies in the SLC16 family. In this study, we conducted a pan-cancer analysis of the SLC16 family. The results showed that multiple members of the SLC16 family could be used as prognostic indicators for many tumors, and were associated with immune invasion and tumor stem cells. Therefore, the SLC16 family has extensive exploration value in the future.