Tumor Mutation Research Articles

Unique immune characteristics and differential anti-PD-1-mediated reinvigoration potential of CD8+ TILs based on BRCA1/2 mutation status in epithelial ovarian cancers

BackgroundWe aimed to investigate the distinct immunological characteristics of the tumor immune microenvironment in epithelial ovarian cancer (EOC) according to BRCA1/2 mutations status and differential PD-1 expression levels.MethodsTumor-infiltrating lymphocytes (TILs)...

Prognostic signature based on S100 calcium-binding protein family members for lung adenocarcinoma and its clinical significance

The S100 family proteins (S100s) participate in multiple stages of tumorigenesis and are considered to have potential value as biomarkers for detecting and predicting various cancers. But the role of S100s in lung adenocarcinoma (LUAD) prognosis is elusive. Transcriptional data of LUAD patients were retrieved from TCGA, and relevant literature was extensively reviewed to collect S100 genes. Differential gene expression analysis was performed on the LUAD data, followed by intersection analysis between the differentially expressed genes (DEGs) and S100 genes. Unsupervised consensus clustering analysis identified two clusters. Significant variations in overall survival between the two clusters were shown by Kaplan-Meier analysis. DEGs between the two clusters were analyzed using Lasso regression and univariate/multivariate Cox regression analysis, leading to construction of an 11-gene prognostic signature. The signature exhibited stable and accurate predictive capability in TCGA and GEO datasets. Subsequently, we observed distinct immune cell infiltration, immunotherapy response, and tumor mutation characteristics in high and low-risk groups. Finally, small molecular compounds targeting prognostic genes were screened using CellMiner database, and molecular docking confirmed the binding of AMG-176, Estramustine, and TAK-632 with prognostic genes. In conclusion, we generated a prognostic signature with robust and reliable predictive ability, which may provide guidance for prognosis and treatment of LUAD.

Evolving Management of Breast Cancer in the Era of Predictive Biomarkers and Precision Medicine.

Breast cancer is the most common cancer among women in the world as well as in the United States. Molecular and histological differentiation have helped clinicians optimize treatments with various therapeutics, including hormonal therapy, chemotherapy, immunotherapy, and radiation therapy. Recently, immunotherapy has become the standard of care in locally advanced triple-negative breast cancer and an option across molecular subtypes for tumors with a high tumor mutation burden. Despite the advancements in personalized medicine directing the management of localized and advanced breast cancers, the emergence of resistance to these therapies is the leading cause of death among breast cancer patients. Therefore, there is a critical need to identify and validate predictive biomarkers to direct treatment selection, identify potential responders, and detect emerging resistance to standard therapies. Areas of active scientific and clinical research include novel personalized and predictive biomarkers incorporating tumor microenvironment, tumor immune profiling, molecular characterization, and histopathological differentiation to predict response and the potential emergence of resistance.

Clinical performance of the TrueMark™ MSI assay for microsatellite instability detection in a Chinese colorectal cancer cohort

BackgroundMicrosatellite instability (MSI) and mismatch repair (MMR) detection is valuable in assessing prognosis and treatment options. However, the conventional detection methods such as immunohistochemistry (IHC) are limited by not fully consistent results as well as a long turnaround time. TrueMark™ MSI Assay is a novel solution for MSI analysis, but lack of research support in the Chinese colorectal cancer (CRC) patients. Materials and methods60 dMMR and 60 pMMR CRC samples identified by IHC were collected and their MSI status were detected using TrueMark™ MSI assay with an expanded panel of 13 markers. The overall performance and diagnostic concordance between TrueMark™ MSI test and MMR IHC analysis were assessed and analyzed. ResultsAccording to the TrueMark™ test, 55 out of the 120 (45.8 %) CRCs were identified as MSI-high (MSI-H) with an instability at ≥ 4/13 markers. Compared with the MMR IHC analysis, an overall percent agreement of 94.2 % and a Kappa of 0.883 were achieved. For the seven inconsistent samples, tumor mutation burden analysis was performed and the results supported the diagnosis by TrueMark™ test. To confirm the robustness of the above findings, a validation was performed in an independent cohort comprising 51 consecutive CRCs. Furthermore, an optimized panel composed of NR-21, NR-24, NR-27, ABI-16, ABI-17 and ABI-20B was developed by multivariate logistic regression model, and showed 100 % agreement with the 13-marker panel for MSI detection in both the derivation and validation sets. ConclusionTrueMark™ MSI provides a fast, reliable and highly automated solution to MSI detection in Chinese CRC patients, and the new 6-marker panel we established shows promise deserving further evaluation.

A prognostic model for anoikis-related genes in pancreatic cancer

Anoikis, a distinct form of programmed cell death, is crucial for both organismal development and maintaining tissue equilibrium. Its role extends to the proliferation and progression of cancer cells. This study aimed to establish an anoikis-related prognostic model to predict the prognosis of pancreatic cancer (PC) patients. Gene expression data and patient clinical profiles were sourced from The Cancer Genome Atlas (TCGA-PAAD: Pancreatic Adenocarcinoma) and the International Cancer Genome Consortium (ICGC-PACA: Pancreatic Ductal Adenocarcinoma). Non-cancerous pancreatic tissue gene expression data were obtained from the Genotype-Tissue Expression (GTEx) project. The R package was used to construct anoikis-related PC prognostic models, which were later validated with the ICGC-PACA database. Survival analyses demonstrated a poorer prognosis for patients in the high-risk group, consistent across both TCGA-PAAD and ICGC-PACA datasets. A nomogram was designed as a predictive tool to estimate patient mortality. The study also analyzed tumor mutations and immune infiltration across various risk groups, uncovering notable differences in tumor mutation patterns and immune landscapes between high- and low-risk groups. In conclusion, this research successfully developed a prognostic model centered on anoikis-related genes, offering a novel tool for predicting the clinical trajectory of PC patients.

The cuproptosis-related signature predicts the prognosis and immune microenvironments of primary diffuse gliomas: a comprehensive analysis

BackgroundEvidence has revealed a connection between cuproptosis and the inhibition of tumor angiogenesis. While the efficacy of a model based on cuproptosis-related genes (CRGs) in predicting the prognosis of peripheral organ tumors has been demonstrated, the impact of CRGs on the prognosis and the immunological landscape of gliomas remains unexplored.MethodsWe screened CRGs to construct a novel scoring tool and developed a prognostic model for gliomas within the various cohorts. Afterward, a comprehensive exploration of the relationship between the CRG risk signature and the immunological landscape of gliomas was undertaken from multiple perspectives.ResultsFive genes (NLRP3, ATP7B, SLC31A1, FDX1, and GCSH) were identified to build a CRG scoring system. The nomogram, based on CRG risk and other signatures, demonstrated a superior predictive performance (AUC of 0.89, 0.92, and 0.93 at 1, 2, and 3 years, respectively) in the training cohort. Furthermore, the CRG score was closely associated with various aspects of the immune landscape in gliomas, including immune cell infiltration, tumor mutations, tumor immune dysfunction and exclusion, immune checkpoints, cytotoxic T lymphocyte and immune exhaustion-related markers, as well as cancer signaling pathway biomarkers and cytokines.ConclusionThe CRG risk signature may serve as a robust biomarker for predicting the prognosis and the potential viability of immunotherapy responses. Moreover, the key candidate CRGs might be promising targets to explore the underlying biological background and novel therapeutic interventions in gliomas.

Development of a Novel Prognostic Panel for Colorectal Cancer Based on Cancer Functional Status, and Validation of STC2 as a Promising Biomarker.

Improving the clinical outcome of colorectal cancer (CRC) patients remains a major challenge. This study aimed to develop a new predictive classifier for CRC and to examine its relationship with the immune environment and therapeutic response. A comprehensive bioinformatics analysis was applied to develop a risk panel comprised of cancer function status-related genes (CFSRGs). This panel was evaluated for prognostic utility by Area Under the Curve (AUC) and Kaplan-Meier (KM) analyses. Differences between high- and low-risk groups were subsequently investigated using multi-omics data. Immunohistochemistry (IHC), quantitative real-time polymerase chain reaction (qRT-PCR), and cell phenotype assays were also employed to ascertain the clinical value of STC2 expression. Significant differences were observed in the survival rate between high- and low-risk groups defined by our 7-CFSRG panel, both in internal and external CRC patient cohorts. The AUC for prediction of survival at 1-, 3- and 5-years was satisfactory in all cohorts. Detailed analysis revealed that tumor mutation burden, drug sensitivity, and pathological stage were closely associated with the risk score. Elevated expression of STC2 in CRC tissues relative to normal paraneoplastic tissues was associated with less favorable patient outcomes. qRT-PCR experiments confirmed that STC2 expression was significantly upregulated in several CRC cell lines (HCT116, SW480, and LOVO) compared to a normal intestinal epithelial cell line (NCM460). The proliferation, migration, and invasion of CRC cells were all significantly inhibited by knockdown of STC2. Our 7-CFSRG panel is a promising classifier for assessing the prognosis of CRC patients. Moreover, the targeting of STC2 may provide a novel therapeutic approach for improving patient outcomes.

Plasma versus Tissue Tumor Mutational Burden as Biomarkers of Durvalumab plus Tremelimumab Response in Patients with Metastatic Colorectal Cancer in the CO.26 Trial.

Tissue-derived tumor mutation burden (TMB) of ≥10 mutations/Mb is a histology-agnostic biomarker for the immune checkpoint inhibitor (ICI) pembrolizumab. However, the dataset in which this was validated lacked colorectal cancers (CRC), and there is limited evidence for immunotherapy benefits in CRC using this threshold. CO.26 was a randomized phase II study of 180 patients, comparing durvalumab and tremelimumab (D + T, n = 119 patients) versus best supportive care (BSC; n = 61 patients). ctDNA sequencing was available for 168 patients (n = 118 D + T; n = 50), of whom 165 had evaluable plasma TMB (pTMB). Tissue sequencing was available for 108 patients. Optimal thresholds for stratifying patients based on OS were determined using a minimal P value approach. This report includes the final OS analysis. Tissue TMB ≥10 mutations/Mb was not predictive of benefit from D + T compared with BSC in microsatellite stable (MSS) metastatic CRC [HR, 0.71 (95% CI, 0.28-1.80); P = 0.47]. No tissue TMB threshold could identify a high TMB group that benefited from ICI. By contrast, plasma TMB (pTMB) ≥28 mutations/Mb was predictive of benefit from D + T [HR, 0.34 (95% CI, 0.13-0.85); P = 0.022], as was clonal pTMB ≥10.6 mutations/Mb [HR, 0.10 (95% CI, 0.014-0.79); P = 0.029] and subclonal pTMB ≥25.9/Mb [HR, 0.20 (95% CI, 0.061-0.69); P = 0.010]. Higher pTMB was associated with length of time on cytotoxic agents (P = 0.021) and prior anti-EGFR exposure (P = 2.44 × 10-06). pTMB derived from either clonal or subclonal mutations may identify a group likely to benefit from immunotherapy, although validation is required. Tissue TMB provided no predictive utility for immunotherapy in this trial.

TMB Signature-Related RCAN2 Promotes Apoptosis by Upregulating EHF/DR5 Pathway in Hepatocellular Carcinoma.

The tumour mutation burden (TMB) is a valuable indicator of the accumulation of somatic mutations, and is thought to be associated with the biological behaviour and prognosis of tumours. However, the related genetic mechanism for these association is still unclear. The aim of the present study was to identify the key gene(s) associated with TMB in hepatocellular carcinoma (HCC) and to investigate its biological functions, downstream transcription factors, and mechanism of action. Patients in The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database were classified according to TMB signature-related genes. Key genes related to the TMB signature and tumour prognosis were identified. Immunohistochemistry and Quantitative Real-Time Polymerase Chain Reaction (qPCR) were then used to assess gene expression in clinical HCC tissues and HCC cells. Cells with altered gene expression were evaluated for the effect on cell proliferation and apoptosis, both in vitro and in vivo. Three independent databases and cell sequencing data were used to identify the mechanisms involved and the downstream transcription factors. The mechanism was also studied by altering the expression of downstream transcription factors in vitro. The integrated cluster (IC) 2 group, characterized by 99 TMB signature-related genes, showed a significant different TMB score compared to the IC1 group (p < 0.001), as well as more favourable tumour prognosis (p = 0.031). We identified five key prognostic genes that were differentially expressed between IC2 and IC1 and were associated with overall survival. The expression of one of these key prognostic genes, RCAN2, was negatively correlated with TMB in 18 out of 33 tumour types examined. A high level of RCAN2 was correlated with better overall survival in HCC (p = 0.0009). Overexpression of RCAN2 enhanced apoptosis in vitro and in vivo, whereas knockdown of RCAN2 attenuated apoptosis. The mechanism by which RCAN2 promotes apoptosis may involve upregulation of the expression of ETS homologous factor (EHF) and of death receptor 5 (DR5). Downregulation of RCAN2 expression was found to correlate with elevated TMB in multiple cancer types. RCAN2 was also found to be a biomarker of HCC prognosis, and to promote the apoptosis of HCC cells through the EHF/DR5 pathway. These findings provide a new perspective on systemic treatment for advanced HCC with a high TMB.

Clinicopathologic and molecular characteristics of neuroendocrine carcinomas of the gallbladder.

Gallbladder neuroendocrine carcinomas (GB-NECs) are a rare subtype of malignant gallbladder cancer (GBC). The genetic and molecular characteristics of GB-NECs are rarely reported. This study aims to assess the frequency of microsatellite instability (MSI) in GB-NECs and characterize their clinicopathologic and molecular features in comparison with gallbladder adenocarcinomas (GB-ADCs). Data from six patients with primary GB-NECs and 13 with GB-ADCs were collected and reevaluated. MSI assay, immunohistochemistry for mismatch repair proteins (MLH1, MSH2, MSH6, and PMS2), comprehensive genomic profiling (CGP) via next-generation sequencing (NGS), and evaluation of tumor mutation burden (TMB) were conducted on these samples. The six GB-NEC cases were all female, with a mean age of 62.0±9.2 years. Of these, two cases were diagnosed as large cell neuroendocrine carcinomas (LCNECs), while the remaining four were small cell neuroendocrine carcinomas (SCNECs). Microsatellite states observed in both GB-NECs and GB-ADCs were consistently microsatellite stable (MSS). Notably, TP53 (100%, 6/6) and RB1 (100%, 6/6) exhibited the highest mutation frequency in GB-NECs, followed by SMAD4 (50%, 3/6), GNAS (50%, 3/6), and RICTOR (33%, 2/6), with RB1, GNAS, and RICTOR specifically present in GB-NECs. Immunohistochemical (IHC) assays of p53 and Rb in the six GB-NECs were highly consistent with genetic mutations detected by targeted NGS. Moreover, no statistical difference was observed in TMB between GB-NECs and GB-ADCs (P=0.864). Although overall survival in GB-NEC patients tended to be worse than in GB-ADC patients, this difference did not reach statistical significance (P=0.119). This study has identified the microsatellite states and molecular mutation features of GB-NECs, suggesting that co-mutations in TP53 and RB1 may signify a neuroendocrine inclination in GB-NECs. The IHC assay provides an effective complement to targeted NGS for determining the functional status of p53 and Rb in clinical practice.

Pan-cancer bioinformatics analysis of hepatic leukemia factor and further validation in colorectal cancer.

Hepatic leukemia factor (HLF) is associated with cancer onset, growth, and progression; however, little is known regarding its biological role in pan-cancer. In order to further evaluate the diagnostic and prognostic value of HLF in pan-cancer and colorectal cancer (CRC), we performed comprehensive bioinformatics analyses of the molecular mechanism of HLF in pan-cancer, with subsequent verification in CRC. We downloaded data (gene expression, clinical data, follow-up duration, and immune-related data) related to 33 solid tumor types from UCSC Xena (University of California Santa Cruz cancer database, https://xena.ucsc.edu/). HLF expression was analyzed in pan-cancer, and its diagnostic efficacy, prognostic value, and correlation with pathological stage and cancer immunity were determined. We also analyzed gene alterations in HLF and biological processes involved in its regulation in pan-cancer. Using CRC data in The Cancer Genome Atlas (TCGA), we assessed correlations between HLF and CRC diagnosis, prognosis, and drug sensitivity and performed functional enrichment analyses. Moreover, we constructed an HLF-related ceRNA regulatory network. Finally, we externally validated HLF expression and diagnostic and prognostic value in CRC using Gene Expression Omnibus (GEO) database, as well as by performing in vitro experiments. HLF expression was downregulated in most tumors, and HLF showed good predictive potential for pan-cancer diagnosis and prognosis. It was closely related to the clinicopathological stages of pan-cancer. Further, HLF was associated with tumor microenvironment and immune cell infiltration in many tumors. Analyses involving cBioPortal revealed changes in HLF amplifications and mutations in most tumors. HLF was also closely associated with microsatellite instability and tumor mutational burden in pan-cancer and involved in regulating various tumor-related pathways and biological processes. In CRC, HLF expression was similarly downregulated, with implications for CRC diagnosis and prognosis. Functional enrichment analysis indicated the association of HLF with many cancer-related pathways. Further, HLF was associated with drug (e.g., oxaliplatin) sensitivity in CRC. The ceRNA regulatory network showed multigene regulation of HLF in CRC. External validation involving GEO databases and quantitative real-time polymerase chain reaction (qRT-PCR) data substantiated these findings. HLF expression generally exhibited downregulation in pan-cancer, contributing to tumor occurrence and development by regulating various biological processes and affecting tumor immune characteristics. HLF was also closely related to CRC occurrence and development. We believe HLF can serve as a reliable diagnostic, prognostic, and immune biomarker for pan-cancer.

LncRNA CARMN m6A demethylation by ALKBH5 inhibits mutant p53-driven tumour progression through miR-5683/FGF2.

N-methyladenosine (m6A) represents a prevalent RNA modification observed in colorectal cancer. Despite its abundance, the biological implications of m6A methylation on the lncRNA CARMN remain elusive in colorectal cancer, especially for mutant p53 gain-of-function. Here, we elucidate that CARMN exhibits diminished expression levels in colorectal cancer patients with mutant p53, attributed to its rich m6A methylation, which promotes cancer proliferation, invasion and metastasis in vitro and in vivo. Further investigation illustrates that ALKBH5 acts as a direct demethylase of CARMN, targeting 477 methylation sites, thereby preserving CARMN expression. However, the interaction of mutant p53 with the ALKBH5 promoter impedes its transcription, enhancing m6A methylation levels on CARMN. Subsequently, YTHDF2/YTHDF3 recognise and degrade m6A-modified CARMN. Concurrently, overexpressing CARMN significantly suppressed colorectal cancer progression in vitro and in vivo. Additionally, miR-5683 was identified as a direct downstream target of lncRNA CARMN, exerting an antitumour effect by cooperatively downregulating FGF2 expression. Our findings revealed the regulator and functional mechanism of CARMN in colorectal cancer with mutant p53, potentially offering insights into demethylation-based strategies for cancer diagnosis and therapy. The m6A methylation of CARMN that is prime for mutant p53 gain-of-function-induced malignant progression of colorectal cancer, identifying a promising approach for cancer therapy.

Assessing the Sensitivity of Nested PCR Followed by Direct Sequencing on Exosomal DNA for EGFR Mutation Detection in NSCL

Early and minimally invasive detection of epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) patients is a promising tool to select patients for targeted therapy in order to improve their prognosis. This study aimed to identify a sensitive, cost-effective, and easily accessible noninvasive method for detecting the EGFR-targetable mutations in the plasma exosomal DNA (exoDNA)+ of patients with NSCLC. This retrospective observational study was conducted over 10 months, from December 2022 to October 2023, at Masih Daneshvari Hospital in Tehran, Iran. A total of 30 patients with stage II-IV NSCLC and targetable mutation in the EGFR gene were included in the study. Nested PCR and Sanger sequencing were used to evaluate EGFR mutations in the DNA extracted from circulating exosomes. The study found a sensitivity of 76.6% for EGFR mutation detection on exoDNA compared to tissue results. No significant impact was observed based on tumor staging, histology, mutation type, smoking status, gender, or age. Therapeutically targetable driver mutations in the EGFR gene can be accurately detected using nested PCR followed by direct sequencing of plasma exoDNA from patients with NSCLC. This approach facilitates timely and more personalized treatment for NSCLC patients, ultimately improving patient prognosis. Additionally, this method reduces the reliance on invasive tissue biopsies and their associated complications.

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cell datasets and using inferCNV, we identified and analysed tumour cell clusters to explore their heterogeneity and changes in abundance throughout LUAD progression. We applied gene set variation analysis (GSVA), pseudotime analysis, scMetabolism, and Cytotrace scores to study biological functions, metabolic profiles and stemness traits. A predictive model for prognosis, based on key cluster marker genes, was developed using CoxBoost and plsRcox (CPM), and validated across multiple cohorts for its prognostic prediction capabilities, tumour microenvironment characterization, mutation landscape and immunotherapy response. We identified nine distinct tumour cell clusters, with Cluster 6 indicating an early developmental stage, high stemness and proliferative potential. The abundance of Clusters 0 and 6 increased from AAH to IAC, correlating with prognosis. The CPM model effectively distinguished prognosis in immunotherapy cohorts and predicted genomic alterations, chemotherapy drug sensitivity, and immunotherapy responsiveness. Key gene S100A16 in the CPM model was validated as an oncogene, enhancing LUAD cell proliferation, invasion and migration. The CPM model emerges as a novel biomarker for predicting prognosis and immunotherapy response in LUAD patients, with S100A16 identified as a potential therapeutic target.

Machine learning developed an intratumor heterogeneity signature for predicting clinical outcome and immunotherapy benefit in bladder cancer.

Bladder cancer is a common malignancy with high invasion and poor clinical outcome. Intratumor heterogeneity (ITH) is linked to cancer progression and metastasis and high ITH can accelerate tumor evolution. Our objective is to develop an ITH-related signature (IRS) for predicting clinical outcome and immunotherapy benefit in bladder cancer. Integrative procedure containing ten machine learning methods was applied to develop an IRS with The Cancer Genome Atlas (TCGA), gene series expression (GSE)13507, GSE31684, GSE32984 and GSE48276 datasets. To evaluate the performance of IRS in predicting the immunotherapy benefit, we also used several predicting scores and three immunotherapy datasets, including GSE91061, GSE78220 and IMvigor210. The predicting model constructed with Enet (alpha =0.2) algorithm had a highest average C-index of 0.69, which was suggested as the optimal IRS. As an independent risk factor for bladder cancer, IRS had a powerful performance in predicting the overall survival (OS) rate of patients, with an area under curve of 1-, 3- and 5-year receiver operating characteristic (ROC) curve being 0.744, 0.791 and 0.816 in TCGA dataset. Bladder cancer patients with low IRS score presented with a higher level of immune-activated cells, cytolytic function and T cell co-stimulation. We also found a lower tumor immune dysfunction and exclusion (TIDE) score, lower immune escape score, higher programmed cell death protein 1 (PD-1) & cytotoxic T-lymphocyte associated protein 4 immunophenoscore, higher tumor mutation burden (TMB) score, higher response rate and better prognosis in bladder cancer with low IRS score. Bladder cancer cases with high IRS score had a higher half maximal inhibitory concentration value of common chemotherapy and targeted therapy regimens. The current study developed an optimal IRS for bladder cancer patients, which acted as an indicator for predicting prognosis, stratifying risk and guiding treatment for bladder cancer patients. Further analysis should be focused on the exploration the differentially expressed genes (DEGs) and related underlying mechanism mediating the development of bladder cancer in different IRS score group.

Characteristics of the immune environment in prostate cancer as an adjunct to immunotherapy.

The tumor microenvironment (TME) exerts an important role in carcinogenesis and progression. Several investigations have suggested that immune cell infiltration (ICI) is of high prognostic importance for tumor progression and patient survival in many tumors, particularly prostate cancer. The pattern of immune infiltration of PCa, on the other hand, has not been thoroughly understood. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets on PCa were obtained, and several datasets were merged into one data set using the "ComBat" algorithm. The ICI profiles of PCa patients were then to be uncovered by two computer techniques. The unsupervised clustering method was utilized to identify three ICI patterns in tumor samples, and Principal Component Analysis (PCA) was conducted to estimate the ICI score. Three different clusters of three ICIs were identified in 1341 PCa samples, which also correlated with different clinical features/characteristics and biological pathways. Patients with PCa are classified into high and low subtypes based on the ICI scores extracted from immune-associated signature genes. High ICI score subtypes are associated with a worse prognosis, which may intrigue the activation of cancer-related and immune-related pathways such as pathways involving Toll-like receptors, T-cell receptors, JAK-STAT, and natural killer cells. The ICI score was linked to tumor mutation load and immune/cancer-relevant signaling pathways, which explain prostate cancer's poor prognosis. The findings of this study not only advanced our knowledge of the mechanism of immune response in the prostate tumor microenvironment but also provided a novel biomarker, that is, the ICI score, for disease prognosis and guiding precision immunotherapy.

Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics.

The presence of lymph node (LN) metastasis directly affects the treatment strategy for lung adenocarcinoma (LUAD). Next-generation sequencing (NGS) has been widely used in patients with advanced LUAD to identify targeted genes, while early detection of pathologic LN metastasis using NGS has not been assessed. Clinicopathologic features and molecular characteristics of 224 patients from Ruijin Hospital were analyzed to detect factors associated with LN metastases. Another 140 patients from Huashan Hospital were set as a test cohort. Twenty-four out of 224 patients were found to have lymph node metastases (10.7%). Pathologic LN-positive tumors showed higher mutant allele tumor heterogeneity (p < 0.05), higher tumor mutation burden (p < 0.001), as well as more frequent KEAP1 (p = 0.001), STK11 (p = 0.004), KRAS (p = 0.007), CTNNB1 (p = 0.017), TP53, and ARID2 mutations (both p = 0.02); whereas low frequency of EGFR mutation (p = 0.005). A predictive nomogram involving male sex, solid tumor morphology, higher T stage, EGFR wild-type, and TP53, STK11, CDKN2A, KEAP1, ARID2, KRAS, SDHA, SPEN, CTNNB1, DICER1 mutations showed outstanding efficiency in both the training cohort (AUC = 0.819) and the test cohort (AUC = 0.780). This study suggests that the integration of genomic profiling and clinical features identifies early-invasive LUAD patients at higher risk of LN metastasis. Improved identification of LN metastasis is beneficial for the optimization of the patient's therapy decisions.

Leveraging single-cell sequencing analysis and bulk-RNA sequencing analysis to forecast necroptosis in cutaneous melanoma prognosis.

Cutaneous melanoma, a malignancy of melanocytes, presents a significant challenge due to its aggressive nature and rising global incidence. Despite advancements in treatment, the variability in patient responses underscores the need for further research into novel therapeutic targets, including the role of programmed cell death pathways such as necroptosis. The melanoma datasets used for analysis, GSE215120, GSE19234, GSE22153 and GSE65904, were downloaded from the GEO database. The melanoma data from TCGA were downloaded from the UCSC website. Using single-cell sequencing, we assess the heterogeneity of necroptosis in cutaneous melanoma, identifying distinct cell clusters and necroptosis-related gene expression patterns. A combination of 101 machine learning algorithms was employed to construct a necroptosis-related signature (NRS) based on key genes associated with necroptosis. The prognostic value of NRS was evaluated in four cohorts (one TCGA and three GEO cohorts), and the tumour microenvironment (TME) was analysed to understand the relationship between necroptosis, tumour mutation burden (TMB) and immune infiltration. Finally, we focused on the role of key target TSPAN10 in the prognosis, pathogenesis, immunotherapy relevance and drug sensitivity of cutaneous melanoma. Our study revealed significant heterogeneity in necroptosis among melanoma cells, with a higher prevalence in epithelial cells, myeloid cells and fibroblasts. The NRS, developed through rigorous machine learning techniques, demonstrated robust prognostic capabilities, distinguishing high-risk patients with poorer outcomes in all cohorts. Analysis of the TME showed that high NRS scores correlated with lower TMB and reduced immune cell infiltration, indicating a potential mechanism through which necroptosis influences melanoma progression. Finally, TSPAN10 has been identified as a key target for cutaneous melanoma and is highly associated with poor prognosis. The findings highlight the complex role of necroptosis in cutaneous melanoma and introduce the NRS as a novel prognostic tool with potential to guide therapeutic decisions.

Deciphering the role of cuproptosis-related lncRNAs in shaping the lung cancer immune microenvironment: A comprehensive prognostic model.

Cuproptosis plays an important role in cancer, but its role in lung cancer remains unknown. Transcriptional profiles, clinical details and mutation data were acquired from the Cancer Genome Atlas database through a variety of methods. The analysis of this publicly available data was comprehensively performed using R software along with its relevant packages, ensuring a thorough examination of the information. In this study, we conducted a detailed analysis of cuproptosis-related genes and lncRNA co-expression, identifying 129 relevant lncRNAs and establishing a prognostic model with four key lncRNAs (LINC00996, RPARP-AS1, SND1-IT1, TMPO-AS1). Utilizing data from TCGA and GEO databases, the model effectively categorized patients into high- and low-risk groups, showing significant survival differences. Correlation analysis highlighted specific relationships between individual lncRNAs and cuproptosis genes. Our survival analysis indicated a higher survival rate in the low-risk group across various cohorts. Additionally, the model's predictive accuracy was confirmed through independent prognostic analysis and ROC curve evaluations. Functional enrichment analysis revealed distinct biological pathways and immune functions between risk groups. Tumour mutation load analysis differentiated high- and low-risk groups by their mutation profiles. Drug sensitivity analysis and immune infiltration studies using the CIBERSORT algorithm further elucidated the potential treatment responses in different risk groups. This comprehensive evaluation underscores the significance of lncRNAs in cuproptosis and their potential as biomarkers for lung cancer prognosis and immune microenvironment.

Multicenter integration analysis of TRP channels revealed potential mechanisms of immunosuppressive microenvironment activation and identified a machine learning-derived signature for improving outcomes in gliomas.

This study aimed to explore the mechanisms of transient receptor potential (TRP) channels on the immune microenvironment and develop a TRP-related signature for predicting prognosis, immunotherapy response, and drug sensitivity in gliomas. Based on the unsupervised clustering algorithm, we identified novel TRP channel clusters and investigated their biological function, immune microenvironment, and genomic heterogeneity. Invitro and invivo experiments revealed the association between TRPV2 and macrophages. Subsequently, based on 96 machine learning algorithms and six independent glioma cohorts, we constructed a machine learning-based TRP channel signature (MLTS). The performance of the MLTS in predicting prognosis, immunotherapy response, and drug sensitivity was evaluated. Patients with high expression levels of TRP channel genes had worse prognoses, higher tumor mutation burden, and more activated immunosuppressive microenvironment. Meanwhile, TRPV2 was identified as the most essential regulator in TRP channels. TRPV2 activation could promote macrophages migration toward malignant cells and alleviate glioma prognosis. Furthermore, MLTS could work independently of common clinical features and present stable and superior prediction performance. This study investigated the comprehensive effect of TRP channel genes in gliomas and provided a promising tool for designing effective, precise treatment strategies.