Nomogram for predicting prognosis of patients with metastatic melanoma after immunotherapy: A Chinese population-based analysis

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.

Association of the Advanced Lung Cancer Inflammation Index (ALI) and Gustave Roussy Immune (GRIm) score with immune checkpoint inhibitor efficacy in patients with gastrointestinal and lung cancer

OBJECTIVE

This study aimed to conduct a comprehensive analysis, evaluating the prognostic significance of the baseline Advanced Lung Cancer Inflammation Index (ALI) and Gustave Roussy Immune (GRIm) Score in patients undergoing immune checkpoint inhibitor (ICI) therapy.

METHODS

A comprehensive search was performed across various databases, including PubMed, the Cochrane Library, EMBASE, and Google Scholar, until October 21, 2023, to compile relevant articles for analysis. The investigation encompassed diverse clinical outcomes, including overall survival (OS) and progression-free survival (PFS).

RESULTS

This analysis included a total of 15 articles, comprising 19 studies involving 3335 patients. Among the 19 studies, nine studies focused on NSCLC, and six studies were conducted on HCC. Pooled results revealed that patients with elevated ALI levels experienced prolonged OS (HR: 0.51, 95% CI: 0.37-0.70, p < 0.001) and extended PFS (HR: 0.61, 95% CI: 0.52-0.72, p < 0.001). Furthermore, a GRIm score > 1 was associated with reduced OS (HR: 2.07, 95% CI: 1.47-2.92, p < 0.001) and diminished PFS (HR: 1.78, 95% CI: 1.35-2.34, p < 0.001) in cancer patients receiving ICIs. Subgroup analysis indicated that ALI cutoff values of 18 exhibited enhanced predictive potential. Additionally, for HCC patients, those with HCC-GRIm score > 2 showed a substantially decreased risk of mortality compared to individuals with HCC-GRIm score ≤ 2 (HR: 2.63, 95% CI: 1.89-3.65, p < 0.001).

CONCLUSION

The ALI and GRIm score served as dependable prognostic indicators for patients undergoing ICI therapy in the context of cancer treatment.

Immunotherapy in melanoma: Can we predict response to treatment with circulating biomarkers?

Melanoma is the most aggressive form of skin cancer, representing approximately 4% of all cutaneous neoplasms and accounting for up to 80% of deaths. Advanced stages of melanoma involve metastatic processes and are associated with high mortality and morbidity, mainly due to the rapid dissemination and heterogeneous responses to current therapies, including immunotherapy. Immune checkpoint inhibitors (ICIs) are currently used in the treatment of metastatic melanoma (MM) and despite being linked to an increase in patient survival, a high percentage of them still do not benefit from it. Accordingly, the number of therapeutic regimens for MM patients using ICIs either alone or in combination with other therapies has increased, together with the need for reliable biomarkers that can both predict and monitor response to ICIs. In this context, circulating biomarkers, such as DNA, RNA, proteins, and cells, have emerged due to their ability to reflect disease status. Moreover, blood tests are minimally invasive and provide an attractive option to detect biomarkers, avoiding stressful medical procedures. This systematic review aims to evaluate the possibility of a non-invasive biomarker signature that can guide therapeutic decisions. The studies reported here offer valuable insight into how circulating biomarkers can have a role in personalized treatments for melanoma patients receiving ICIs therapy, emphasizing the need for rigorous clinical trials to confirm findings and establish standardized procedures.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.

Deciphering the molecular landscape: integrating single-cell transcriptomics to unravel myofibroblast dynamics and therapeutic targets in clear cell renal cell carcinomas

Background

Clear cell renal cell carcinomas (ccRCCs) epitomize the most formidable clinical subtype among renal neoplasms. While the impact of tumor-associated fibroblasts on ccRCC progression is duly acknowledged, a paucity of literature exists elucidating the intricate mechanisms and signaling pathways operative at the individual cellular level.

Methods

Employing single-cell transcriptomic analysis, we meticulously curated UMAP profiles spanning substantial ccRCC populations, delving into the composition and intrinsic signaling pathways of these cohorts. Additionally, Myofibroblasts were fastidiously categorized into discrete subpopulations, with a thorough elucidation of the temporal trajectory relationships between these subpopulations. We further probed the cellular interaction pathways connecting pivotal subpopulations with tumors. Our endeavor also encompassed the identification of prognostic genes associated with these subpopulations through Bulk RNA-seq, subsequently validated through empirical experimentation.

Results

A notable escalation in the nFeature and nCount of Myofibroblasts and EPCs within ccRCCs was observed, notably enriched in oxidation-related pathways. This phenomenon is postulated to be closely associated with the heightened metabolic activities of Myofibroblasts and EPCs. The Myofibroblasts subpopulation, denoted as C3 HMGA1+ Myofibroblasts, emerges as a pivotal subset, displaying low differentiation and positioning itself at the terminal point of the temporal trajectory. Intriguingly, these cells exhibit a high degree of interaction with tumor cells through the MPZ signaling pathway network, suggesting that Myofibroblasts may facilitate tumor progression via this pathway. Prognostic genes associated with C3 were identified, among which TUBB3 is implicated in potential resistance to tumor recurrence. Finally, experimental validation revealed that the knockout of the key gene within the MPZ pathway, MPZL1, can inhibit tumor activity, proliferation, invasion, and migration capabilities.

Conclusion

This investigation delves into the intricate mechanisms and interaction pathways between Myofibroblasts and ccRCCs at the single-cell level. We propose that targeting MPZL1 and the oxidative phosphorylation pathway could serve as potential key targets for treating the progression and recurrence of ccRCC. This discovery paves the way for new directions in the treatment and prognosis diagnosis of ccRCC in the future.

Exploring the immune microenvironment of osteosarcoma through T cell exhaustion-associated gene expression: a study on prognosis prediction

Background

Osteosarcoma is a highly aggressive type of bone cancer with a poor prognosis. In the tumor immune microenvironment, T-cell exhaustion can occur due to various factors, leading to reduced tumor-killing ability. The purpose of this study was to construct a prognostic model based on T-cell exhaustion-associated genes in osteosarcoma.

Methods

Patient data for osteosarcoma were retrieved from the TARGET and GEO databases. Consensus clustering was employed to identify two novel molecular subgroups. The dissimilarities in the tumor immune microenvironment between these subgroups were evaluated using the "xCell" algorithm. GO and KEGG analyses were conducted to elucidate the underlying mechanisms of gene expression. Predictive risk models were constructed using the least absolute shrinkage and selection operator algorithm and Cox regression analysis. To validate the prognostic significance of the risk gene expression model at the protein level, immunohistochemistry assays were performed on osteosarcoma patient samples. Subsequently, functional analysis of the key risk gene was carried out through in vitro experimentation.

Results

Four gene expression signatures (PLEKHO2, GBP2, MPP1, and VSIG4) linked to osteosarcoma prognosis were identified within the TARGET-osteosarcoma cohort, categorizing patients into two subgroups. The resulting prognostic model showed strong predictive capability, with area under the receiver operating characteristic curve (AUC) values of 0.728/0.740, 0.781/0.658, and 0.788/0.642 for 1, 3, and 5-year survival in both training and validation datasets. Notably, patients in the low-risk group had significantly higher stromal, immune, and ESTIMATE scores compared to high-risk counterparts. Additionally, a nomogram was developed, exhibiting high accuracy in predicting the survival outcome of osteosarcoma patients. Immunohistochemistry, Kaplan-Meier, and time-dependent AUC analyses consistently supported the prognostic value of the risk model within our osteosarcoma patient cohort. In vitro experiments provided additional validation by demonstrating that the downregulation of GBP2 promoted the proliferation, migration, and invasion of osteosarcoma cells while inhibiting apoptosis.

Conclusion

The current study established a prognostic signature associated with TEX-related genes and elucidated the impact of the pivotal gene GBP2 on osteosarcoma cells via in vitro experiments. Consequently, it introduces a fresh outlook for clinical prognosis prediction and sets the groundwork for targeted therapy investigations in osteosarcoma.

Predicting cutaneous malignant melanoma patients' survival using deep learning: a retrospective cohort study

BACKGROUND

Cutaneous malignant melanoma (CMM) has the worst prognosis among skin cancers, especially metastatic CMM. Predicting its prognosis accurately could direct clinical decisions.

METHODS

The Surveillance, Epidemiology, and End Results database was screened to collect CMM patients' data. According to diagnosed time, patients were subdivided into three cohorts, train cohort (diagnosed between 2010 and 2013), validation cohort (diagnosed in 2014), and test cohort (diagnosed in 2015). Train cohort was used to train deep learning survival model for cutaneous malignant melanoma (DeepCMM). DeepCMM was then evaluated in train cohort and validation cohort internally, and validated in test cohort externally.

RESULTS

DeepCMM showed 0.8270 (95% CI, confidence interval, CI 0.8260-0.8280) as area under the receiver operating characteristic curve (AUC) in train cohort, 0.8274 (95% CI 0.8286-0.8298) AUC in validation cohort, and 0.8303 (95% CI 0.8289-0.8316) AUC in test cohort. Then DeepCMM was packaged into a Windows 64-bit software for doctors to use.

CONCLUSION

Deep learning survival model for cutaneous malignant melanoma (DeepCMM) can offer a reliable prediction on cutaneous malignant melanoma patients' overall survival.

Reprogramming of Treg cells in the inflammatory microenvironment during immunotherapy: a literature review

Regulatory T cells (Treg), as members of CD4+ T cells, have garnered extensive attention in the research of tumor progression. Treg cells have the function of inhibiting the immune effector cells, preventing tissue damage, and suppressing inflammation. Under the stimulation of the tumor inflammatory microenvironment (IM), the reprogramming of Treg cells enhances their suppression of immune responses, ultimately promoting tumor immune escape or tumor progression. Reducing the number of Treg cells in the IM or lowering the activity of Treg cells while preventing their reprogramming, can help promote the body's anti-tumor immune responses. This review introduces a reprogramming mechanism of Treg cells in the IM; and discusses the regulation of Treg cells on tumor progression. The control of Treg cells and the response to Treg inflammatory reprogramming in tumor immunotherapy are analyzed and countermeasures are proposed. This work will provide a foundation for downregulating the immunosuppressive role of Treg in the inflammatory environment in future tumor immunotherapy.

Primary tumour category, site of metastasis, and baseline serum S100B and LDH are independent prognostic factors for survival in metastatic melanoma patients treated with anti-PD-1

Background

Prognostic classification of metastatic melanoma patients treated with anti-PD-1 is of great interest to clinicians.

Objective

We aimed to determine the anti-PD-1 treatment related prognostic performance of demographics, clinical and histological prognostic markers and baseline serum S100B and LDH levels in advanced melanoma.

Methods

A total of 200 patients with unresectable metastatic melanoma were included in this retrospective study. 34.5% had stage M1c disease and 11.5% had stage M1d disease at the start of therapy. 30% had pT4b primary melanoma. 55.5% had elevated baseline serum S100B levels and 62.5% had elevated baseline serum LDH levels. We analysed the risk of death using univariate and multivariate Cox proportional-hazards models and the median overall (OS) and progression-free (PFS) survival using the Kaplan-Meier estimator.

Results

The median follow-up time from the start of anti-PD-1 treatment in patients who were alive at the end of the study (N=81) was 37 months (range: 6.1-95.9). The multivariate Cox regression analysis showed that M1c stage (vs. M1a, p=0.005) or M1d stage at the start of therapy (vs. M1a, p=0.001), pT4b category (vs. pT1a, p=0.036), elevated baseline serum S100B levels (vs. normal S100B, p=0.008) and elevated LDH levels (vs. normal LDH, p=0.049) were independently associated with poor survival. The combination of M1d stage, elevated baseline serum S100B and LDH levels and pT4b category was associated with a very high risk of death (HR 4.72 [1.81; 12.33]). In the subgroup of patients with pT4b primary melanoma, the median OS of patients with normal serum S100B levels was 37.25 months [95% CI 11.04; 63.46]), while the median OS of patients with elevated serum S100B levels was 8.00 months [95% CI 3.49; 12.51]) (p<0.001); the median OS of patients with normal serum LDH levels was 41.82 months [95% CI 11.33; 72.32]), while the median OS of patients with elevated serum LDH levels was 12.29 months [95% CI 4.35; 20.23]) (p=0.002).

Conclusion

Our real-world study indicates that the prognostic role of primary melanoma parameters is preserved in anti-PD-1 treated stage IV patients. Furthermore, there seems to be perspective in combining clinical, histological and serum prognostic markers in a prognostic model.

Integrating single-cell analysis and machine learning to create glycosylation-based gene signature for prognostic prediction of uveal melanoma

BACKGROUND

Increasing evidence suggests a correlation between glycosylation and the onset of cancer. However, the clinical relevance of glycosylation-related genes (GRGs) in uveal melanoma (UM) is yet to be fully understood. This study aimed to shed light on the impact of GRGs on UM prognosis.

METHODS

To identify the most influential genes in UM, we employed the AUCell and WGCNA algorithms. The GRGs signature was established by integrating bulk RNA-seq and scRNA-seq data. UM patients were separated into two groups based on their risk scores, the GCNS_low and GCNS_high groups, and the differences in clinicopathological correlation, functional enrichment, immune response, mutational burden, and immunotherapy between the two groups were examined. The role of the critical gene AUP1 in UM was validated through in vitro and in vivo experiments.

RESULTS

The GRGs signature was comprised of AUP1, HNMT, PARP8, ARC, ALG5, AKAP13, and ISG20. The GCNS was a significant prognostic factor for UM, and high GCNS correlated with poorer outcomes. Patients with high GCNS displayed heightened immune-related characteristics, such as immune cell infiltration and immune scores. In vitro experiments showed that the knockdown of AUP1 led to a drastic reduction in the viability, proliferation, and invasion capability of UM cells.

CONCLUSION

Our gene signature provides an independent predictor of UM patient survival and represents a starting point for further investigation of GRGs in UM. It offers a novel perspective on the clinical diagnosis and treatment of UM.