Identification of Lactate-Related Gene Signature for Prediction of Progression and Immunotherapeutic Response in Skin Cutaneous Melanoma

Sensitivity to immune checkpoint inhibitors in BRAF/MEK inhibitor refractory melanoma

BACKGROUND

Resistance to BRAF and MEK inhibitors (BRAFi/MEKi) in metastatic melanoma frequently results in cross-resistance to immune checkpoint inhibitors (ICI), limiting effective treatment options. However, a subset of BRAFi/MEKi-resistant patients remains responsive to second-line ICI, suggesting heterogeneous underlying resistance mechanisms. This study aimed to explore the tumor immune microenvironment in BRAFi/MEKi-resistant melanoma to uncover factors influencing sensitivity to second-line ICI therapy.

METHOD

To investigate mechanisms underlying resistance and responsiveness to second-line ICIs, BRAFi/MEKi-resistant melanoma mouse models were used. Flow cytometry was employed to analyze immune cell populations within the tumor microenvironment, focusing on changes in CD8+T effector cells and other key immune subsets. RNA sequencing was performed to profile transcriptomic changes in resistant tumors, providing insights into the signaling pathways associated with resistance. Clinical samples from BRAFi/MEKi-resistant patients were further evaluated for correlations between immune profiles and key signaling pathways to support findings from the preclinical models.

RESULTS

Using BRAFi/MEKi-resistant melanoma mouse models, we observed distinct alterations in the tumor-immune microenvironment. Tumors exhibiting resistance showed a significant increase in CD8+T effector cells following BRAFi/MEKi treatment, suggesting an immune-stimulatory response. Mechanistic analysis identified the activation of the EGFR-STAT signaling pathway as a key driver of intrinsic resistance in these models. Notably, these tumors retained sensitivity to second-line ICI therapy, contrasting with NRAS-driven BRAFi/MEKi-resistant tumors, which demonstrated cross-resistance to ICIs. Supporting these findings, clinical samples from BRAFi/MEKi-resistant patients revealed a correlation between elevated EGFR activation and higher immune scores, indicating potential sensitivity to ICI therapy in this subset of patients.

CONCLUSION

EGFR overexpression emerges as a potential predictive biomarker for responsiveness to second-line ICIs in BRAFi/MEKi-resistant melanoma. These findings underscore the need for stratified therapeutic approaches and highlight EGFR as a target for improving outcomes in ICI therapy.

Tumor Microenvironment Lactate: Is It a Cancer Progression Marker, Immunosuppressant, and Therapeutic Target?

The "Warburg effect" is a term coined a century ago for the preferential use of glycolysis over aerobic respiration in tumor cells for energy production, even under aerobic conditions. Although this is a less efficient mechanism of generating energy from glucose, aerobic glycolysis, in addition to the canonical anaerobic glycolysis, is an effective means of lactate production. The abundant waste product, lactate, yielded by the dual glycolysis in a tumor, has been discovered to be a major biomolecule that drives cancer progression. Lactate is a metabolic energy source that, via cell membrane lactate transporters, shuttles in and out of cancer cells as well as cancer cell-associated stromal cells and immune cells within the tumor microenvironment (TME). Additionally, lactate serves as a pH tuner, signaling ligand and transducer, epigenetic and gene transcription regulator, TME modifier, immune suppressor, chemoresistance modulator, and prognostic marker. With such broad functionalities, the production-consumption-reproduction of TME lactate fuels tumor growth and dissemination. Here, we elaborate on the lactate sources that contribute to the pool of lactate in the TME, the functions of TME lactate, the influence of the TME lactate on immune cell function and local tissue immunity, and anticancer therapeutic approaches adopting lactate manipulations and their efficacies. By scrutinizing these properties of the TME lactate and others that have been well addressed in the field, it is expected that a better weighing of the influence of the TME lactate on cancer development, progression, prognosis, and therapeutic efficacy can be achieved.

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

Background

The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy.

Methods

Relevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0.

Results

A total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629-0.828) for PFS in the training set, 0.760 (95%CI: 0.728-0.792) and 0.819 (95%CI: 0.757-0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721-0.771) and 0.700 (95%CI: 0.677-0.724) for OS in the training and validation sets, respectively.

Conclusion

Machine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

Prognostic implication of a novel lactate score correlating with immunotherapeutic responses in pan-cancer

A thorough assessment of lactate-related genes (LRGs) in different types of human cancers is currently lacking. To elucidate the molecular landscape of LRGs, we conducted a comprehensive analysis using genomic, mRNA, and microRNA expression profiles and developed a lactate score model using the least absolute shrinkage and selection operator (LASSO) algorithm. We found that our lactate score could be a prognostic marker instead of LDHA for several cancer patients who possess high-frequency variants in LRGs. The lactate score also demonstrated an association with CD8+ T cells infiltration in multiple cancer types. Furthermore, our findings indicate that the lactate score holds promise as a potential biomarker for immunotherapy in patients with bladder cancer (BLCA) and skin cutaneous melanoma (SKCM). Among the seventeen genes of the lactate score model, PDP1 showed the strongest positive correlation with lactate score and the potential as a standalone biomarker for prognosis. In general, our study has yielded crucial insights into the potential application of the lactate score as a predictive biomarker for both survival outcomes and the response to immunotherapy. By recognizing the prognostic significance of lactate metabolism, we open avenues for further investigations aimed at harnessing the therapeutic potential of lactate.

A novel signature based on cancer-associated fibroblast genes to predict prognosis, immune feature, and therapeutic response in breast cancer

Breast cancer (BC) ranks first in the incidence of tumors in women and remains the most prevalent malignancy in women worldwide. Cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) profoundly influence the progression, recurrence, and therapeutic resistance in BC. Here, we intended to establish a risk signature based on screened CAF-associated genes in BC (BCCGs) for patient stratification. Initially, BCCGs were screened by a combination of several CAF gene sets. The identified BCGGs were found to differ significantly in the overall survival (OS) of BC patients. Accordingly, we constructed a prognostic prediction signature of 5 BCCGs, which were independent prognostic factors associated with BC based on univariate and multivariate Cox regression. The risk model divided patients into low- and high-risk groups, accompanied by different OS, clinical features, and immune infiltration characteristics. Receiver operating characteristic (ROC) curves and a nomogram further validated the predictive performance of the prognostic model. Notably, 21 anticancer agents targeting these BCCGs possessed better sensitivity in BC patients. Meanwhile, the elevated expression of the majority of immune checkpoint genes suggested that the high-risk group may benefit more from immune checkpoint inhibitors (ICIs) therapy. Taken together, our well-established model is a robust instrument to precisely and comprehensively predict the prognosis, immune features, and drug sensitivity in BC patients, for combating BC.

Construction and validation of an angiogenesis-related lncRNA prognostic model in lung adenocarcinoma

Background: There is increasing evidence that long non-coding RNAs (lncRNAs) can be used as potential prognostic factors for cancer. This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) using angiogenesis-related long non-coding RNAs (lncRNAs) as potential prognostic factors.

Methods: Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify aberrantly expressed angiogenesis-related lncRNAs in LUAD. A prognostic signature was constructed using differential expression analysis, overlap analysis, Pearson correlation analysis, and Cox regression analysis. The model’s validity was assessed using K-M and ROC curves, and independent external validation was performed in the GSE30219 dataset. Prognostic lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) networks were identified. Immune cell infiltration and mutational characteristics were also analyzed. The expression of four human angiogenesis-associated lncRNAs was quantified using quantitative real-time PCR (qRT-PCR) gene arrays.

Results: A total of 26 aberrantly expressed angiogenesis-related lncRNAs in LUAD were identified, and a Cox risk model based on LINC00857, RBPMS-AS1, SYNPR-AS1, and LINC00460 was constructed, which may be an independent prognostic predictor for LUAD. The low-risk group had a significant better prognosis and was associated with a higher abundance of resting immune cells and a lower expression of immune checkpoint molecules. Moreover, 105 ceRNA mechanisms were predicted based on the four prognostic lncRNAs. qRT-PCR results showed that LINC00857, SYNPR-AS1, and LINC00460 were significantly highly expressed in tumor tissues, while RBPMS-AS1 was highly expressed in paracancerous tissues.

Conclusion: The four angiogenesis-related lncRNAs identified in this study could serve as a promising prognostic biomarker for LUAD patients.

Identification of immune subtypes of melanoma based on single-cell and bulk RNA sequencing data

The tumor microenvironment plays a crucial role in melanoma. In this study, the abundance of immune cells in melanoma samples was assessed and analyzed using single sample gene set enrichment analysis (ssGSEA), and the predictive value of immune cells was assessed using univariate COX regression analysis. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis was applied to construct an immune cell risk score (ICRS) model with a high predictive value for identifying the immune profile of melanoma patients. The pathway enrichment between the different ICRS groups was also elucidated. Next, five hub genes for diagnosing the prognosis of melanoma were screened by two machine learning algorithms, LASSO and random forest. The distribution of hub genes in immune cells was analyzed on account of Single-cell RNA sequencing (scRNA-seq), and the interaction between genes and immune cells was elucidated by cellular communication. Ultimately, the ICRS model on account of two types of immune cells (Activated CD8 T cell and Immature B cell) was constructed and validated, which can determine melanoma prognosis. In addition, five hub genes were identified as potential therapeutic targets affecting the prognosis of melanoma patients.

A novel hypoxia- and lactate metabolism-related signature to predict prognosis and immunotherapy responses for breast cancer by integrating machine learning and bioinformatic analyses

Background

Breast cancer is the most common cancer worldwide. Hypoxia and lactate metabolism are hallmarks of cancer. This study aimed to construct a novel hypoxia- and lactate metabolism-related gene signature to predict the survival, immune microenvironment, and treatment response of breast cancer patients.

Methods

RNA-seq and clinical data of breast cancer from The Cancer Genome Atlas database and Gene Expression Omnibus were downloaded. Hypoxia- and lactate metabolism-related genes were collected from publicly available data sources. The differentially expressed genes were identified using the “edgeR” R package. Univariate Cox regression, random survival forest (RSF), and stepwise multivariate Cox regression analyses were performed to construct the hypoxia-lactate metabolism-related prognostic model (HLMRPM). Further analyses, including functional enrichment, ESTIMATE, CIBERSORTx, Immune Cell Abundance Identifier (ImmuCellAI), TIDE, immunophenoscore (IPS), pRRophetic, and CellMiner, were performed to analyze immune status and treatment responses.

Results

We identified 181 differentially expressed hypoxia-lactate metabolism-related genes (HLMRGs), 24 of which were valuable prognostic genes. Using RSF and stepwise multivariate Cox regression analysis, five HLMRGs were included to establish the HLMRPM. According to the medium-risk score, patients were divided into high- and low-risk groups. Patients in the high-risk group had a worse prognosis than those in the low-risk group (P < 0.05). A nomogram was further built to predict overall survival (OS). Functional enrichment analyses showed that the low-risk group was enriched with immune-related pathways, such as antigen processing and presentation and cytokine-cytokine receptor interaction, whereas the high-risk group was enriched in mTOR and Wnt signaling pathways. CIBERSORTx and ImmuCellAI showed that the low-risk group had abundant anti-tumor immune cells, whereas in the high-risk group, immunosuppressive cells were dominant. Independent immunotherapy datasets (IMvigor210 and GSE78220), TIDE, IPS and pRRophetic analyses revealed that the low-risk group responded better to common immunotherapy and chemotherapy drugs.

Conclusions

We constructed a novel prognostic signature combining lactate metabolism and hypoxia to predict OS, immune status, and treatment response of patients with breast cancer, providing a viewpoint for individualized treatment.

A lactate-related LncRNA model for predicting prognosis, immune landscape and therapeutic response in breast cancer

Breast cancer (BC) has the highest incidence rate of all cancers globally, with high heterogeneity. Increasing evidence shows that lactate and long non-coding RNA (lncRNA) play a critical role in tumor occurrence, maintenance, therapeutic response, and immune microenvironment. We aimed to construct a lactate-related lncRNAs prognostic signature (LRLPS) for BC patients to predict prognosis, tumor microenvironment, and treatment responses. The BC data download from the Cancer Genome Atlas (TCGA) database was the entire cohort, and it was randomly assigned to the training and test cohorts at a 1:1 ratio. Difference analysis and Pearson correlation analysis identified 196 differentially expressed lactate-related lncRNAs (LRLs). The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to construct the LRLPS, which consisted of 7 LRLs. Patients could be assigned into high-risk and low-risk groups based on the medium-risk sore in the training cohort. Then, we performed the Kaplan–Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. The results indicated that the prognosis prediction ability of the LRLPS was excellent, robust, and independent. Furthermore, a nomogram was constructed based on the LRLPS risk score and clinical factors to predict the 3-, 5-, and 10-year survival probability. The GO/KEGG and GSEA indicated that immune-related pathways differed between the two-risk group. CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore (IPS) showed that low-risk patients had higher levels of immune infiltration and better immunotherapeutic response. The pRRophetic and CellMiner databases indicated that many common chemotherapeutic drugs were more effective for low-risk patients. In conclusion, we developed a novel LRLPS for BC that could predict the prognosis, immune landscape, and treatment response.

Characterization of Lactate Metabolism Score in Breast and Thyroid Cancers to Assist Immunotherapy via Large-Scale Transcriptomic Data Analysis

Breast cancer (BC) and thyroid cancer (TC) have the highest rate of incidence, especially in women. Previous studies have revealed that lactate provides energetic and anabolic support to cancer cells, thus serving as an important oncometabolite with both extracellular and intracellular signaling functions. However, the correlation of lactate metabolism scores with thyroid and breast cancer immune characteristics remains to be systematically analyzed. To investigate the role of lactate at the transcriptome level and its correlation with the clinical outcome of BC and TC, transcriptome data of 1,217 patients with breast cancer (BC) and 568 patients with thyroid cancer (TC) were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets with their corresponding clinical and somatic mutation data. The lactate metabolism score was calculated based on a single-sample gene set enrichment analysis (ssGSEA). The results showed that lactate metabolism-related genes and lactate metabolism scores was significantly associated with the survival of patients with BRCA and THCA. Notably, the lactate metabolism scores were strongly correlated with human leukocyte antigen (HLA) expression, tumor-infiltrating lymphocyte (TIL) infiltration, and interferon (IFN) response in BC and TC. Furthermore, the lactate metabolism score was an independent prognostic factor and could serve as a reliable predictor of overall survival, clinical characteristics, and immune cell infiltration, with the potential to be applied in immunotherapy or precise chemotherapy of BC and TC.

Prognostic Value of Cancer-Associated Fibroblast-Related Gene Signatures in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a global health challenge with an increasing incidence worldwide. Cancer-associated fibroblasts (CAFs) function critically in HCC initiation and development. However, the prognostic significance of CAF-related gene signatures in HCC remains unknown. Therefore, the specific functions of CAF-related genes in HCC were investigated to help develop potential therapeutic strategies. In this study, CAF-related genes were screened from three CAF-related gene sets. HCC data from the Gene Expression Omnibus (GEO) database was applied to verify the screened CAF-related genes. Cluster analysis was used to identify clusters based on the expression pattern of CAF-related genes and two identified clusters were found to have a significant difference in overall survival (OS) and progression free intervals (PFI). The prognosis of HCC patients was predicted using the prognostic risk score model developed based on HCC data from The Cancer Genome Atlas (TCGA) databases. High-risk group patients had a worse OS than those in low-risk group in TCGA. These results were validated in International Cancer Genome Consortium (ICGC) database. Moreover, combining the clinicopathological characteristics related to prognosis with the model, a nomogram was built for a more accurate prediction of OS of HCC patients. In addition, analyses of immune infiltration characteristics of tumor microenvironment (TME), chemosensitivity, and immunotherapy response were conducted to further evaluate the prognostic value of CAF-related genes. Patients with low-risk scores were found to have higher chemosensitivity to cisplatin, doxorubicin, and sorafenib. Individuals with high-risk scores were found with a higher expression of most immune checkpoints which indicated patients with high-risk scores may benefit more from treatment with immune checkpoint inhibitors. Furthermore, a correlation between immune infiltration characteristics of TME and patients with different risk levels was found. These findings provide a possibility for the further development of personalized treatments in HCC.