Development and External Validation of a Novel Immune Checkpoint-Related Gene Signature for Prediction of Overall Survival in Hepatocellular Carcinoma

Identification of fatty acid metabolism-related genes in the tumor microenvironment of breast cancer by a development and validation of prognostic index signature

BACKGROUND

Breast cancer (BRCA) is a malignancy originating in the breast cells, characterized by a poor overall survival rate. Post-resection, chemotherapy is commonly recommended as a primary therapeutic approach; however, its efficacy remains limited. Recent advancements in lipidomics and metabolomics have provided new insights into the intricate landscape of fatty acid metabolism (FAM) and the fatty acid lipidome in both health and disease. A growing body of evidence suggests that dysregulations in FAM and fatty acid levels play a significant role in cancer initiation and progression. Despite these advances, the precise mechanisms through which FAM mediates the anti-cancer effects of lobaplatin in BRCA remain poorly understood and warrant further investigation.

METHODS

GEO and TCGA data were classified into two types. We aimed to show how FAMGs influence immune function, immune checkpoints, and m6a in BRCA. A co-expression analysis discovered that gene expression is strongly connected to pyroptosis. The TCGA gathered information about mRNAsi, gene mutations, CNV, and clinical features.

RESULTS

In the low-risk group, overall survival (OS) is longer. GSEA was utilized to identify immune and tumor-related pathways. Most of the FAMG-derived prognostic signatures predominantly modulate immunological and oncogenic signaling pathways, including the Wnt, neurotrophin, chemokine, and calcium signaling cascades. Among the genes involved are CEL, WT1, and ULBP2. Expression levels varied as well. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and drug sensitivity all pointed to the gene.

CONCLUSIONS

The primary objective of this study is to identify and validate BRCA-associated FAMGs that can serve as prognostic indicators and provide insights into immune system function, while also offering evidence to support the development of fatty acid metabolism-related molecularly targeted therapeutics. Consequently, FAMGs and their interactions with the immune system, as well as their role in BRCA, may emerge as promising therapeutic targets.

Development and validation of prognostic and diagnostic models utilizing immune checkpoint-related genes in public datasets for clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal cell carcinoma, and immune checkpoint regulator-based immunotherapy has emerged as an effective treatment for advanced stages of the disease. However, the expression patterns, prognostic significance, and diagnostic value of immune checkpoint-related genes (ICRGs) in ccRCC remain underexplored. This study utilized large-scale ccRCC datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) to analyze ICRGs and develop a prognostic and diagnostic model, which was validated using quantitative PCR in clinical samples from ccRCC patients.

Methods

RNA-seq data and clinical information were retrieved from TCGA, ICGC, and GEO databases. Differentially expressed genes (DEGs) were identified, and immune checkpoint-related genes (DICRGs) were selected by intersecting DEGs with ICRGs, followed by validation in independent datasets. Univariate and multivariate Cox regression analyses were used to develop the prognostic model. Protein expression of key genes was validated through immunohistochemistry (IHC) using data from the Human Protein Atlas (HPA). qRT-PCR confirmed gene expression levels in ccRCC and normal kidney tissues. Diagnostic models were constructed using machine learning, and functional enrichment and immune infiltration analyses were performed.

Results

Fourteen DICRGs were identified, with four (EGFR, TRIB3, ZAP70, and CD4) showing prognostic significance in Cox analyses. IHC revealed high expression of these genes in ccRCC tissues, and qRT-PCR confirmed increased expression of EGFR, TRIB3, and CD4, while ZAP70 expression showed no significant change. A prognostic risk score was developed based on gene expression levels. Functional analysis identified enriched pathways related to organic anion transport and metabolism, while immune infiltration analysis revealed associations between ZAP70, CD4, and risk scores.

Conclusion

This study establishes a prognostic model for ccRCC based on four ICRGs, providing valuable insights into the molecular mechanisms underlying prognosis and diagnosis in ccRCC.

Identification of clinical prognostic factors and analysis of ferroptosis-related gene signatures in the bladder cancer immune microenvironment

BACKGROUND

Bladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and poses a significant burden in terms of both incidence and mortality rates on a global scale. Among all BLCA cases, non-muscle invasive bladder cancer constitutes approximately 75% of the total. In recent years, the concept of ferroptosis, an iron-dependent form of regulated cell death marked by the accumulation of lipid peroxides, has captured the attention of researchers worldwide. Nevertheless, the precise involvement of ferroptosis-related genes (FRGs) in the anti-BLCA response remains inadequately elucidated.

METHODS

The integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of FRGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and FRGs, unraveling their functional relevance and potential implications in BLCA.

RESULTS

FRGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including MYBPH, SOST, SPRR2A, and CRNN, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CYP4F8, PDZD3, CRTAC1, and LRTM1 were identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.

CONCLUSIONS

FRGs are associated with the onset and progression of BLCA. A FRGs signature offers a viable alternative to predict BLCA, and these FRGs show a prospective research area for BLCA targeted treatment in the future.

Development and validation of prognostic index based on purine metabolism genes in patients with bladder cancer

Background

Bladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and is associated with significant morbidity and mortality worldwide. Dysregulation of tumor metabolic pathways is closely linked to the initiation and proliferation of BLCA. Tumor cells exhibit distinct metabolic activities compared to normal cells, and the purine metabolism pathway, responsible for providing essential components for DNA and RNA synthesis, is believed to play a crucial role. However, the precise involvement of Purine Metabolism Genes (PMGs) in the defense mechanism against BLCA remains elusive.

Methods

The integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of PMGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and PMGs, unraveling their functional relevance and potential implications in BLCA.

Results

PMGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including CLDN6, CES1, SOST, SPRR2A, MYBPH, CGB5, and KRT1, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CRTAC1 was identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.

Conclusion

PMGs have been implicated in the etiology and progression of bladder cancer (BLCA). Prognostic models corresponding to this malignancy aid in the accurate prediction of patient outcomes. Notably, exploring the potential therapeutic targets within the tumor microenvironment (TME) such as PMGs and immune cell infiltration holds promise for effective BLCA management, albeit necessitating further research. Moreover, the identification of a gene signature associated with purine Metabolism presents a credible and alternative approach for predicting BLCA, signifying a burgeoning avenue for targeted therapeutic investigations in the field of BLCA.

Pyrimidine metabolism regulator-mediated molecular subtypes display tumor microenvironmental hallmarks and assist precision treatment in bladder cancer

Background

Bladder cancer (BLCA) is a common urinary system malignancy with a significant morbidity and death rate worldwide. Non-muscle invasive BLCA accounts for over 75% of all BLCA cases. The imbalance of tumor metabolic pathways is associated with tumor formation and proliferation. Pyrimidine metabolism (PyM) is a complex enzyme network that incorporates nucleoside salvage, de novo nucleotide synthesis, and catalytic pyrimidine degradation. Metabolic reprogramming is linked to clinical prognosis in several types of cancer. However, the role of pyrimidine metabolism Genes (PyMGs) in the BLCA-fighting process remains poorly understood.

Methods

Predictive PyMGs were quantified in BLCA samples from the TCGA and GEO datasets. TCGA and GEO provided information on stemness indices (mRNAsi), gene mutations, CNV, TMB, and corresponding clinical features. The prediction model was built using Lasso regression. Co-expression analysis was conducted to investigate the relationship between gene expression and PyM.

Results

PyMGs were overexpressed in the high-risk sample in the absence of other clinical symptoms, demonstrating their predictive potential for BLCA outcome. Immunological and tumor-related pathways were identified in the high-risk group by GSWA. Immune function and m6a gene expression varied significantly between the risk groups. In BLCA patients, DSG1, C6orf15, SOST, SPRR2A, SERPINB7, MYBPH, and KRT1 may participate in the oncology process. Immunological function and m6a gene expression differed significantly between the two groups. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and drug sensitivity all showed significant gene connections.

Conclusions

BLCA-associated PyMGs are available to provide guidance in the prognostic and immunological setting and give evidence for the formulation of PyM-related molecularly targeted treatments. PyMGs and their interactions with immune cells in BLCA may serve as therapeutic targets.

Glutamine metabolism genes prognostic signature for stomach adenocarcinoma and immune infiltration: potential biomarkers for predicting overall survival

Background

Stomach adenocarcinoma (STAD), caused by mutations in stomach cells, is characterized by poor overall survival. Chemotherapy is commonly administered for stomach cancer patients following surgical resection. An imbalance in tumor metabolic pathways is connected to tumor genesis and growth. It has been discovered that glutamine (Gln) metabolism plays a crucial role in cancer. Metabolic reprogramming is associated with clinical prognosis in various cancers. However, the role of glutamine metabolism genes (GlnMgs) in the fight against STAD remains poorly understood.

Methods

GlnMgs were determined in STAD samples from the TCGA and GEO datasets. The TCGA and GEO databases provide information on stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and clinical characteristics. Lasso regression was performed to build the prediction model. The relationship between gene expression and Gln metabolism was investigated using co-expression analysis.

Results

GlnMgs, found to be overexpressed in the high-risk group even in the absence of any symptomatology, demonstrated strong predictive potential for STAD outcomes. GSEA highlighted immunological and tumor-related pathways in the high-risk group. Immune function and m6a gene expression differed significantly between the low- and high-risk groups. AFP, CST6, CGB5, and ELANE may be linked to the oncology process in STAD patients. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and medication sensitivity all revealed a strong link to the gene.

Conclusion

GlnMgs are connected to the genesis and development of STAD. These corresponding prognostic models aid in predicting the prognosis of STAD GlnMgs and immune cell infiltration in the tumor microenvironment (TME) may be possible therapeutic targets in STAD. Furthermore, the glutamine metabolism gene signature presents a credible alternative for predicting STAD outcomes, suggesting that these GlnMgs could open a new field of study for STAD-focused therapy Additional trials are needed to validate the results of the current study.

Relationship between serum Th1/Th2 imbalance and depression in elderly patients with COPD and its clinical implications

BACKGROUND

Chronic obstructive pulmonary disease (COPD) causes organic damage as well as anxiety, depression, fear, and other psychological disorders, which seriously affect the quality of life and prognosis of patients and cause a huge economic burden to the family and society.

OBJECTIVE

The aim of this study was to investigate the correlation between an imbalance of serum Th1/Th2 indicators and psychiatric depression in elderly patients with COPD and analyze its implications for clinical management.

METHODS

From January 2018 to May 2022, 120 elderly patients with COPD treated at our hospital were categorized into two groups based on the self-rating depression scale (SDS): COPD with depression (SDS score ⩾ 50) and COPD alone (SDS score < 50). Blood gas analysis, pulmonary function, and serum Th1/Th2 index were determined. Receiver operating characteristic (ROC) curves were analyzed to explore the diagnostic value of serum Th1/Th2 ratios for COPD complicated by depression.

RESULTS

Compared with the group without depression, the partial pressure of carbon dioxide and COPD assessment test scores were significantly higher, and the oxygenation index, forced expiratory volume in one second (FEV1), and percent predicted FEV1 were significantly lower in the COPD with depression group (P< 0.05). Interleukin (IL)-1β, IL-2, IL-6, IL-8, IL-10, and tumor necrosis factor-α (TNF-α) were significantly higher in the COPD with depression group than in the group without depression (P< 0.05). Logistic regression analysis indicated that the imbalance of serum IL-1β, IL-2, IL-6, IL-8, IL-10, and TNF-α was a risk factor for mental depression in elderly patients with COPD. When comparing prognostic indices, the interval before the first onset of clinically noticeable deterioration (CID-C) in the COPD with depression group was noticeably shorter than that in the COPD without depression group; the incidence of CID-C within 6 months was noticeably higher in the COPD with depression group than in the group without depression.

CONCLUSION

Elderly patients with COPD and depression had reduced pulmonary function and higher serum Th1/Th2 levels, and an imbalance in serum Th1/Th2 indicators was a potential risk factor for depression. Moreover, elderly patients with COPD and depression were at a higher risk of disease progression and had a worse prognosis. Thus, an imbalance in serum Th1/Th2 indicators is a potential prognostic factor for evaluating depression in patients with COPD.

Potential biomarkers for predicting the overall survival outcome of kidney renal papillary cell carcinoma: an analysis of ferroptosis-related LNCRNAs

Background

Kidney renal papillary cell carcinoma (KIRP) is a dangerous cancer, which accounts for 15–20% of all kidney malignancies. Ferroptosis is a rare kind of cell death that overcomes medication resistance. Ferroptosis-related long non-coding RNAs (LNCRNAs) in KIRP, remain unknown.

Method

We wanted to express how ferroptosis-related LNCRNAs interact with immune cell infiltration in KIRP. Gene set enrichment analysis in the GO and KEGG databases were used to explore gene expression enrichment. The prognostic model was constructed using Lasso regression. In addition, we also analyzed the modifications in the tumor microenvironment (TME) and immunological association.

Result

The expression of LNCRNA was closely connected to the ferroptosis, according to co-expression analyses. CASC19, AC090197.1, AC099850.3, AL033397.2, LINC00462, and B3GALT1-AS1 were found to be significantly increased in the high-risk group, indicating that all of these markers implicates the malignancy processes for KIRP patients and may be cancer-promoting variables. LNCTAM34A and AC024022.1 were shown to be significantly elevated in the low-risk group; these might represent as the KIRP tumor suppressor genes. According to the TCGA, CCR, and inflammation-promoting genes were considered to be significantly different between the low-risk and high-risk groups. The expression of CD160, TNFSF4, CD80, BTLA, and TNFRSF9 was different in the two risk groups.

Conclusion

LNCRNAs associated with ferroptosis were linked to the occurrence and progression of KIRP. Ferroptosis-related LNCRNAs and immune cell infiltration in the TME may be potential biomarkers in KIRP that should be further investigated.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01037-0.

Novel Hypoxia-Associated Gene Signature Depicts Tumor Immune Microenvironment and Predicts Prognosis of Colon Cancer Patients

Hypoxia, a typical hallmark of numerous tumors, indicates poor infiltration of antitumor lymphocytes, as well as facilitates the development, progression, and drug resistance of malignant cells. Here, the present research was performed to identify novel hypoxia-related molecular markers and their correlation to the tumor immune microenvironment (TIME) in colon cancer. The expression of hypoxia-related gene signature was extracted from The Cancer Genome Atlas (TCGA) COAD cohort. Based on this signature, a risk score model was constructed using the Lasso regression model. Its discrimination ability and stability were validated in another independent cohort (GSE17536) from Gene Expression Omnibus (GEO) database. Moreover, molecular biology experiments (quantitative real-time PCR and multiple immunohistochemistry) were performed to validate the results of bioinformatics analyses. Three hub genes, including PPFIA4, SERPINE1, and STC2, were chosen to build the risk score model. All of these genes were increasingly expressed in the hypoxia subgroup (HS). Compared with the normoxia subgroup (NS), HS had worse pathological features (T, N, M, and stage) and overall survival (OS), more expression of immune checkpoint molecules, poorer infiltration of some pro-inflammation immune cells (CD4⁺ T cells and CD8⁺ T cells), and enriched infiltration of M0/M2 macrophages. After the risk model was proven to be valuable and stable, a nomogram was built based on this model and some clinicopathological factors. Moreover, it had been identified that three hub genes were all increasingly expressed in hypoxic conditions by quantitative real-time PCR (qPCR). The results of multiple immunohistochemistry (mIHC) also showed that higher expression of hub genes was associated with poorer infiltration of pro-inflammation immune cells (CD8⁺ T cells and M1 macrophages) and richer infiltration of anti-inflammation immune cells (Treg cells and M2 macrophages). In conclusion, the present study uncovered the relations among hypoxia, TIME, and clinicopathological features of colon cancer. It might provide new insight and a potential therapeutic target for immunotherapy.

A novel immune checkpoint-related gene signature for hepatocellular carcinoma to predict clinical outcomes and therapeutic response

Immune checkpoint genes (ICGs) have recently been proven to perform instrumental functions in the maintenance of immune homeostasis and represent a promising therapeutic strategy; however, their expression patterns and prognostic values are not fully elucidated in hepatocellular carcinoma (HCC). In this investigation, we focused on establishing and validating a prognostic gene signature to facilitate decision-making in clinical practice. Clinical information, as well as transcriptome data, was obtained from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox method were employed to build a multi-gene signature in the TCGA database, while the ICGC database was used for validation. Subsequently, utilizing the six-gene signature, we were able to categorize patients into high- and low-risk groups. In two cohorts, survival analysis findings revealed a dismal outlook for the high-risk group. The receiver operating characteristic curves were utilized to estimate the gene signature's prediction ability. Moreover, correlation analysis showed high-risk group was linked to advanced pathological stage, infiltration of immune cells and therapeutic response. In summary, this unique gene profile might serve not only as a useful prognostic indicator but also as a marker of therapy responsiveness in HCC.

Novel necroptosis-related gene signature for predicting the prognosis of pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a deadly digestive system tumor with a poor prognosis. Recently, necroptosis has been considered as a type of inflammatory programmed cell death. However, the expression of necroptosis-related genes (NRGs) in PAAD and their associations with prognosis remain unclear. NRGs' prediction potential in PAAD samples from The TCGA and GEO datasets was investigated. The prediction model was constructed using Lasso regression. Co-expression analysis showed that gene expression was closely related to necroptosis. NRGs were shown to be somewhat overexpressed in high-risk people even when no other clinical symptoms were present, indicating that they may be utilized in a model to predict PAAD prognosis. GSEA showed immunological and tumor-related pathways in the high-risk group. Based on the findings, immune function and m6A genes differ significantly between the low-risk and high-risk groups. MET, AM25C, MROH9, MYEOV, FAM111B, Y6D, and PPP2R3A might be related to the oncology process for PAAD patients. Moreover, CASKIN2, TLE2, USP20, SPRN, ARSG, MIR106B, and MIR98 might be associated with low-risk patients with PAAD. NRGs and the relationship of the immune function, immune checkpoints, and m6A gene expression with NRGs in PAAD may be considered as potential therapeutic targets that should be further studied.

Systematic Characterization of Novel Immune Gene Signatures Predicts Prognostic Factors in Hepatocellular Carcinoma

Background: The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of tumor immune microenvironment promotes the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes. Methods: Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed using gene expression profiles from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate the predictive performance in different cohorts. The high and low groups were divided based on the risk score of the model, and different algorithms were used to evaluate the tumor immune infiltration cell (TIIC). The expression and prognosis of core genes in pan-cancer cohorts were analyzed, and gene enrichment analysis was performed using clusterProfiler. Finally, the expression of the hub genes of the model was validated by clinical samples. Results: Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes were enriched in the tumor immunologic related signaling pathways. The first 15 genes were selected using RankAggreg analysis, and all the genes showed a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, a 5-gene signature risk model (ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1) was constructed. The signature has strong robustness and can stabilize different cohorts (TCGA-LIHC, HCCDB18, and GSE14520). Compared with other existing models, our model has better performance. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and found a consistent trend in the distribution of TIIC. In the high-risk score group, scores of Macrophages M1, Mast cell resting, and T cells CD8 were significantly lower than those of the low-risk score group. Different immune expression characteristics, lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, and SPP1 were highly expressed in cancer tissues, while IL18RAP and SLC11A1 expression in cancer tissues was lower. In addition, IL18RAP has a highly positive correlation with B cell, macrophage, Neutrophil, Dendritic cell, CD8 cell, and CD4 cell. The SPP1, PRKCD, and SLC11A1 genes have the strongest correlation with macrophages. The expression of ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1 genes varies among different immune subtypes and between different T stages. Conclusion: The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.

Comprehensive analysis of N6-methyladenosine -related long non-coding RNAs and immune cell infiltration in hepatocellular carcinoma

We aimed to illustrate the influence of N6-methyladenosine (m6A) long non-coding RNAs (lncRNAs) and immune cell infiltration in hepatocellular carcinoma (HCC). The relationship of lncRNAs and m6A was identified through gene expression analysis using PERL and R packages. The Kyoto Encyclopedia of Genes and Genomes gene expression enrichment analysis was performed via gene set enrichment analysis. Lasso regression was utilized to construct prognostic model. Differences in the tumor microenvironment and the immune correlation were analyzed to clarify immune cell infiltration in different clusters and their correlation with the clinical prognosis. Co-expression analysis showed that lncRNA expression was associated closely with m6A. Many lncRNAs were predictive risk factors of prognosis in HCC. m6A-lncRNAs were partially highly expressed in tumor tissue and could be used in a prognostic model to predict HCC prognosis, independent of other clinical characteristics. ‘NOTCH SIGNALING PATHWAY’ was most significantly enriched according to GSEA. CKLF-like MARVEL transmembrane domain-containing member 3 (CMTM3) was overexpressed in tumor tissue. Immune cells, such as activated CD4 memory T cells, CD8 T cells, and follicular helper T cells, highly infiltrated tissues in cluster 2. All related scores were higher in cluster 2, indicating a lower purity of tumor cells and higher density of immune-related cells in the tumor microenvironment. m6A-lncRNAs are closely related to HCC occurrence and progression. Corresponding prognostic models can help predict HCC prognosis. m6A-lncRNAs and the related immune cell infiltration in the tumor microenvironment can provide novel therapeutic targets in HCC that need to be further studied.