Skip to main content
NCBI home page
Discover Oncology logoLink to Discover Oncology
. 2026 Apr 24;17:857. doi: 10.1007/s12672-026-04957-7

A novel LncRNA signature associated with cuproptosis for prognostic prediction and immune response assessment in rectum adenocarcinoma

Minghe Lv 1, Ruping Zhao 1,✉
PMCID: PMC13243164  PMID: 42029881

Abstract

Background

Cuproptosis, a novel form of cell death, induces protein-toxic stress before leading to cell demise. However, studies exploring long noncoding RNA (LncRNA) related to cuproptosis in Rectum adenocarcinoma (READ) are limited. This study aims to identify prognostic markers associated with cuproptosis-related LncRNAs and their relationship with the immune microenvironment in READ using bioinformatics approaches.

Methods

RNA sequencing, genetic mutations, copy number variations, and clinical data for TCGA_READ were obtained from The Cancer Genome Atlas (TCGA). READ patients were randomly assigned to either a training cohort or a validation cohort. In the training cohort, the prognostic model was developed using least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression models. The prognostic significance of the model was further validated in both the validation and full cohorts. Tumor mutation burden (TMB), immune-related activities, and functional enrichment analyses (Gene Ontology [GO] and Kyoto Encyclopedia of Genes and Genomes [KEGG]) were performed to explore the relationship between LncRNAs and cuproptosis.

Results

From existing literature, we identified 22 genes associated with cuproptosis. Using co-expression analysis, we identified 421 LncRNAs related to cuproptosis. Cox regression analysis led to the identification of four cuproptosis-related LncRNAs (GPRACR, LINC01555, NIHCOLE, and MPC1-DT) as prognostic markers. Patients were stratified into high- and low-risk groups based on the median risk score. Kaplan–Meier survival analysis showed that patients in the high-risk group had significantly worse overall survival (OS) and progression-free survival (PFS), with higher mortality rates. Univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prognostic factor. A nomogram was constructed using multivariate Cox regression to predict the survival outcomes of READ patients. The TMB in the low-risk group was significantly higher than in the high-risk group, and patients with low-risk scores and high TMB had the best OS. Using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm, we observed significant differences in immune markers, including TAM_M2, IFNG, and CD8, between the high- and low-risk groups.

Conclusion

In conclusion, the four cuproptosis-related LncRNAs identified in this study serve as reliable prognostic markers for READ patients. These findings provide novel insights into potential immunotherapeutic strategies and clinical applications in the management of READ.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-026-04957-7.

Keywords: Cuproptosis, LncRNA, Prognosis, Immune, Rectum adenocarcinoma

Introduction

The incidence of rectal cancer (RC), a common malignant tumor, is rising, with a growing proportion of patients being identified with locally advanced illness [1]. Rectum adenocarcinoma (READ) accounts for approximately 90% of all colorectal cancer histologic types. In Western countries, total mesorectal excision (TME) in conjunction with neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for locally advanced rectal cancer (LARC) in order to prevent local recurrence [2, 3]. In addition to improving the preservation rate of the anal sphincter, nCRT in conjunction with TME can dramatically lower the local disease recurrence rate. With the advent of immunotherapy, there have been new advancements in the treatment of rectal cancer, leading to further improvement in prognosis [4]. Studies have found that approximately 15% of rectal cancer (RC) tumors have microsatellite instability (MSI), and these patients respond well to immunotherapy and often have a better prognosis [5]. The KEYNOTE-177 trial is a multicenter randomized trial that evaluated the efficacy of pembrolizumab combined with chemotherapy (based on 5-fluorouracil, which can be combined with bevacizumab or cetuximab) as the first-line treatment for patients with microsatellite highly unstable tumors, and results show that Pembrolizumab was associated with a longer progression-free survival (median 16.5 months and 8.2 months, hazard ratio 0.60, p < 0.001) and a longer average overall survival (13.7 months and 10.8 months) [6]. However, for the majority of LARC patients, tumor metastasis remains a major cause of death [7]. Furthermore, even in the era of immunotherapy, rectal cancer still has a high incidence rate and mortality rate. Therefore, more efficient prognostic models must be developed, despite the fact that tumor pathological staging plays an important role in prognosis prediction.

The human body requires copper as one of its trace elements. Tumors were discovered to have a much higher quantity of copper than the surrounding tissues [8]. This suggests that copper is crucial for the proliferation of tumor cells and that an imbalance in copper homeostasis can produce cytotoxicity and have an impact on the initiation and progression of malignancies [9–12]. A novel kind of cell death called cuproptosis differs from the well-known mechanisms of apoptosis, autophagy, and ferroptosis. Copper ions have the ability to cause cell death even in cases where the established mechanism is inhibited. Cuproptosis is the process by which lipoacylated proteins aggregate as a result of direct copper ion binding to lipoacylated tricarboxylic acid cycle components during mitochondrial respiration [13]. Clinical studies and earlier animal models have demonstrated that a copper deficit prevents the angiogenesis and growth of tumors [14]. But the precise process causing cuproptosis is still not fully understood. A copper-dependent anticancer drug (IC25 (10)) has strong antitumor effects both in vivo and in vitro in hematological malignancies [15]. Reduced mitochondrial oxidative phosphorylation inhibits the spread of triple negative breast cancer [16]. By inhibiting mTORC1/2, pancreatic cancer is more sensitive to rapamycin when copper is deficient [17]. Therefore, there is a lot of clinical utility for studying cuproptosis’s role in cancer.

The majority of single-stranded RNAs longer than 200 nucleotides, known as long noncoding RNAs (LncRNAs), are incapable of encoding proteins [18]. Through mediating chromosomal alteration, transcriptional activation, and interference, LncRNA affects a multitude of physiological and biochemical cellular processes [19]. Prior comprehensive investigations of LncRNAs have revealed their involvement in significant diseases and physiologies, including autophagy, differentiation, apoptosis, and the cell cycle [20]. According to reports, aberrant LncRNAs can be employed as prognostic markers for a number of different malignancies [21, 22]. For instance, by examining RNA-seq data, it was possible to identify DDX11-AS1, a prognostic LncRNA with therapeutic potential in hepatocellular cancer [23]. LncRNA reprogramming (LncRNA-ROR) functions as a normal LncRNA with anticancer effects in vivo and in vitro in pancreatic cancer. By stimulating miR-145, LncRNA-ROR can also prevent the proliferation of cancer cells [24]. However, there is currently limited research on cuproptosis-related LncRNAs and their association with the prognosis of Rectum adenocarcinoma (READ) patients, making this an area worthy of further investigation. The aim of this study is to identify LncRNA markers associated with cuproptosis that could enhance current approaches to diagnosing, treating, monitoring, and preventing Rectum adenocarcinoma. Cuproptosis-related LncRNAs were identified through bioinformatics analysis, and their biological functions and potential roles in prognosticating READ outcomes were explored.

Methods and materials

Figure 1 presented a detailed flowchart outlining the various steps involved in our investigation. A total of 164 tumor tissue samples, along with relevant clinical data, were obtained from the TCGA database.

Fig. 1.

Fig. 1

Open in a new tab

The overall experimental flow chart of our study

Data processing and identifcation of cuproptosis‑related LncRNAs

The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) provided RNA-Seq data on gene expression, clinical information, and somatic mutation data for 164 patients with Rectum adenocarcinoma [25]. RNA-Seq data were used in conjunction with TCGA gene annotations to identify long non-coding RNAs (LncRNAs). Cuproptosis-related genes were selected based on prior literature [26], with a focus on studies specifically related to cuproptosis [27]. To identify potential cuproptosis-related LncRNAs, we conducted a co-expression correlation analysis between LncRNAs and cuproptosis-related gene expression profiles using the limma package, with a Pearson correlation threshold of |Cor|> 0.4 and a significance level of P < 0.001.

Prognostic model construction

The TCGA-READ dataset was randomly divided into training and testing groups in a 7:3 ratio. To identify cuproptosis-related LncRNAs, univariate Cox regression analysis was performed on the training group, followed by LASSO Cox regression applied to significantly expressed LncRNAs using a 1000 ten-fold cross-validation. Multivariate Cox regression analysis was then conducted to select the optimal prognostic LncRNAs (P < 0.05). The best model parameters were used to generate the signature and calculate the associated risk scores. Lasso Risk Score = ∑n i = 1Coef ∗ iXi, where Xi is the normalized count of each LncRNA associated to cuproptosis and Coefi is the representation of the coefficients. Patients can receive a risk score that is based on the Lasso prognostic model.

Validation of risk models

To assess the prognostic value of the signature, patients were classified into high- and low-risk groups based on the median risk score. To evaluate the independent prognostic significance of the risk prediction signature, we performed both univariate and multivariate Cox regression analyses. Additionally, we used the survival package to calculate overall survival (OS) and progression-free survival (PFS) for Rectum adenocarcinoma (READ) patients across different groups. The expression heatmap of LncRNAs and the survival status of patients were visualized using the pheatmap package, based on their risk scores. Furthermore, the area under the ROC curve (AUC) for 1-, 3-, and 5-year survival predictions was calculated for the training, testing, and combined groups using the survivalROC package.

Establish and evaluate a nomogram

We evaluated the independent prognostic value of the risk model using both univariate and multivariate Cox regression analyses. Based on the results from these analyses, we constructed a nomogram using the R packages “rms”, “regplot”, and “survival”. To assess the accuracy of the nomogram, we generated a calibration curve.

Investigation of the association between the clinical index and the prognostic risk score

We investigated the association between the prognostic risk score and clinical indices using univariate and multivariate Cox regression analyses. This analysis aimed to identify correlations between the risk score and clinical factors, such as stage, age, and sex, to evaluate their potential roles in READ. The findings help determine whether the model is applicable to patients with different clinical characteristics.

Pathway enrichment analysis and gene set enrichment analysis

Using the R package “limma”, with the restricted condition set to log2 |fold change|> 1 and false discovery rate < 0.05, the differentially expressed genes (DEGs) between the high- and low-risk groups were found. We investigated the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) database pathways to elucidate the molecular activities and important signaling pathways using the R packages “clusterProfiler”, “org.Hs.eg.db”, and “enrichplot”. Using the R package “limma”, with the restricted condition set to log2 |fold change|> 1 and false discovery rate < 0.05, the differentially expressed genes (DEGs) between the high- and low-risk groups were found. We investigated the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) database pathways utilizing the R packages “clusterProfiler”, “org.Hs.eg.db”, and “enrichplot”.

Estimation of intertumoral immune cell infiltration and immunotherapy

We assessed immune cell infiltration and function using the single-sample gene set enrichment analysis (ssGSEA) function from the R software's genome variation analysis package. This analysis was conducted to explore the correlation between the risk score of cuproptosis-related LncRNA prognostic markers and immune cell infiltration, with the results visualized in an associated heatmap. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm (http://tide.dfci.harvard.edu) was applied to predict the response to immunotherapy by simulating tumor immune escape mechanisms. We then used the TIDE algorithm to evaluate the impact of immunotherapy in both high- and low-risk cohorts.

Calculation of tumor mutation burden scores

Tumor mutational burden (TMB) reflected the total number of mutations in a cancer. Using the “maftools” package in R, we analyzed the mutation data of READ samples obtained from TCGA. The relationship between risk scores and TMB in READ patients was visualized using a waterfall plot.

Copy number variation analysis

We used the “tcgabiolinks” package to download the somatic cell copy number variation data of rectum adenocarcinoma patients from the TCGA database, and then pre-processed the downloaded CNV data using GISTIC2.0 (https://broadinstitute.github.io/gistic2/). The obtained files were analyzed using the “maftools” and “tidyverse” packages.

Statistical analyses

The R programming language (Version 4.3.2) on R studio was used to process all statistical analyses. The “limma” packages in R were used to merge RNA-seq transcriptome data and somatic mutation data that were downloaded by “tcgabiolinks” package from TCGA. The relationships between cuproptosis-related LncRNAs and genes were examined using the Pearson correlation test. The “limma” R program was used to search for differential LncRNAs in the curproptosis-related LncRNA prognostic indicators. The R “survival” and “survminer” packages were used to carry out Cox regression and survival analysis. The hazard ratios of the univariate and multivariate analyses were determined using the Cox proportional risk regression model. The R package’s “clusterProfiler” was used to evaluate the KEGG pathways and GO keywords. Heat maps for cluster analysis were created using the “Pheatmap” R package. The Kaplan–Meier analysis with a log-rank test was used to assess the OS and PFS time of the various groups. The categorical data was compared between groups using the chi-square test. P values less than 0.05 were regarded as statistically significant.

Results

The identifcation of cuproptosis‑related LncRNAs in rectum adenocarcinoma patients from TCGA database

We removed the genes encoding proteins and identified 5495 LncRNAs of rectum adenocarcinoma patients in the TCGA database. By reading the many literature and refer to previous researches, we gained 22 cuproptosis-related genes, including NFE2L2 (NFE2 like bZIP transcription factor 2), NLRP3 (NLR family pyrin domain containing 3), ATP7B (ATPase copper transporting beta), ATP7A (ATPase copper transporting alpha), SLC31A1 (Solute carrier family 31 member 1), FDX1 (Ferredoxin 1), LIAS (Lipoic acid synthetase), LIPT1 (lipoyltransferase 1), LIPT2 (lipoyl (octanoyl) transferase 2), DLD (Dihydrolipoamide dehydrogenase), DLAT (Dihydrolipoamide S-acetyltransferase), PDHA1 (Pyruvate dehydrogenase E1 subunit alpha 1), PDHB (Pyruvate dehydrogenase E1 subunit beta), MTF1 (Metal regulatory transcription factor 1), GLS (Glutaminase), CDKN2A (Cyclin dependent kinase inhibitor 2A), DBT (Dihydrolipoamide branched chain transacylase E2), GCSH (Glycine cleavage system protein H), DLST (Dihydrolipoamide S-succinyltransferase), CCS (Copper chaperone for superoxide dismutase), COMMD1 (Copper metabolism domain containing 1), and ATOX1 (Antioxidant 1 copper chaperone). In this part, we gained the transcriptome data of 164 rectum adenocarcinoma patients from TCGA dataset, screened 421 cuproptosis-related LncRNAs via employing Pearson analysis, constructed a Sankey plot between cuproptosis-related genes and cuproptosis-related LncRNAs (Fig. 2A). Henceforth, cuproptosis-related LncRNAs (p < 0.05) were investigated using univariate COX regression analysis. Table 1 displayed the clinical data for the 164 patients who were split into two groups: the training group (n = 115) and the testing group (n = 49). The findings demonstrated that every clinical feature was the same for both the training and validation groups.

Fig. 2.

Fig. 2

Open in a new tab

Correlation analysis of cuproptosis-related genes and LncRNAs in rectum adenocarcinoma. A Sankey diagram of co-expression between cuproptosis-related genes and cuproptosis-related long noncoding RNA (LncRNAs). B Heatmap of cuproptosis-related genes and prognostic cuproptosis-related LncRNAs. *p < 0.05, **p < 0.01, and ***p < 0.001

Table 1.

Characteristic of rectum adenocarcinoma patients

Covariates Type Total dataset (n = 164) Training dataset (n = 115) Testing dataset (n = 49) P value
Gender Female 74(45.12%) 50(43.48%) 24(48.98%) 0.6336
male 90(54.88%) 65(56.52%) 25(51.02%)
Age  ≤ 65 81(49.39%) 56(48.7%) 25(51.02%) 0.9188
 > 65 83(50.61%) 59(51.3%) 24(48.98%)
T T1 9(5.49%) 5(4.35%) 4(8.16%) 0.4566
T2 28(17.07%) 22(19.13%) 6(12.24%)
T3 112(68.29%) 76(66.09%) 36(73.47%)
T4 14(8.54%) 11(9.57%) 3(6.12%)
unknow 1(0.61%) 1(0.87%) 0(0%)
M M0 125(76.22%) 88(76.52%) 37(75.51%) 0.9274
M1 23(14.02%) 17(14.78%) 6(12.24%)
unknow 16(9.76%) 10(8.7%) 6(12.24%)
N N0 84(51.22%) 60(52.17%) 24(48.98%) 0.7602
N1 44(26.83%) 29(25.22%) 15(30.61%)
N2 33(20.12%) 24(20.87%) 9(18.37%)
unknow 3(1.83%) 2(1.74%) 1(2.04%)
Stage Stage I 30(18.29%) 21(18.26%) 9(18.37%) 0.9404
Stage II 51(31.1%) 37(32.17%) 14(28.57%)
Stage III 51(31.1%) 35(30.43%) 16(32.65%)
Stage IV 24(14.63%) 18(15.65%) 6(12.24%)
unknow 8(4.88%) 4(3.48%) 4(8.16%)
Open in a new tab

Construction of prognostic model of the cuproptosis-related LncRNAs

Using the expression levels of the cuproptosis-related LncRNAs and the outcomes of patients, a prognostic model was constructed for rectum adenocarcinoma. In Table 2 and Table S1, Lasso Cox regression analysis identified cuproptosis-related LncRNAs, univariate Cox regression analysis identified 10 LncRNAs, and multivariate COX analysis identified 4 LncRNAs as independent prognostic factors. These 4 LncRNAs as independent prognostic factors were significantly correlated with the expression of curoposis-related genes, except CDKN2A (Fig. 2B). As shown in Fig. 3, we found that the expression of LINC01555,

Table 2.

Results of univariate and multivariate cox regression analyses of cuproptosis-related LncRNAs in training group

LncRNAs Univariate cox regression Multivariate cox regression
HR HR (95% CI)L HR (95% CI)H Pvalue LncRNAs Coef
LINC01555 0.7100 0.5698 0.8846 0.0023 LINC01555 −0.2279
GPRACR 1.5673 1.0130 2.4250 0.0436 GPRACR 0.7059
LINC01142 1.8427 1.0513 3.2298 0.0328 NIHCOLE −0.2740
LYPLAL1-DT 1.4163 1.0079 1.9903 0.0449 MPC1-DT −0.5017
TMEM147-AS1 0.5072 0.2871 0.8959 0.0193
NIHCOLE 0.7304 0.5904 0.9036 0.0038
LINC02156 0.7056 0.5017 0.9923 0.0450
SMG1-DT 0.6200 0.3903 0.9850 0.0430
MPC1-DT 0.5861 0.3751 0.9158 0.0190
KLHL30-AS1 1.7623 1.0929 2.8415 0.0201
Open in a new tab

HR, hazard ratio; 95% CI, 95% confidence interval; L = lower limit, H = upper limit

Fig. 3.

Fig. 3

Open in a new tab

Kaplan–Meier curve of independent prognostic LncRNAs of rectum adenocarcinoma patients between low expression and high expression group. The effects of the expression of LINC01555 (A), GPRACR (B), NIHCOLE (C), and MPC1-DT (D) on overall survival in rectum adenocarcinoma patients

NIHCOLE, and MPC1-DT were all significantly associated with great survival prognosis in rectum adenocarcinoma patients, while the elevated expression of GPRACR contributed to poor prognosis. In particular, we found that the expression of NIHCOLE was negatively correlated with the expression of CDKN2A gene, and the expression of the remaining lcnRNA was positively correlated with the expression of genes associated with copper death. The risk score of each sample was calculated based on the expression of 4 independent prognostic LncRNAs indentified by multivariate cox regression. The risk score for each patient was calculated by the formula: Risk score = (−0.2279*ExpressionLINC01555) + (0.7059*ExpressionGPRACR) + (−0.2740*ExpressionNIHCOLE) + (−0.5017*ExpressionMPC1-DT). Figure 4A showed the distribution of coefficients for each item included in the LASSO regression model. Figure 4B exhibited LASSO regression screened of cuproptosis-related LncRNAs at the minimum point of cross-validation. The results of univariate regression analysis of the cuproposis-related LncRNAs (p < 0.05) by using forest plots was shown in Fig. 4C. Forest plot showed different LncRNAs for high and low risk, with red representing high-risk LncRNAs and green representing low-risk LncRNAs. The correlation heatmap also showed the relationship between the cuproptosis-related genes and LncRNAs.

Fig. 4.

Fig. 4

Open in a new tab

Construction of the prognostic cuproptosis-related LncRNAs risk model in rectum adenocarcinoma. A, B Lasso–Cox regression analysis was used to conduct prognostic prediction models. C Univariate Cox regression analysis for identifying the prognostic cuproptosis-related LncRNAs

Risk score distribution and survival status of rectum adenocarcinoma patients in low and high-risk groups

In order to explore the relationship between risk score of LncRNAs and OS of rectum adenocarcinoma patients, we firstly employed heatmap to exhibit the correlation between LncRNAs expression and risk score (Fig. 5A, D, and G), used point plot to divided all samples into low and high-risk group (Fig. 5B, E, and H), and used point diagram to show the relation between survival statue and risk core (Fig. 5C, F, and I). We found that there was a positive correlation between the expression of GPRACR of majority patients and risk score, whereas the expression of LINC01555, NIHCOLE, and MPC1-DT of majority patients negatively related to risk score based on the prediction model. We also discovered that there was a greater mortality rate among high-risk patients compared to low-risk patients, as indicated by the risk curves that represented the correlation between the risk score and survival status of patients with rectum adenocarcinoma.

Fig. 5.

Fig. 5

Open in a new tab

Risk score distribution and survival status of rectum adenocarcinoma patients in low and high-risk groups. A–C Heatmap, risk curves, and survival statue of all patients with rectum adenocarcinoma. D–F Heatmap, risk curves, and survival statue of rectum adenocarcinoma patients in training set. G-I Heatmap, risk curves, and survival statue of rectum adenocarcinoma patients in testing set

Independent prognostic analysis and construction of nomogram

Based on previous results, we identified four LncRNAs (GPRACR, LINC01555, NIHCOLE, and MPC1-DT) that were associated with cuproptosis and had independent prognostic value for overall survival in patients with rectum adenocarcinoma (Fig. 3). In this part, univariate and multivariate Cox regression analyses were performed to assess the predictive value of the prognostic model. Univariate Cox regression analysis exhibited that Age, T, N, M, stage, and risk score had a significant impact on the overall survival of rectum adenocarcinoma patients (Fig. 6A). In multivariate Cox regression analysis, only Age, M, and risk score remained prognostic value for OS (Fig. 6B). The cuproptosis-related LncRNAs for OS were shown to be accurate and valuable as diagnostic tools by the ROC curves; the AUC was 0.812 at 1 year, 0.792 at 2 years, and 0.793 at 3 years (Fig. 6C and Table S2). Both C-index and ROC curve indicated the predictive accuracy of the prognostic model was superior to other clinical indexes including age, gender, T, N, M, and stage (Fig. 6D, 6E, and Table S2). A nomogram plot is a predictive tool for quantitative analysis of clinical outcomes in patients with rectum adenocarcinoma. Thus, we initiated a prognostic nomogram based on the risk score and other clinical characteristics (Fig. 6F and Figure S1), and we found that the risk score in our predictive model and the age of patients contributed significantly to the overall survival prognosis of patients with rectum adenocarcinoma.

Fig. 6.

Fig. 6

Open in a new tab

Independent prognostic analysis of overall survival of rectum adenocarcinoma patients and construction of Nomogram. A Univariate Cox analysis of clinical features and risk score. B multivariate Cox analysis of clinical features and risk score. C TimeROC curve predicted 1, 3, and 5 years of OS for rectum adenocarcinoma patients. D ROC curve of the predictive accuracy of the risk model and clinical parameters. E C-index of the predictive accuracy of the risk model and clinical parameters. F A nomogram of predict prognosis was constructed based on the cuproptosis-related LncRNAs prognostic marker. (1 = female, 2 = male)

Kaplan–Meier curve analysis and subgroup validation

In order to explore the predictive value of the constructed model (identifying the cuproptosis-related LncRNAs prognostic biomarks) for overall survival prognosis, we analyzed the difference between the two groups according to the high-low risk stratification of the predictive model in all samples (Fig. 7A), training sets (Fig. 7B), and test sets (Fig. 7C), respectively, and found that the prognosis of patients in the high-risk group was worse than that in the low-risk group. Similarly, we further explored the differences of PFS between the two stratified patients. In Fig. 7D, E, Kaplan–Meier curve was used to show the differences of PFS of rectum adenocarcinoma patients in the groups of the prediction model respectively. The results were also consistent with OS, and the prognosis in the low-risk group was better than that in the high-risk patients. Afterwards, in order to examine the clinical usefulness of the cuproptosis-related LncRNAs prognostic biomarks, we investigated the correlation between the cuproptosis-related LncRNAs prognostic biomarks and clinical features. The findings showed that the distribution of risk scores and clinical stages differed significantly; in particular, stages I–II and III–IV (p < 0.05, Fig. 7G, H), Female and male (p < 0.05, Fig. 7I, J), Age ≤ 65 and Age > 65 (p < 0.05, Fig. 7K, L) were statistically significant. Therefore, we know that regardless of early or advanced rectum adenocarcinoma patients, the prediction model constructed by our cuproptosis-related LncRNAs can significantly distinguish between the patients with good prognosis and the population with poor prognosis, and such a good prediction is also applicable to different genders and different age groups of rectum adenocarcinoma patients. These positive results further validate the reliability of our predictive model based on cuproptosis-related LncRNAs.

Fig. 7.

Fig. 7

Open in a new tab

Kaplan–Meier curves of rectum adenocarcinoma patients of low and high-risk group. The OS analysis of rectum adenocarcinoma patients with high or low-risk in all sample (A), training group (B), or testing group (C). D The PFS analysis of all rectum adenocarcinoma patients with high or low-risk. E The PFS analysis of rectum adenocarcinoma patients with high or low-risk in training group. F The PFS analysis of rectum adenocarcinoma patients with high or low-risk in testing group. The Kaplan–Meier curves showed the OS analysis of rectum adenocarcinoma patients with stage Ⅰ-Ⅱ (G), stage Ⅲ-Ⅳ (H), male (I), female (J), Age ≤ 65 (K), or Age > 65 (L) between high and low-risk group

GO and KEGG enrichment of differentially expressed genes between the low-risk and high-risk group

We carried out GO and KEGG enrichment analyses in order to investigate the biological activities and pathway analysis of the differentially expressed genes between the high- and low-risk groups. A total of 231 DEGs were identified. Within the biological process category, the genes were predominantly grouped in response to mesoderm development, positive regulation of amine transport, and muscle organ morphogenesis. It was primarily abundant in the myofibril, contractile fiber, and sarcomere under the category of cellular components (Fig. 8A–C). Using bar, bubble, and circular charts, the enriched items were displayed, genes in the KEGG category were enriched in the following categories: Cytoskeleton in muscle cells, Neuroactive ligand-receptor interaction, Vascular smooth muscle contraction, and Motor proteins (Fig. 8D–F).

Fig. 8.

Fig. 8

Open in a new tab

GO and KEGG pathway enrichment analysis. A Bubble chart of the top 5 GO enrichment terms. B Bar plot of the top 5 GO enrichment terms. C Circle diagram of GO enrichment analysis. D Bubble chart of the top 4 KEGG enrichment terms. E Bar plot of the top 4 KEGG enrichment terms. F Circle diagram of KEGG enrichment analysis

Gene mutation and TMB analysis between low and high-risk group

In an effort to look into the possible significance of tumor mutation load in rectum adenocarcinoma patients, we gathered information on somatic mutation in these patients and computed corresponding TMB scores. As shown in Fig. 9A, the somatic mutation waterfall exhibited that APC, TTN and TP53 were the top 3 mutated genes in rectum adenocarcinoma patients with low-risk score; while in rectum adenocarcinoma patients with high-risk score, APC, TP53, and KRAS were the top 3 mutated genes (Fig. 9B). The tumor mutation burden for each sample was displayed in Fig. 9C. As shown in Fig. 9D, the high-risk group had a lower mutation load than the low-risk group in rectum adenocarcinoma patients. Using median cutoff values, we separated the patients into “High-TMB” and “Low-TMB” groups and ran survival analyses. The findings demonstrated that among patients with rectum adenocarcinoma, the high-risk group had a greater survival rate than the low-risk group (Fig. 9E). The combined survival curve can be obtained by combining the tumor mutation load and risk scores in a survival analysis. It was discovered that the OS of patients with rectum adenocarcinoma was significantly influenced by TMB and risk score. According to Fig. 9F, patients who had a low-risk score along with a high TMB group had the longest survival when compared to the other three stratification groups.

Fig. 9.

Fig. 9

Open in a new tab

Mutational landscape in rectum adenocarcinoma patients and relationship between TMB and risk score. A The waterfall bolt of gene mutation of rectum adenocarcinoma patients in low-risk group. B The waterfall bolt of gene mutation of rectum adenocarcinoma patients in high-risk group. C TMB of all samples. D The comparison of TMB of rectum adenocarcinomas between low-risk and high-risk group (Y-axis represented TMB (mut/Mb)). E Survival analysis between low-TMB and high-TMB group. F Survival analysis of rectal patients with low-risk or high-risk between low-TMB and high-TMB group

Immunological landscape in rectum adenocarcinoma patients and relationship between immune cell molecule and risk score

The single-sample gene set enrichment analysis (ssGSEA) algorithms function package in the R software genome variation analysis package was used to assess the infiltration and function of tumor-infiltrating immune cells in order to examine the relationship between the cuproptosis-related LncRNAs’ risk score and immune cell infiltration. The immune response heatmap based on the ssGSEA algorithm was displayed in Fig. 10A. The correlation analysis between immune cell populations and related functions, based on the ssGSEA of the TCGA-READ data, showed that the high- and low-risk groups differed significantly in type I tumor necrosis factor (TNF). TIDE algorithm scores for all samples were shown in Fig. 10B. There was a significant difference in TIDE scores between the high and low-risk group (Fig. 10C). However, the score of TAM_M2, INFG, and CD8 all existed in an obvious difference between high-risk and low-risk group (Fig. 10D–F).

Fig. 10.

Fig. 10

Open in a new tab

Immunological landscape in rectum adenocarcinoma patients and relationship between immune cell molecule and risk score. A Heatmap of the tumor-infiltrating lymphocytes based on single-sample gene set enrichment analysis algorithms among the high- and low-risk groups in rectum adenocarcinoma patients. B TIDE prediction score of all samples. C Comparison of TIDE prediction score between the high- and low-risk groups. D Comparison of TAM_M2 prediction score between the high- and low-risk groups via using TIDE algorithm. E Comparison of IFNG prediction score between the high- and low-risk groups via using TIDE algorithm. F Comparison of CD8 prediction score between the high- and low-risk groups via using TIDE algorithm. *p < 0.05, **p < 0.01, and ***p < 0.001

Copy number variation (CNV) analysis of rectum adenocarcinoma patients from TCGA dataset

We preprocessed CNV data by using GISTIC2.0 (https://broadinstitute.github.io/gistic2/) and obtained chromosomal maps of amplified (Fig. 11A) and deleted (Fig. 11B) genes, respectively, and used heat maps to present CNV profiles for all genes (Fig. 11C). The horizontal coordinate of the bubble plot was the number of samples with copy number variation in each region, and the vertical coordinate is the number of genes contained in each region, and the size was measured by the q value of each region (Fig. 11D). Chromosomal maps with G-score and chromosomal coordinates highlighted significant amplification and deletion areas with colored sections (Fig. 11E). Figure 11F showed how much copy number variation occurs in each region in a waterfall plot, where one row was a region and one column were a sample. In order to explore CNV of the cuproptosis -related genes in rectum adenocarcinoma patients, we employed the lollipop chart to showed the frequency of amplification and deletion of 20 cuproptosis-related genes (Fig. 11G). The amplification frequency of ATP7B and DLD was much higher than that of other cuproptosis-related genes. In order to further explore the functional differences between amplified and deleted genes, we performed GO and KEGG enrichment analysis for amplified or deleted genes in patients with rectum adenocarcinoma, respectively. The GO enrichment analysis of the amplified genes primarily involved the processes of calcium-dependent protein binding, keratinization, and cornified envelope formation (Fig. 11H). Mainly involved in the PI3K-Akt signaling pathway and spliceosome were the amplified genes’ KEGG enrichment analysis results (Fig. 11I). The findings of the GO enrichment of the deleted genes, as indicated in Fig. 11J, were primarily related to DNA-binding transcription activator activity, homophilic cell adhesion via plasma membrane adhesion molecules, cell–cell adhesion via plasma membrane adhesion molecules, and RISC complex. The MAPK signaling pathway was mostly involved in the deleted genes’ KEGG enrichment analysis results (Fig. 11K).

Fig. 11.

Fig. 11

Open in a new tab

Copy number variation (CNV) analysis of rectum adenocarcinoma patients from TCGA dataset. A CNV analysis of amplified genes. B CNV analysis of deleted genes. C Heatmap of CNV analysis of genes in all samples. D Bubble plot of CNV of genes. E Chromosomal plot of gene copy number variation analysis. F Waterfall plot of CNV of all genes. G Lollipop plot of CNV of cuproptosis-related genes. The GO (H) and KEGG (I) enrichment analysis of amplified genes were showed in the bar chart and bubble chart. The GO (J) and KEGG (K) enrichment analysis of deleted genes were showed in the bar chart and bubble chart

Discussion

Patients with advanced and metastatic READ still have a terrible prognosis, even with recent advancements in surgery, radiation, and chemotherapy. Cuproptosis aids in the removal of damaged cells and defeats the chemotherapy-resistant nature of cancerous cells [28]. Cuproptosis may therefore prove to be a useful treatment for a variety of cancers in the future. Furthermore, LncRNA influences cancer growth and therapy via biological mechanisms [29]. LncRNAs have been discovered to be a significant factor in the prognosis of READ and could be a useful molecular target for READ therapy [30]. But cuproptosis’s regulation mechanisms are still mostly a mystery, particularly in the context of LncRNA. Therefore, in order to identify putative prognostic markers, we should concentrate on the possible interplay between LncRNA and cuproptosis.

Four of the ten prognostic cuproptosis-related LncRNAs that we discovered in the current study were chosen for prognostic construction to predict OS in READ patients. We first identified 421 cuproptosis-related LncRNAs and 22 cuproptosis-related genes. Next, in order to determine the prognostic cuproptosis-related LncRNAs, we employed Lasso regression and COX regression. Furthermore, we investigated the connections between prognostic markers associated to cuproptosis and common clinical characteristics, upstream regulatory mechanisms, somatic mutation, copy number variation, immune cell infiltration, and READ immunotherapy. Based on Cox, Lasso, and multivariate Cox regression analyses, we identified four LncRNAs associated with prognosis, including NIHCOLE, GPRACR, LINC01555, and MPC1-DT. In particular, we found that in the heatmap of prognostic LncRNA and cuproptosis-related genes (Fig. 2A), NIHCOLE, as a LncRNA with independent prognostic significance, was negatively correlated with the expression of cuproptosis-related gene CDKN2A. Previous studies showed that deletion of CDKN2A on chromosome 9 promotes the progression of lung cancer, resulting in poor clinical prognosis [31]. As a poor prognostic indicator, CDKN2A has been shown to stimulate epithelial–mesenchymal transition and facilitate rectum adenocarcinoma spread [32, 33]. It may be concluded that NIHCOLE is a LncRNA linked to a favorable prognosis based on the genetic features of CDKN2A that have been demonstrated in prior research as well as our experimental findings. Actually, we discovered that patients with rectum adenocarcinoma who expressed a lot of NIHCOLE had longer overall survival times in our examination of OS survival data. Consistent with our results in patients with rectum adenocarcinoma, previous research discovered that NIHCOLE increased the efficiency of DNA double-strand breaks in hepatocellular carcinoma, impairing tumor growth [34]. In our study, NIHCOLE and CDKN2A were combined to predict the prognosis of rectum adenocarcinoma and performed well. Studies have shown that LncRNA DLGAP1-AS2 activated the AKT/mTOR/cyclinD1 pathway and interacts with E2F1 to upregulate the expression of CD151, thereby accelerating the radioresistance of rectal cancer cells [35]. scaRNA2 was the most enriched LncRNA that binds to the ATR protein, and its knockdown inhibited DNA damage repair by eliminating ATR activation and DSB excision-mediated HR repair, increased tumor sensitivity to radiotherapy and chemotherapy, and enhanced therapeutic efficacy [36]. These pathways may be the mechanisms by which LncRNAs regulate the prognosis of rectal cancer patients. We still need further experiments to explore and obtain more reliable evidence.

Afterward, we confirm that the risk model is accurate. The OS of the high-risk group was found to be lower than that of the low-risk group, according to the Kaplan–Meier technique. The cuproptosis-related prognostic markers were highly accurate in predicting 1-, 3-, and 5-year survival, as seen by the ROC curves, and their AUC values were all higher than 0.79. For READ patients, a novel nomogram was developed to predict distant metastases, lymph node metastasis, and prognosis. Furthermore, the probable biological mechanism of the relevant cuproptosis-related prognostic indicators was discovered using functional enrichment analysis. These differentially elevated cuproptosis-related prognostic indicators were primarily enriched in the cytoskeleton in muscle cells, mesoderm development, and sarcomere, according to GO and KEGG analysis. The analyses of CNV data showed that PI3K/Akt signaling pathway may be one of the most important enrichment items in the KEGG signaling pathway enrichment analysis of amplified genes in rectum adenocarcinoma patients, while MAPK signaling pathway is the main enrichment item in the enrichment analysis of deleted genes. Rectal cancer is one of the cancer types where there is the close link between CNV and cuproptosis gene expression, according to a pan-cancer genomic investigation of cuproptosis [37]. Our results showed that ATP7B was the cuproptosis-related gene with the most significant copy number variation, suggesting that it was the main link between cuproptosis-related prognostic lcnRNA and CNV in patients with rectum adenocarcinoma.

Our results indicated that the low-risk group’s TMB was much higher than the high-risk groups, indicating that patients with a reduced READ risk benefit more from immunotherapy. Moreover, we found that the OS of rectum adenocarcinoma patients with low risk scores combined with high mutation load had the longest overall survival, compared to the OS of rectum adenocarcinoma patients in other groups. The two most frequently mutated genes among the top 15 were APC and TP53, with mutations being more common in both the high- and low-risk categories of READ patients. In addition to being involved in the normal physiology and metabolism of diabetes, liver, and cardiovascular disorders, TP53 is a frequently mutated oncogene in human malignancies and the survival of patients with various malignancies is linked to TP53 [38, 39]. It affects the development of cancers of the lung [40], bladder [41], prostate [42], and colorectal regions [43]. It is commonly recognized that the prognosis of malignancies is strongly correlated with the number of immune cells present [44]. In this study, we discovered that individuals with rectum adenocarcinoma who had high and low risk ratings had distinct levels of immune and stromal cells. The infiltration and function of tumor-infiltrating immune cells were evaluated using the single-sample gene set enrichment analysis (ssGSEA) algorithms function package in the R software genome variation analysis package. We found that the score of type I tumor necrosis factor (TNF) of rectum adenocarcinoma patients in high-risk group were obviously higher than that in the low-risk group. Furthermore, we employed the TIDE algorithm to evaluate the score of TAM_M2, INFG, and CD8, finding that the scores of INFG and CD8 in low-risk group were significantly higher than that in the high-risk group, while the scores of TAM_M2 in low-risk group were markedly lower than that in the high-risk group.

Limitations

A novel type of cell death called cuproptosis could be crucial to the development of new cancer therapies. However, several LncRNAs have a range of biological effects on how cancer progresses and is treated. But there’s still a lot to learn about the relationship between LncRNA and cuproptosis. Overall, from the standpoint of cuproptosis, this work offers fresh insights into the genesis and development of READ tumors. Cuproptosis biomarkers were investigated in order to determine how READ patients would fare and to help guide therapy decisions. We also candidly acknowledge the limitations of a single database and the inherent biases resulting from the retrospective nature of data input. However, we inevitably used only the TCGA validation and entire cohorts, and additional patients could improve the reliability of the model; thus, further validation is needed through preclinical studies. In the meantime, our study needs further validation in vivo via using RT-PCR and western blotting to test the mRNA and protein expression levels of cuproptosis-related genes, and in vitro by constructing animal model. It would be more convincing if new patient information data sets could be collected in the future to further validate the results of this study.

Conclusion

In summary, we discovered a risk model that correctly predicted the prognosis of READ and was based on four cuproptosis-related LncRNAs. As a result, our research offered a fresh possible approach to treating READ patients’ immunotherapy response and tailored therapy. These four LncRNAs, including GPRACR, LINC01555, NIHCOLE, and MPC1-DT, connected to cuproptosis might be READ treatment targets.

Supplementary Information

Supplementary Material 1. (593.7KB, rar)

Acknowledgements

Not applicable.

Abbreviations

LncRNA

Long noncoding RNA

READ

Rectum adenocarcinoma

OS

Overall survival

PFS

Progression-free survival

TMB

Tumor mutation burden

TCGA

The Cancer Genome Atlas

KEGG

Kyoto encyclopedia of genes and genomes enrichment

TIDE

Tumor immune dysfunction and exclusion

Author contributions

Conceptualization, Minghe Lv; Data curation, Minghe Lv; Formal analysis, Minghe Lv; Investigation, Minghe Lv; Methodology, Minghe Lv and Ruping Zhao; Project administration, Minghe Lv and Ruping Zhao; Software, Minghe Lv; Supervision, Minghe Lv and Ruping Zhao; Validation, Minghe Lv; Visualization, Minghe Lv; Writing—original draft, Minghe Lv; Writing—review & editing, Minghe Lv and Ruping Zhao.

Funding

This work was supported by Science and technology development project of Shanghai University of Traditional Chinese Medicine (Grant Number: 24KFL065).

Data availability

The datasets generated and analyzed during the current study are available in TCGA dataset.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All the authors declare their consent to the publication of the article.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12–49. 10.3322/caac.21820. [DOI] [PubMed] [Google Scholar]
  • 2.Gérard JP, Conroy T, Bonnetain F, et al. Preoperative radiotherapy with or without concurrent fluorouracil and leucovorin in t3-4 rectal cancers: results of ffcd 9203. J Clin Oncol. 2006;24(28):4620–5. 10.1200/JCO.2006.06.7629. [DOI] [PubMed] [Google Scholar]
  • 3.Bosset JF, Collette L, Calais G, et al. Chemotherapy with preoperative radiotherapy in rectal cancer. N Engl J Med. 2006;355(11):1114–23. 10.1056/NEJMoa060829. [DOI] [PubMed] [Google Scholar]
  • 4.Underwood PW, Ruff SM, Pawlik TM. Update on targeted therapy and immunotherapy for metastatic colorectal cancer. Cells. 2024. 10.3390/cells13030245. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Vilar E, Gruber SB. Microsatellite instability in colorectal cancer-the stable evidence. Nat Rev Clin Oncol. 2010;7(3):153–62. 10.1038/nrclinonc.2009.237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.André T, Shiu KK, Kim TW, et al. Pembrolizumab in microsatellite-instability-high advanced colorectal cancer. N Engl J Med. 2020;383(23):2207–18. 10.1056/NEJMoa2017699. [DOI] [PubMed] [Google Scholar]
  • 7.Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Transl Oncol. 2021;14(10):101174. 10.1016/j.tranon.2021.101174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Ishida S, Andreux P, Poitry-Yamate C, Auwerx J, Hanahan D. Bioavailable copper modulates oxidative phosphorylation and growth of tumors. Proc Natl Acad Sci U S A. 2013;110(48):19507–12. 10.1073/pnas.1318431110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Liu WQ, Lin WR, Yan L, Xu WH, Yang J. Copper homeostasis and cuproptosis in cancer immunity and therapy. Immunol Rev. 2024;321(1):211–27. 10.1111/imr.13276. [DOI] [PubMed] [Google Scholar]
  • 10.Ge EJ, Bush AI, Casini A, et al. Connecting copper and cancer: from transition metal signalling to metalloplasia. Nat Rev Cancer. 2022;22(2):102–13. 10.1038/s41568-021-00417-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Jiang Y, Huo Z, Qi X, Zuo T, Wu Z. Copper-induced tumor cell death mechanisms and antitumor theragnostic applications of copper complexes. Nanomedicine. 2022;17(5):303–24. 10.2217/nnm-2021-0374. [DOI] [PubMed] [Google Scholar]
  • 12.Yang L, Yu J, Tao L, et al. Cuproptosis-related lncrnas are biomarkers of prognosis and immune microenvironment in head and neck squamous cell carcinoma. Front Genet. 2022;13:947551. 10.3389/fgene.2022.947551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Chang WH, Lin HH, Tsai IK, et al. Copper centers in the cryo-EM structure of particulate methane monooxygenase reveal the catalytic machinery of methane oxidation. J Am Chem Soc. 2021;143(26):9922–32. 10.1021/jacs.1c04082. [DOI] [PubMed] [Google Scholar]
  • 14.Yu Z, Zhou R, Zhao Y, et al. Blockage of SLC31A1-dependent copper absorption increases pancreatic cancer cell autophagy to resist cell death. Cell Prolif. 2019;52(2):e12568. 10.1111/cpr.12568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Chen G, Niu C, Yi J, et al. Novel triapine derivative induces copper-dependent cell death in hematopoietic cancers. J Med Chem. 2019;62(6):3107–21. 10.1021/acs.jmedchem.8b01996. [DOI] [PubMed] [Google Scholar]
  • 16.Ramchandani D, Berisa M, Tavarez DA, et al. Copper depletion modulates mitochondrial oxidative phosphorylation to impair triple negative breast cancer metastasis. Nat Commun. 2021;12(1):7311. 10.1038/s41467-021-27559-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Geng R, Ke N, Wang Z, et al. Copper deprivation enhances the chemosensitivity of pancreatic cancer to rapamycin by mtorc1/2 inhibition. Chem Biol Interact. 2023;382:110546. 10.1016/j.cbi.2023.110546. [DOI] [PubMed] [Google Scholar]
  • 18.Gao N, Li Y, Li J, et al. Long non-coding rnas: the regulatory mechanisms, research strategies, and future directions in cancers. Front Oncol. 2020;10:598817. 10.3389/fonc.2020.598817. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Statello L, Guo CJ, Chen LL, Huarte M. Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol. 2021;22(2):96–118. 10.1038/s41580-020-00315-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Meryet-Figuière M, Lambert B, Gauduchon P, et al. An overview of long non-coding rnas in ovarian cancers. Oncotarget. 2016;7(28):44719–34. 10.18632/oncotarget.8089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Ai W, Li F, Yu HH, Liang ZH, Zhao HP. Up-regulation of long noncoding RNA LINC00858 is associated with poor prognosis in gastric cancer. J Gene Med. 2020;22(7):e3179. 10.1002/jgm.3179. [DOI] [PubMed] [Google Scholar]
  • 22.Jiang W, Song Y, Zhong Z, Gao J, Meng X. Ferroptosis-related long non-coding rna signature contributes to the prediction of prognosis outcomes in head and neck squamous cell carcinomas. Front Genet. 2021;12:785839. 10.3389/fgene.2021.785839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Shi M, Zhang XY, Yu H, et al. Ddx11-as1 as potential therapy targets for human hepatocellular carcinoma. Oncotarget. 2017;8(27):44195–202. 10.18632/oncotarget.17409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Gao S, Wang P, Hua Y, et al. Ror functions as a cerna to regulate nanog expression by sponging mir-145 and predicts poor prognosis in pancreatic cancer. Oncotarget. 2016;7(2):1608–18. 10.18632/oncotarget.6450. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Hutter C, Zenklusen JC. The Cancer Genome Atlas: creating lasting value beyond its data. Cell. 2018;173(2):283–5. 10.1016/j.cell.2018.03.042. [DOI] [PubMed] [Google Scholar]
  • 26.Aubert L, Nandagopal N, Steinhart Z, et al. Copper bioavailability is a KRAS-specific vulnerability in colorectal cancer. Nat Commun. 2020;11(1):3701. 10.1038/s41467-020-17549-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Tsvetkov P, Coy S, Petrova B, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science. 2022;375(6586):1254–61. 10.1126/science.abf0529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Wang Y, Chen Y, Zhang J, et al. Cuproptosis: a novel therapeutic target for overcoming cancer drug resistance. Drug Resist Updat. 2024;72:101018. 10.1016/j.drup.2023.101018. [DOI] [PubMed] [Google Scholar]
  • 29.Tan YT, Lin JF, Li T, Li JJ, Xu RH, Ju HQ. LNCRNA-mediated posttranslational modifications and reprogramming of energy metabolism in cancer. Cancer Commun. 2021;41(2):109–20. 10.1002/cac2.12108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Ban Y, Tan P, Cai J, et al. Lncarod is stabilized by m6a methylation and promotes cancer progression via forming a ternary complex with hspa1a and ybx1 in head and neck squamous cell carcinoma. Mol Oncol. 2020;14(6):1282–96. 10.1002/1878-0261.12676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Liu W, Zhuang C, Huang T, et al. Loss of cdkn2a at chromosome 9 has a poor clinical prognosis and promotes lung cancer progression. Mol Genet Genomic Med. 2020;8(12):e1521. 10.1002/mgg3.1521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Dong Y, Zheng M, Wang X, Yu C, Qin T, Shen X. High expression of cdkn2a is associated with poor prognosis in colorectal cancer and may guide pd-1-mediated immunotherapy. BMC Cancer. 2023;23(1):1097. 10.1186/s12885-023-11603-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Shi WK, Li YH, Bai XS, Lin GL. The cell cycle-associated protein cdkn2a may promotes colorectal cancer cell metastasis by inducing epithelial-mesenchymal transition. Front Oncol. 2022;12:834235. 10.3389/fonc.2022.834235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Unfried JP, Marín-Baquero M, Rivera-Calzada Á, et al. Long noncoding rna nihcole promotes ligation efficiency of dna double-strand breaks in hepatocellular carcinoma. Cancer Res. 2021;81(19):4910–25. 10.1158/0008-5472.CAN-21-0463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Xiao SY, Yan ZG, Zhu XD, Qiu J, Lu YC, Zeng FR. LncRNA DLGAP1-AS2 promotes the radioresistance of rectal cancer stem cells by upregulating CD151 expression via E2F1. Transl Oncol. 2022;18:101304. 10.1016/j.tranon.2021.101304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Chen Y, Shen H, Liu T, et al. Atr-binding lncrna scarna2 promotes cancer resistance through facilitating efficient dna end resection during homologous recombination repair. J Exp Clin Cancer Res. 2023;42(1):256. 10.1186/s13046-023-02829-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Liu H, Tang T. Pan-cancer genetic analysis of cuproptosis and copper metabolism-related gene set. Front Oncol. 2022;12:952290. 10.3389/fonc.2022.952290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Donehower LA, Soussi T, Korkut A, et al. Integrated analysis of tp53 gene and pathway alterations in The Cancer Genome Atlas. Cell Rep. 2019;28(5):1370–84. 10.1016/j.celrep.2019.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Mcgrail DJ, Pilié PG, Rashid NU, et al. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann Oncol. 2021;32(5):661–72. 10.1016/j.annonc.2021.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Mogi A, Kuwano H. TP53 mutations in nonsmall cell lung cancer. J Biomed Biotechnol. 2011;2011:583929. 10.1155/2011/583929. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Siracusano S, Rizzetto R, Porcaro AB. Bladder cancer genomics. Urologia. 2020;87(2):49–56. 10.1177/0391560319899011. [DOI] [PubMed] [Google Scholar]
  • 42.Liu Z, Guo H, Zhu Y, et al. Tp53 alterations of hormone-naïve prostate cancer in the Chinese population. Prostate Cancer Prostatic Dis. 2021;24(2):482–91. 10.1038/s41391-020-00302-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Du L, Kim JJ, Shen J, Chen B, Dai N. KRAS and TP53 mutations in inflammatory bowel disease-associated colorectal cancer: a meta-analysis. Oncotarget. 2017;8(13):22175–86. 10.18632/oncotarget.14549. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Zhang Z, Bu L, Luo J, Guo J. Targeting protein kinases benefits cancer immunotherapy. Biochim Biophys Acta Rev Cancer. 2022;1877(4):188738. 10.1016/j.bbcan.2022.188738. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (593.7KB, rar)

Data Availability Statement

The datasets generated and analyzed during the current study are available in TCGA dataset.

Articles from Discover Oncology are provided here courtesy of Springer

RESOURCES