Prognostic value of NOB1 expression levels in various cancers: a systematic review and meta-analysis

Abstract

Aim: This study evaluates the prognostic significance of NOB1 expression levels in various cancers.

Patients & methods: Studies examining NOB1 expression in cancer, encompassing data from 1694 patients across 14 studies, were analyzed for overall survival (OS) and progression-free survival (PFS) using hazard ratios (HRs) and 95% CIs, and for clinicopathological parameters using relative risks (RRs).

Results: High NOB1 expression correlated with shorter OS (HR: 2.12, 95% CI: 1.82–2.48) and PFS (HR: 2.23, 95% CI: 1.62–3.07) and was associated with adverse tumor characteristics such as stage and metastasis.

Conclusion: Elevated NOB1 expression in various tumors signifies a poor prognosis, serving as a predictive marker for malignancy outcomes.

PROSPERO Register Number: CRD42023394051

Plain Language Summary

This study looked at how a protein called NOB1 affects cancer. NOB1 is usually found in healthy parts of the body like the lungs and liver, but it might also show up in cancer. We checked data from many sources to see if there's a connection between NOB1 and cancer's severity. After studying 1694 samples from 14 studies, we found that people with more NOB1 in their bodies had a tougher time with cancer. Their disease got worse quicker and they did not live as long as those with less NOB1. Also, higher NOB1 levels were linked to more serious and aggressive cancers. This means that NOB1 could help doctors predict how bad a patient's cancer is and plan treatments better.

Tweetable Abstract

A comprehensive meta-analysis indicates that elevated NOB1 expression is associated with shorter survival in various cancers. This discovery underscores the potential of NOB1 as a prognostic marker in malignant tumors. #CancerResearch #PrognosticMarker

Plain language summary

Article highlights.

• Cancer is a significant global health concern, leading to high mortality rates worldwide.

• Timely diagnosis, treatment and prognostic assessment of cancer patients remain challenging.

• The search for biological markers related to cancer occurrence, progression and prognosis is crucial.

• NOB1 gene, associated with cell cycle regulation and protein expression, has garnered attention for its potential role in cancer.

Methods

• A systematic literature search was conducted following PRISMA-P guidelines.

• Inclusion criteria involved cohort or case-control studies with confirmed malignancy diagnosis and available survival data.

• Data extraction and quality assessment were performed independently by two researchers.

• Statistical analyses were conducted using STATA software, focusing on HRs and RRs to evaluate NOB1's prognostic value and clinicopathological associations.

Results

• Literature screening yielded 14 eligible studies from China, covering nine cancer types and involving 1694 cases.

• Higher NOB1 expression levels were significantly associated with shorter OS and PFS in cancer patients.

• NOB1 expression correlated with tumor stage, degree of differentiation, lymph node metastasis and distant metastasis.

• Subgroup analyses showed consistent associations across different cancer types and sample sizes.

Discussion

• NOB1's aberrant expression in various cancers suggests its potential as a prognostic marker and therapeutic target.

• The exact molecular mechanisms underlying NOB1's role in tumorigenesis and progression remain unclear.

• Future research should address the limitations of existing studies and explore NOB1's prognostic value across a broader range of cancers.

Conclusion

• High NOB1 expression is associated with poor prognosis in various cancers, indicating its potential as a prognostic marker.

• NOB1 expression levels may help stratify patients for risk assessment and treatment planning.

• Further validation through larger, multicenter studies and standardized clinical trials is warranted to confirm NOB1's prognostic significance across different cancer types.

1. Background

Cancer is a major public health concern worldwide and is the leading cause of mortality in numerous countries [1,2]. The timely diagnosis, treatment and prognostic assessment of patients with cancer have been a persistent challenge in medicine for a long time. The poor life quality and the unsatisfactory 5-year survival of most patients with cancer are due to late diagnosis and treatment [3]. Therefore, searching for biological markers related to cancer occurrence, progression and prognosis is of great clinical significance.

While polygenic regulation leads to cancer occurrence and progression, the exact mechanism remains unknown. The NOB1 gene on chromosome 16q22.1 comprises nine exons and eight introns [4,5]. NOB1 is closely associated with the cell cycle and transcriptional regulation, and it is known to play a major role in the functional regulation of the 26S proteasome and protein expression [5,6]. Studies have reported roles for NOB1 in ribosome synthesis and cell cycle regulation [5–8]. NOB1 is highly expressed in tissues such as the spleen, lungs and liver. In contrast, it is lowly expressed in tissues such as the colon, kidney and ovaries and not expressed in the heart, brain, pancreas and peripheral blood [9]. Nonetheless, recent research has highlighted that NOB1 is aberrantly expressed in numerous tumors and associated with the prognosis of patients with certain tumors [10–12].

Data from in vivo and in vitro studies have revealed that knockdown of NOB1 can greatly inhibit tumor growth [13,14]. However, the molecular mechanisms through which NOB1 regulates tumorigenesis and progression remain to be clarified. Furthermore, most recent studies examining the association between NOB1 expression levels and the prognosis of patients with cancer had small sample sizes, and outcomes vary significantly between existing studies. Therefore, the prognostic value of NOB1 remains unknown. This study systematically collected published studies and, for the first time, evaluated the prognostic value of NOB1 in determining the prognosis of patients with cancer through meta-analysis, providing a reference for clinical diagnosis and risk assessment.

2. Methods

2.1. Literature search strategy

This study conducted a literature search according to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines [15]. The PRISMA-P table is provided as Supplementary Table S1. Two researchers independently searched the PubMed, Embase, Web of Science and Cochrane Library databases for articles published from their inception to 1 October 2023. All searches used a combination of subject terms and free words. The retrieval keywords were as follows: (‘NOB1’ OR ‘Nin one binding protein’ OR ‘NIN1/RPN12 binding protein 1 homolog’) AND (‘cancer’ OR ‘tumor’ OR ‘neoplasm’ OR ‘carcinoma’ OR ‘malignancy’) AND ‘prognosis’. Details of the search strategy are provided in the Supplementary Material (Supplementary Tables S2–S5). The references of the retrieved articles were manually searched to identify any missing articles.

2.2. Inclusion & exclusion criteria

2.2.1. Inclusion criteria

The inclusion criteria for this study were as follows: cohort or case-control studies, malignancy diagnosis confirmed by pathological methods, available hazard ratios (HRs) and their 95% CIs for overall survival (OS) and progression-free survival (PFS) or extractable Kaplan-Meier curves, a sample size of ≥20 patients, and published in the English language.

2.2.2. Exclusion criteria

The exclusion criteria for this study were as follows: reviews, commentaries, letters, conference reports and case reports; in vitro and animal studies; studies on hematological tumors; studies on genetic variants in the NOB1 gene (e.g., polymorphisms or methylation patterns); articles using sample cases from public databases; and unavailable survival data or duplicated data.

2.3. Data extraction

Two researchers independently screened the literature according to the inclusion and exclusion criteria and extracted the desired information from the articles. Information extracted from the articles included the first author's name, publication year, country, tumor type, sample size, follow-up duration, outcome indicators and clinically relevant pathological features. The result indicators were OS and PFS. HRs and their 95% CIs for OS and PFS already provided in the articles were directly extracted. If the outcomes were only represented by Kaplan-Meier curves in the article and no specific values were reported, the data from the Kaplan-Meier curves were extracted with the Engauge Digitizer software (version 4.1; http://markummitchell.github.io/engauge-digitizer) per the method reported by Tierney et al. [16]. The extracted clinicopathological parameters included age, sex, tumor stage, degree of tumor differentiation and lymph node metastasis. In case of disagreement during the data extraction, the two investigators held a joint decision, and a third investigator adjudicated if the discussion was not unanimous. The literature screening process is illustrated in Figure 1.

Figure 1.

Enhanced flow diagram of literature retrieval and selection for meta-analysis. This figure shows a detailed flowchart of the literature retrieval and selection process used in our meta-analysis. It includes the initial number of records identified, the number excluded after preliminary screening, the reasons for exclusion and the final number of studies included in the meta-analysis.

2.4. Quality evaluation of the articles

The quality of the included articles was independently evaluated by two investigators following the criteria of the Newcastle-Ottawa Scale (NOS) [17]. Each item was scored as 0 (no) or 1 (yes), with a total NOS score of 9. The two investigators jointly decided the final NOS score of each article, with disagreements resolved through mutual discussion or adjudicated by a third researcher. Articles with a NOS score of ≥6 were considered high quality and selected for this study.

2.5. Statistical analyses

The data were analyzed using the STATA software (version 12.0). The HRs and their 95% CIs were used to assess the relationship between NOB1 expression levels and the survival of patients with cancer. The relative risks (RRs) and their 95% CIs were used to evaluate the association between NOB1 expression levels and clinicopathological features. Heterogeneity in the studies was assessed using the chi-square test and the I² statistic [18]. Heterogeneity was considered high when the p-value was <0.05 for the chi-square test and the I² value was ≥50%. A random-effects model was used to combine HR and RR in the presence of high heterogeneity; otherwise, a fixed-effects model was used [19]. Begg's and Egger's tests were used to assess the significance of publication bias [20,21]. A p < 0.05 was considered statistically significant, except when testing heterogeneity.

3. Results

3.1. Literature screening & the basic characteristics of the included studies

The search strategy described in Section 2.1 identified 97, 89, 92 and 0 articles in the PubMed, Embase, Web of Science and Cochrane Library databases, respectively. After screening the identified articles against the inclusion and exclusion criteria specified in Section 2.2, 14 were selected for inclusion in this study (Figure 1). The 14 articles included in this study were all from China and published between 2013 and 2020 [22–35]. Their sample sizes ranged from 30 to 456, with a mean of 121. The included studies examined nine cancer types and involved 1694 cases. The features of all eligible articles are provided in Table 1.

Table 1.

Comprehensive characteristics of studies included in the meta-analysis.

Study (year)	Cancer type	Sample size	Follow-up (months)	Tumor stage	Outcome measures	Analysis type	Gender (female/male)	NOS
Jian-ping Che (2013)	Prostate carcinoma	132	67.9	83/49 (II/ III)	OS	U	0/132	7
Xiao-yu Li (2013)	BIDC	162	35	97/65 (I-II/III)	OS	U & M	162/0	7
Shan Lin (2013)	PTC	50	NA	30/20 (I-II/III-IV)	NA	NA	29/21	8
Hong-liang Wang (2013)	Gliomas	56	NA	26/30 (I-II/III-IV)	NA	NA	22/34	8
Kun Liu (2014)	NSCLC	70	60	47/23 (I-II/III)	OS, PFS	U & M	31/39	7
G. Liu (2014)	Prostate carcinoma	300	150	NA	RFS	U & M	0/300	8
Jian-wei Jia (2014)	Renal cancer	62	35	48/14 (I-II/III-IV)	OS	U	30/32	7
W-P Zhou (2014)	Gastric cancer	30	NA	14/16 (I-II/III-IV)	NA	NA	8/22	7
Jie Chen (2015)	Prostate carcinoma	456	35	300/146 (II/ III)	OS, RFS	U & M	0/456	8
Kun Liu (2015)	NSCLC	98	60	65/33 (I-II/III)	OS, PFS	U & M	20/78	8
Lei Wu (2015)	Colorectal cancer	114	60	NA	OS, PFS	U & M	NA	7
Ji-rong Yin (2015)	OSCC	50	NA	33/17 (I/II-IV)	NA	NA	22/28	8
Hong Qi (2018)	Gastric cancer	65	60	34/31(I-II/III-IV)	OS	U	25/40	7
Yong-tao Zhang (2020)	Osteosarcoma	49	60	24/25 (I-II/III-IV)	OS	U	24/25	7

This table provides a detailed summary of the characteristics of all studies included in the meta-analysis, including author details, publication year, sample size, cancer type and other relevant metrics.

BIDC: Breast infiltrating ductal carcinoma; M: Multivariate; NA: Not available; NOS: Newcastle Ottawa Scale; NSCLC: Non-small cell lung cancer; OS: Overall survival; OSCC: Oral squamous cell carcinoma; PFS: Progression-free survival; PTC: Papillary thyroid carcinoma; RFS: Relapse-free survival; U: Univariate.

3.2. Relationship between NOB1 expression & OS

Ten articles demonstrated an association between NOB1 expression levels and OS among 1208 patients with cancer, with minor heterogeneity observed among studies (I² = 43.6%, p = 0.046). Therefore, the analysis used a fixed effects model. NOB1 expression was negatively correlated with OS (HR: 2.12, 95% CI: 1.82–2.48, p < 0.001; Figure 2); patients with low NOB1 expression had better OS than those with high NOB1 expression. Moreover, significant associations were observed in both univariate (HR: 2.14, 95% CI: 1.78–2.57, p < 0.001) and multivariate (HR: 2.09, 95% CI: 1.56–2.79, p < 0.001) analyses. Subgroup analyses showed that the association between higher NOB1 tumor expression and worse OS was not significantly affected by characteristics such as cancer types, sample size or follow-up time (Table 2).

Figure 2.

Detailed meta-analysis of pooled hazard ratios (HRs) for overall survival (OS) in patients with cancer. This figure provides a comprehensive meta-analysis of the pooled HRs for OS in patients with cancer. Each part includes forest plots depicting the HRs from individual studies, the corresponding CIs and the overall pooled HR.

Table 2.

Subgroup meta-analysis of pooled hazard ratios for overall survival.

Parameter	Studies (n)	HR (95% CI)	p-value	Heterogeneity
				I 2	p h	Model
Overall	13	2.12 (1.82∼2.48)	<0.001	43.6%	0.046	Fixed-effects
Cancer type						Fixed-effects
NSCLC	4	1.72 (1.40∼2.12)	<0.001	0.0%	0.909
Prostate carcinoma	2	3.47 (2.09∼5.76)	<0.001	12.7%	0.285
Other cancer types	7	2.60 (2.00∼3.38)	<0.001	38.1%	0.138
Sample sizes						Fixed-effects
>100	6	2.73 (2.13∼3.51)	<0.001	26.5%	0.236
<100	7	1.82 (1.49∼2.21)	<0.001	27.2%	0.221
Follow-up (months)						Fixed-effects
>36	7	2.18 (1.75∼2.71)	<0.001	55.8%	0.035
<36	6	2.07 (1.67∼2.58)	<0.001	34.4%	0.178

The results of the subgroup meta-analyses, focusing on the pooled HRs for OS in different cohorts. It details the categorization of subgroups, the number of studies in each, pooled HR values, CIs and statistical significance.

HR: Hazard ratio; NSCLC: Non-small-cell lung cancer.

3.3. Relationship between NOB1 expression & PFS

Three articles indicated an association between NOB1 expression levels and PFS among 282 patients with cancer, with significant heterogeneity observed among studies (I² = 62.9%, p = 0.019). Consequently, the analysis used a random effects model. NOB1 expression was negatively correlated with PFS (HR: 2.23, 95% CI: 1.62–3.07, p < 0.001; Figure 3); patients with low NOB1 expression had better PFS than those with high NOB1 expression. Moreover, significant associations were identified in both univariate (HR: 2.12, 95% CI: 1.38–3.27, p < 0.001) and multivariate (HR: 2.36, 95% CI: 1.35–4.11, p < 0.001) analyses.

Figure 3.

Detailed meta-analysis of pooled hazard ratios (HRs) for progression-free survival (PFS) in patients with cancer. This figure provides a comprehensive meta-analysis of the pooled HRs for PFS in patients with cancer. Each part includes forest plots depicting the HRs from individual studies, the corresponding CIs and the overall pooled HR.

3.4. Relationship between NOB1 expression & clinicopathological characteristics

The relationship between NOB1 expression levels and the clinicopathological traits of patients with cancer has been extensively studied. This meta-analysis includes data from 14 studies, which collectively suggest significant associations between high NOB1 expression and key clinicopathological features. Specifically, data from six studies demonstrated that NOB1 expression was significantly correlated with the degree of tumor differentiation (RR: 2.05, 95% CI: 1.35–3.10, p < 0.001; Supplementary Figure S1 & Table 3). Similarly, data from seven studies indicated that NOB1 expression was significantly correlated with tumor stage (RR: 1.94, 95% CI: 1.06–3.56, p = 0.031; Supplementary Figure S2 & Table 3). Moreover, data from six studies suggested that high NOB1 expression was significantly associated with lymph node metastasis (RR: 1.85, 95% CI: 1.39–2.47, p < 0.001; Supplementary Figure S3 & Table 3). Furthermore, data from five studies demonstrated that high NOB1 expression was significantly associated with distant metastasis (RR: 2.50, 95% CI: 1.20–5.20, p = 0.015; Supplementary Figure S4 & Table 3). Conversely, data from eight and six studies, respectively, indicated no significant associations between NOB1 expression and patients' age (RR: 1.04, 95% CI: 0.92–1.16, p = 0.551; Supplementary Figure S5 & Table 3) or sex (RR: 0.95, 95% CI: 0.75–1.20, p = 0.669; Supplementary Figure S6 & Table 3).

Table 3.

Meta-analysis of the association between NOB1 overexpression and various clinicopathological parameters.

Parameter	Studies (n)	Patients (n)	RR	LCI	UCI	p-value	Heterogeneity
							I 2	P h	Model
Age	8	1083	1.04	0.92	1.16	0.551	0.0%	0.777	Fixed-effects
Sex	6	327	0.95	0.75	1.20	0.669	0.0%	0.838	Fixed-effects
Tumor stage	7	783	1.94	1.06	3.56	0.031	83.9%	<0.001	Random-effects
Lymph node metastasis	6	409	1.85	1.39	2.47	<0.001	51.0%	0.070	Random-effects
Distant metastasis	5	983	2.50	1.20	5.20	0.015	79.4%	<0.001	Random-effects
Tumor differentiation	6	333	2.05	1.35	3.10	0.001	54.2%	0.053	Random-effects

This table presents a comprehensive analysis of the association between NOB1 overexpression and various clinicopathological parameters in patients with cancer. It includes data on tumor stage, lymph node metastasis, distant metastasis, tumor differentiation and other relevant factors, along with their corresponding pooled RRs, CIs and significance levels.

LCI: Lower confidence interval; RR: Risk ratio; UCI: Upper confidence interval.

3.5. Publication bias & sensitivity analysis

The publication bias in this study was assessed by applying Begg's test and Egger's test to the included studies. Begg's test found no significant publication in both the univariate analysis of NOB1 level (Z = 0.52, p = 0.602; Figure 4A) and the multivariate analysis of NOB1 level (Z = -0.34, p = 1.000; Figure 4B). Since Egger's test has a higher sensitivity than Begg's test, Egger's test was further conducted. In addition, Egger's test also found no significant publication in the univariate analysis of NOB1 level (p = 0.429; Figure 4C) and the multivariate analysis of NOB1 level (p = 0.850; Figure 4D). The outcomes of both tests indicated no significant publication bias for OS in this study.

Figure 4.

Analysis of publication bias regarding NOB1 expression and OS in cancer studies. This figure evaluates publication bias in studies examining the association between NOB1 expression and OS in patients with cancer. It includes (A) Begg's funnel plot in univariate analysis, (B) Begg's funnel plot in multivariate analysis, (C) Egger's test results in univariate analysis, (D) Egger's test results in multivariate analysis, (E) sensitivity analysis in multivariate analysis, (F) sensitivity analysis in multivariate analysis.

To evaluate the stability of our meta-analysis' aggregated data, we performed a sensitivity analysis. This analysis revealed that excluding any individual study from the NOB1 level did not markedly affect the combined HRs in either univariate or multivariate analysis, demonstrating the reliability of our pooled results (Figure 4E & F).

4. Discussion

Cancer poses a serious threat to human health and remains incurable. Therefore, researchers are searching for new tumor markers and therapeutic targets to improve the timely diagnosis, treatment and prognosis of patients with cancer [36–38]. Research over the past few years has shown that NOB1 is aberrantly overexpressed in various cancer types and is strongly associated with tumorigenesis, progression and drug resistance during treatment [39]. NOB1 has been reported to help regulate the cell cycle through the ubiquity-proteasome pathway (UPP), a significant intracellular protein degradation pathway [40]. NOB1 is required for ubiquitin-proteasome-dependent hydrolysis. NOB1 interacts with the proteasome 26S subunit non-ATPase 8 (PSMD8/Nin1P/Rpn12) in the 19S regulatory particle of the 26S proteasome, acting as a molecular chaperone and promoting proteasome maturation [41]. Therefore, NOB1 plays a crucial role in UPP-dependent protein hydrolysis, and its abnormal expression could cause the intracellular accumulation of polyubiquitin proteins. In addition, abnormal NOB1 expression leads to the malfunction of the ubiquitin-proteasome system, resulting in the abnormal degradation of oncoproteins and tumor suppressor proteins and blocked apoptosis of mutant cells, leading to tumorigenesis [42]. Moreover, recent studies have shown that the degradation of intranuclear proto-oncoproteins, such as cellular myelocytomatosis virus oncoprotein (c-Myc), cellular oncogene fos (c-Fos), TP53 and EIA, depends on the ubiquitination pathway [43]; considering that NOB1 plays a major role in ubiquitin-dependent hydrolysis, it can be hypothesized that NOB1 may have a role in the degradation of certain proto-oncoproteins and be involved in tumorigenesis and progression. However, the particular molecular mechanism by which NOB1 regulates tumorigenesis and progression is poorly understood at present.

Recent literature underscores the complexity of NOB1's role across various cancer types, highlighting both its prognostic significance and its functional implications in tumorigenesis and cancer progression [9]. Numerous studies have highlighted that NOB1 is an effective new target for the clinical management of various malignancies. Nonetheless, the currently available studies have reported inconsistent results and included inadequate sample sizes. In addition, studies have shown that NOB1 overexpression is not uniformly observed across all cancers, suggesting a context-dependent role in cancer biology. The differential expression of NOB1 in response to treatment stresses the need for a deeper understanding of its molecular underpinnings to potentially guide therapeutic strategies. This meta-analysis is the first to integrate the outcomes of existing studies on NOB1 to further validate its potential prognostic value in tumors. It showed that high NOB1 expression is unfavorable for survival in various cancer types. High NOB1 expression was significantly associated with the tumor stage, the degree of tumor differentiation, lymph node metastasis and distant metastasis, further underscoring the significant association between high NOB1 expression and poor prognosis in patients with cancer. Therefore, our results provide strong evidence that NOB1 is potentially a new predictor of the prognosis of patients with cancer. The observed results of our meta-analysis, while providing a broad overview of NOB1's prognostic value, may be influenced by these cancer-specific roles and responses. Therefore, it is crucial to interpret our findings within the broader landscape of cancer research, acknowledging the heterogeneity and complexity of NOB1's function across different cancer types and treatment modalities. This underscores the necessity for future studies to delve deeper into the mechanistic aspects of NOB1's role in cancer, which could help refine its utility as a prognostic marker and therapeutic target.

In our study, a significant association was observed between NOB1 expression levels and the prognosis of patients with malignant tumors, consistent with the prevailing perspective in current research. However, our study had some limitations. First, the articles included in our study covered only nine cancer types and, therefore, did not represent the full range of malignancies. Second, our meta-analysis focused on HRs to evaluate the prognostic significance of NOB1 expression in cancer because the area under the receiver operating characteristic curve (AUC) and accuracy, critical metrics in diagnostic studies, were not reported in the included articles. This issue highlights a notable gap in the field, where survival studies often emphasize HRs over AUCs due to the former's ability to analyze time-to-event data effectively. However, the absence of AUCs and accuracies limits the comparability of our findings with studies that use these metrics. We recognize the importance of incorporating a diverse range of metrics, including the AUC and accuracy, in future research to enhance the robustness and applicability of prognostic studies. Therefore, we strongly recommend that future research in this domain prioritize the inclusion of AUC values and accuracy. Third, since the studies included in our study were retrospective, the strength of evidence for evidence-based medicine was weak due to the lack of randomized controlled trials. Fourth, while most of the included articles were published in high-impact journals, they were all conducted in China, making the representation of our study weak. Fifth, the patients with cancer included in our study had different tumor stages, surgical approaches and adjuvant treatments, introducing some clinical heterogeneity. Sixth, the inconsistent procedures and determination criteria used by the included studies when measuring NOB1 expression may have increased the heterogeneity in our study, leading to measurement bias. Seventh, the corresponding data taken from Kaplan-Meier curves may contain a few errors, which might impact our final results. Finally, our study had some heterogeneity and bias that could not be completely avoided. Therefore, the conclusions of our meta-analysis must be further validated by higher quality clinical studies.

In conclusion, high NOB1 expression may result in shorter OS in patients with cancer and is associated with their tumor stage, degree of tumor differentiation, lymph node metastasis and distant metastasis. In addition to the significant association between high NOB1 expression and shorter OS, our findings also reveal a substantial association with PFS in patients with cancer. NOB1 expression was negatively correlated with PFS, indicating that patients with elevated NOB1 expression experience faster disease progression or recurrence. This finding underscores the utility of NOB1 not only as a prognostic marker for predicting OS but also for its potential in forecasting disease progression, offering clinicians a comprehensive tool for risk stratification and treatment planning. Therefore, by incorporating both OS and PFS outcomes, our study supports the prognostic significance of NOB1 expression across various cancer types, further cementing its role in prognosis and potentially guiding therapeutic decisions in clinical practice. However, the lack of AUC values and accuracy in the analyzed studies underscores a critical gap in the current prognostic evaluations of NOB1. Regarding future perspective, the prognostic value of NOB1 in multiple tumors will be further validated using larger sample sizes, broader population sources and standardized clinical trials.

Funding Statement

This research was funded by the Clinical Research Incubation Project, West China Hospital, Sichuan University (grant no. 2021HXFH022); the Science and Technology Department of Sichuan Province (grant no. 22GJHZ0015); and the Beijing Medical Award Foundation (grant no. YXJL-2021-1009-0660).

Supplemental material

Supplementary data for this article can be accessed at https://doi.org/10.1080/17520363.2024.2352408

Author contributions

Conceptualization, B Zou, Y Xu; writing-original draft preparation, Y Zhang, Z Li; writing-review and editing, Y Zhang, Z Li, X Chen and Y Huang; supervision, B Zou, Y Xu; funding acquisition, B Zou, Y Xu. All authors have read and agreed to the published version of the manuscript.

Financial disclosure

This research was funded by the Clinical Research Incubation Project, West China Hospital, Sichuan University (grant no. 2021HXFH022); the Science and Technology Department of Sichuan Province (grant no. 22GJHZ0015); and the Beijing Medical Award Foundation (grant no. YXJL-2021-1009-0660). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, stock ownership or options and expert testimony.

Writing disclosure

Medical writing support was provided by Cambridge Proofreading LLC and was funded by Clinical Research Incubation Project, West China Hospital, Sichuan University.