ARHGAP11A affects lung adenocarcinoma (LUAD) and pancreatic adenocarcinoma (PAAD) progression by regulating FAM83A

Introduction

As a leading cause of mortality, cancer poses serious risks not only to individual health but also to public health worldwide (1). Lung cancer is the second most common cancer, and has a high mortality rate (2,3). Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer (2,3). Despite the widespread use of surgical procedures and the development of molecular targeted therapy and immunotherapy in recent years, the five-year survival rate of patients with LUAD remains low (3-6).

Pancreatic adenocarcinoma (PAAD) is a highly invasive tumor of the digestive tract, and the seventh most common cause of cancer-related death (7). The early diagnosis and treatment of the disease are difficult, and the prognosis is poor. Similar to lung cancer, despite the continued widespread use and development of treatment methods and diagnostic biomarkers, the overall five-year survival rate of PAAD still does not exceed 10% due to significant heterogeneity, untimely diagnosis, and high drug resistance (8-11).

The poor prognosis of patients with LUAD and PAAD is largely due to the lack of effective diagnostic markers and therapeutic targets (12-14). Thus, novel molecular targets and early diagnostic markers need to be identified to aid in the treatment of LUAD and PAAD.

As a molecular switch for a biological process, the Rho GTPase cycle mainly depends on the active binding state of guanosine triphosphate (GTP) and the inactive binding state of guanosine diphosphate. The main regulatory factors involved in this process include the Guangdong nucleotide exchange factors and Rho GTPase-activating protein (RhoGAP) (15).

Rho GTPase-activating protein 11A (ARHGAP11A) is a RhoGAP subfamily member that differs from other RhoGAP proteins. Unlike other RhoGAP proteins, ARHGAP11A is often highly expressed in tumors, such as colon cancer, basal-like breast cancer, and hepatocellular carcinoma. The knock down of ARHGAP11A expression inhibits the proliferation of basal breast cancer. Further, decreased ARHGAP11A expression significantly affects the proliferation, invasion, and migration of hepatocellular carcinoma cells. The high expression of ARHGAP11A is also associated with a poor prognosis in LUAD and PAAD. However, research on the specific mechanism of ARHGAP11A in LUAD and PAAD is limited (16-24).

As the smallest member of the FAM83 family, family with sequence similarity 83, member A (FAM83A) is mainly distributed on chromosome 8q24. In recent years, multiple studies have reported that FAM83A is highly expressed in lung, breast, cervical, hepatocellular, and pancreatic cancers, which suggests that FAM83A may be closely related to the occurrence and development of tumors. FAM83A also serves as a diagnostic and prognostic biomarker for non-small cell carcinoma and is closely associated with pathological progression. The knock down of FAM83A significantly inhibits the PI3K/AKT/Snail pathway, thereby blocking tumor cell epithelial-mesenchymal transition. In addition, the close association between FAM83A and programmed cell death ligand 1 (PD-L1) has been reported to enhance the immune escape of tumor cells (25-30). FAM83A also plays an important role in drug resistance and pancreatic cancer cell proliferation (31). However, little is known about the regulatory relationship between these two closely related tumor markers in LUAD and PAAD.

Even under aerobic conditions, the vast majority of tumor cells continue to consume glucose to produce lactate. The consumed glucose is used to synthesize the basic molecules of nucleotides and triglycerides to maintain the controlled growth of tumor organisms. This phenomenon is known as aerobic glycolysis, or the Warburg effect (32-34).

Lactate dehydrogenase A (LDHA) plays an important role in the early stages of glycolysis by regenerating the nicotinamide adenine dinucleotide to maintain an efficient glycolysis rate, and by effectively catalyzing the generation of lactic acid. The acidic environment produced by the lactic acid products also promotes tumor cell invasion, migration, drug resistance, and immune evasion (35-37). Thus, LDHA plays an important role in the occurrence and development of tumors, and has been identified as a potential therapeutic target. However, the mechanism by which ARHGAP11A and FAM83A affect glycolysis by regulating LDHA in LUAD and PAAD is currently unclear.

This study found that ARHGAP11A and FAM83A showed the highest correlation in LUAD and PAAD, and constructed a combined prognostic model of ARHGAP11A and FAM83A to effectively predict patient prognosis. Notably, ARHGAP11A and FAM83A jointly regulate the expression of LDHA and have important effects on tumor proliferation, apoptosis, invasion, migration, and membrane potential. Thus, exploring how ARHGAP11A regulates LDHA by affecting FAM83A expression may have important implications for the prognosis, diagnosis, and treatment of patients. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1961/rc).

Methods

Data collection

The RNA-sequencing data of 32 related tumors were downloaded from The Cancer Genome Atlas (TCGA) database, as were the clinicopathological parameters of 494 cases of LUAD and 177 cases of PAAD, including the survival time, survival status, gender, age, grade, tumor, node, metastasis (T, N, and M), TNM stage. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to generate online box plots of the LDHA differences between LUAD and PAAD.

From 2022 to 2023, three lung cancer tissue samples, three tissue samples of pancreatic cancer and LUAD associated with primary tumors, as well as corresponding adjacent non-tumor tissue samples were collected from the Fudan University Shanghai Cancer Center, fixed in 5% formaldehyde, after which, paraffin-embedded sections were prepared.

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (approval No. 050432-4-2018*), and informed consent was obtained from all the patients.

Multiplex immunofluorescence test

The paraffin sections were first soaked in xylene I–III and ethanol solutions of different concentrations, and then washed in distilled water for 5 min for paraffin removal and rehydration. Afterwards, the slices were placed in a prepared repair solution of Tris Ethylenediaminetetraacetic acid (EDTA) buffer (pH 9.0) and boiled at a high heat for 20–30 min. The slices were then left to cool to room temperature and washed with phosphate-buffered saline (PBS) three times, for 2–3 min each time, to complete antigen repair in the slices. Next, 3% hydrogen peroxide was added to the slices, which were then incubated at room temperature for 15 min to block endogenous peroxidase.

The slices were then incubated with diluted goat serum for 15 min to block non-specific sites. The relevant primary and secondary antibodies were then added, and tyramine signal amplification (TSA) was performed. Each step required incubation at 37 °C for 15 min and washing with PBS three times. The processed slices were placed into the Tris EDTA buffer and microwaved for 20 min to remove the TSA antibody complexes.

In the next round of antibody re-staining, the above steps were repeated starting from the antigen blocking. After which, the cells were incubated with 4’-6-diamidino-2-phenylindole (DAPI) for 5 min, the cell nucleus was stained, and anti-fluorescence quenching agent was added for preservation. Finally, a full scan of all slices was performed using the Vectra 3.0.5 (Akoya Biosciences, Massachusetts, USA​).

The primary antibodies were rabbit-derived anti ARHGAP11A and FAM83A antibodies. The secondary antibodies were anti-rabbit antibodies labeled with horseradish peroxidase. Finally, each group of immunofluorescence positive cells was labeled, and the corresponding total number was calculated using ImageJ software.

Gene Set Enrichment Analysis (GSEA)

A GSEA is a method of gene functional classification that can determine different biological functions based on the enrichment level of specific gene sets (38). RNA-sequencing data related to ARHGAP11A and FAM83A in LUAD and PAAD were used to create specific format files, which were then imported into GSEA4.3.21 software for online analysis. The Hallmark gene set was used as the reference functional set. The statistical threshold for screening was a P value <0.05.

GEPIA

The GEPIA database integrates tissue RNA expression sequencing data from TCGA and GTEX libraries, comprising 9,736 tumors and 8,587 normal tissues. It also contains related clinical information and pathological staging data, which play an important role in the diagnosis, prognosis, and target screening of tumor patients (39).

Cell culture

Human LUAD and pancreatic cancer cell lines PANC1 and A549 were purchased from the Chinese Academy of Sciences (Beijing, China) The cells were cultured at 37 °C with 5% carbon dioxide in a cell incubator. The main components of the complete culture medium used for the cell culture were 10% fetal bovine serum (Gibco, Carlsbad, CA, USA), 89% Dulbecco’s Modified Eagle Medium (Gibco, Carlsbad, CA, USA), and 1% penicillin-streptomycin mixture (Beyotime Biotechnology, Shanghai, China).

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

The cells were seeded in a six-well plate, and the density was controlled at around 60–70% prior to transplantation. Lip2000-Si-ARHGAP11A or FAM83A mixture was added, and the cells were cultured for about 8 h; the primary knockdown sequences of ARHGAP11A and FAM83A are shown in Table 1. Next, the supernatant was discarded, and the cells were washed three times with PBS. Complete culture medium was added, and the cells were incubated for 24–48 h. Total RNA was extracted using Rizol reagent (Seier Fisher Scientific, Massachusetts, USA), ethanol, isopropanol, and chloroform. RT-qPCR experiments were then performed using a reverse transcription kit and the universal SYBR-qPCR master mixture. The synthesized primary primers were mainly obtained from Sangon Biotech (Shanghai, China), and the main sequences are shown in Table 2.

Western blot

The cells were seeded into six-well plates at a seeding density of ~60% and cultured overnight. Next, transfection was performed using a mixture comprising Lip2000, siARHGAP11A, or FAM83A, with a subsequent incubation period of 8 h. Next, the supernatant was discarded and standard complete culture medium was added for 48 h. Radio immunoprecipitation assay (RIPA) lysis solution containing protease inhibitors was then used to break down the protein, and the protein concentration was quantified using the bicinchoninic acid (BCA) protein quantification kit. Next, 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was performed to separate the proteins with different molecular weights, which were transferred onto polyvinylidene fluoride (PVDF) membranes (Beyotime Biotechnology, Shanghai, China) using a fast transfer solution. The PVDF membranes were sealed with fast blocking solution for 10–20 min. After which, primary antibody diluent was added, and the cells were incubated at 4 °C for 8–10 h. The PVDF membranes were washed three times with TBS (Tris Buffered Saline) containing Tween (TBST), for 10 min each time. Secondary antibody diluent was then added, and the cells were incubated at room temperature for 2 h, and then rinsed three times with TBST for 10 min each time. Finally, the final experimental results were displayed using the chemiluminescence method. The ARHGAP11A and FAM83A antibodies were obtained from the Affinity Biosciences (Ohio, USA) and Proteintech companies (Chicago, USA), respectively. The molecular weights of the antibodies were 110 kDa and 50–70 kDa, with catalog numbers of DF4425 and Cat No. 29289-1-AP, respectively.

Glucose and lactate content detection

After interfering with the cells in the six-well culture plate for 48 hours, the supernatant was discarded, and the cells were washed three times with PBS, standard culture medium was then added, after which, the cells were cultured for 8 h. Extract equal volumes of supernatant and test it using a glucose and lactate content detection kit.

Establishment and validation of prognostic models

Univariate Cox regression was used to screen for genes with prognostic value, with a P value threshold of <0.01. Subsequently, multivariate Cox regression analyses were conducted to establish a risk model, and a differential survival analysis and joint survival analysis of the risk genes were performed on the high- and low-risk groups. The relevant scoring formula in the risk model was as follows: risk score = Coef 1 × Gene1 + Coef 2 × Gene2 + ... Coef N × GeneN, where Coef is the risk coefficient calculated by the multivariate Cox regression analysis. A receiver operating characteristic (ROC) curve analysis of the risk model was then performed, and area under the curve (AUC) values were obtained.

The relationship between the risk models and clinical and pathological parameters

First, distribution differences in gender, age, grade, and T, N, M and TNM stage between the high- and low-risk groups were explored. Univariate and multivariate Cox regression analyses were then performed on the clinical and pathological parameters and risk scores. Finally, a ROC curve analysis was performed on the clinical and pathological parameters and risk scores.

Cell Counting Kit-8 (CCK-8) and plate cloning experiment

After 8 h of interference treatment, the cells were seeded into a 96-well plate. Each well had a cell density of 2×1,000 cells. The cells were cultured for 4, 24, 48, and 72 h, and CCK-8 reagent was added at each time point. The cells were incubated at 37 °C for 2–4 h, and the absorbance of the 96 blank plates at each time period was then measured using an enzyme-linked immunosorbent assay (ELISA) reader, with a specified wavelength of 450 nm. After interference, the cells were seeded in six-well culture plate, with a median of approximately 1,000 cells per well. The cells were cultured for approximately 7 days, and the supernatant was discarded. The cells were washed three times with PBS, fixed with formaldehyde, and then stained with crystal violet. Original images were captured using a camera, and the number of cell clones was counted using ImageJ.

Cell flow cytometry, apoptosis, and cycle experiments

After 48 h of cell interference, the cells were collected using EDTA-free trypsin digestion and incubated with fluorescein isothiocyanate (FITC) membrane-associated protein V and propidium iodide (PI) using an apoptosis assay kit for 30 min. Subsequently, another batch of cells treated with interference was fixed with 75% ethanol for 24 h. The cells were incubated with PI using a cell-cycle and apoptosis assay kit for 30 min, washed three times with PBS, and apoptosis and cell-cycle progression were analyzed using flow cytometry.

Cell membrane potential detection

Cells (2×10⁵) were seeded in a six-well plate, treated for 48 h, and washed three times with PBS, after which 1 mL of complete culture medium and 1 mL of JC-1 working solution were added. The cells were then incubated at 37 °C for 30 min. Next, the cells were washed three times with JC-1 buffer, and then underwent trypsin digestion and centrifugation. The supernatant was discarded, and complete culture medium was added to make a cell suspension. Finally, flow cytometry detection was performed. Similar to the previous cell treatment, JC-1 working solution was added to a six- or 96-well plate, and the cells were incubated at 37 °C for 30 min. The cells was washed three times, and normal culture medium was added. The different fluorescence wavelengths of mitochondrial JC-1 monomers or aggregates were measured using an Olympus fluorescence microscope (Olympus Corporation, Tokyo, JPN). Finally, the depolarization of mitochondrial membrane potential was determined by the ratio of green/red fluorescence intensity.

Cell invasion and migration experiments

Fifty percent of the cells were seeded in a six-well plate overnight. After knocking down ARHGAP11A and FAM83A, a 10-microliter tip was used for cell scratching. Serum-free culture medium was added to the six-well plate, and photographs were taken at 0 and 24 h, respectively. The scratch area was calculated using ImageJ, and the cell migration rate was calculated using the following formula: migration rate = (initial scratch area – final scratch area) / initial scratch area × 100%.

In the migration experiment, the cells were seeded in a six-well plate and, ARHGAP11A and FAM83A were knocked down for 24 h. Wash the cell culture dish three times with PBS to remove serum. After centrifugation, the cells were counted and diluted with basic culture medium according to the number of cells. Then, the cells were added to the upper chamber, and serum culture medium was added to the lower chamber. In the invasion experiment, diluted matrix gel (1:10) was added to the upper chamber of the Transwell chamber; the other steps were largely the same as those in the migration experiment.

Statistical analysis

First, the “limma” package in R language was used to analyze the correlation and differences in genes between the different tumors, target genes, and risk groups. The “survival” package in R language was used for the univariate and multivariate Cox regression analyses, and differential survival analysis. The “pROC” package in R language was used to perform the ROC curve analysis on various models and pathological parameters. Afterwards, the “ggplot2” package in R language was used to draw various statistical charts related to bioinformatics. In the cell-related experiments, GraphPad Prism 8 software was mainly used to draw the statistical charts, and student t-tests or Wilcoxon tests were used to compare data between different groups, with each result repeated at least three times.

Results

Tumors with the highest correlation between ARHGAP11A and FAM83A

RNA-sequencing data were collected from 32 types of tumors, and the correlation between ARHGAP11A and FAM83A among all the tumors were analyzed. ARHGAP11A and FAM83A had the highest correlation between LUAD and PAAD (Figure 1A). Additionally, in LUAD and PAAD, the ARHGAP11A high-expression group also exhibited high FAM83A expression, consistent with the expression pattern of ARHGAP11A (Figure 1B,1C).

Figure 1 The tumor types with the highest correlation between ARHGAP11A and FAM83A were screened. (A) Correlation radar map showing the correlation levels of ARHGAP11A and FAM83A in a variety of tumors. (B,C) Box diagram showing differences in the expression of FAM83A in the high- and low-expression ARHGAP11A groups in LUAD and PAAD. *, P<0.05; **, P<0.01; ***, P<0.001. ARHGAP11A, Rho GTPase-activating protein 11A; FAM83A, family with sequence similarity 83, member A; LUAD, lung adenocarcinoma; PAAD, pancreatic adenocarcinoma.

Correlation between ARHGAP11A and FAM83A in LUAD and PAAD tissues

The collected LUAD and PAAD tissues were analyzed using multi-color immunofluorescence, and the cells expressing positive results were labeled and counted. In the same proportion of malignant fields, we found that the expression of ARHGAP11A and FAM83A was low in the normal lung and pancreatic tissues, whereas their expression was high in the tumor tissues (Figure 2A,2B). Notably, the correlation between ARHGAP11A and FAM83A was high across all tissues (Figure 2C).

Figure 2 The differential distribution and correlation of ARHGAP11A and FAM83A in normal and tumor tissues based on multi-color immunofluorescence experiments. (A,B) Distribution differences between ARHGAP11A and FAM83A in normal and tumor groups. (C) The expression correlation of ARHGAP11A and FAM83A in normal and tumor groups. *, P<0.05; **, P<0.01. ARHGAP11A, Rho GTPase-activating protein 11A; DAPI, 4’-6-diamidino-2-phenylindole; FAM83A, family with sequence similarity 83, member A; LUAD, lung adenocarcinoma; PAAD, pancreatic adenocarcinoma.

Significantly enriched pathways and proteins of ARHGAP11A and FAM83A in LUAD and PAAD

LUAD and PAAD transcriptomic data were collected and converted into corresponding formats, according to the requirements of the GSEA online software analysis. We analyzed the enriched pathways of both the ARHGAP11A and FAM83A high-expression groups in LUAD and PAAD using the Hallmark dataset. ARHGAP11A and FAM83A were identified as participating in key signaling pathways enriched in both LUAD and PAAD, including the MTORC1_SIGNALING, MYC_TARGETS_V1, and GLYCOLYSIS signaling pathway (CMSIGS) (Figures 3,4A).

Figure 3 Hallmark analysis of ARHGAP11A and FAM83A using GSEA software in LUAD and PAAD, respectively. (A,B) LUAD. (C,D) PAAD. ARHGAP11A, Rho GTPase-activating protein 11A; FAM83A, family with sequence similarity 83, member A; GSEA, Gene Set Enrichment Analysis; LUAD, lung adenocarcinoma; PAAD, pancreatic adenocarcinoma.

Figure 4 Pathway proteins jointly associated with ARHGAP11A and FAM83A in LUAD and PAAD. (A) In LUAD and PAAD, the Hallmark pathway sets related to ARHGAP11A and FAM83A overlap. (B) In the two types of tumors, ARHGAP11A and FAM83A highly correlated genes overlap. (C) In the two types of tumors, the intersection of ARHGAP11A and FAM83A co-related genes and pathway gene sets. (D) Differences in expression of LDHA between normal and tumor groups based on LUAD and PAAD. (E,F) The effect of different ARHGAP11A and FAM83A combination groups on the expression of LDHA in two types of tumors. *, P<0.05. ARHGAP11A, Rho GTPase-activating protein 11A; FAM83A, family with sequence similarity 83, member A; LDHA, lactate dehydrogenase A; LUAD, lung adenocarcinoma; PAAD, pancreatic adenocarcinoma.

Next, we analyzed the gene sets in tumors that showed a high correlation with both ARHGAP11A and FAM83A, and identified 84 intersecting genes (IntersectingGenes) from the intersection sets (Figure 4B). We collected gene sets from CMSIGS and co-intersected them with IntersectingGenes to obtain LDHA (Figure 4C), a co-significant protein associated with ARHGAP11A and FAM83A. Finally, we used the GEPIA database to analyze and draw differential expression maps of LDHA between normal and tumor groups online (Figure 4D). After stratifying the samples based on ARHGAP11A and FAM83A expression, we observed that LDHA expression was highest in the group with high co-expression of both ARHGAP11A and FAM83A, and lowest in the group with low co-expression of both genes (Figure 4E,4F).

The regulatory relationship between ARHGAP11A, FAM83A, and LDHA based on mRNA and protein levels

After interference treatment in the LUAD A549 cells and PAAD PANC1cells, the knock down of ARHGAP1A resulted in a gradual decrease in FAM83A and LDHA expression at both the RNA and protein levels (Figure 5). After the knock down of FAM83A, the expression of LDHA in the tumor cells also decreased at the messenger RNA (mRNA) and protein levels (Figure 6A,6B,6C). After the knock down of ARHGAP11A and FAM83A, the glucose content of the tumor cells increased while the lactate content decreased (Figure 6D,6E).

Figure 5 At the cellular level, the knock down of ARHGAP11A affected FAM83A and LDHA at the mRNA and protein levels. (A) mRNA. (B) Protein. *, P<0.05; **, P<0.01. ARHGAP11A, Rho GTPase-activating protein 11A; FAM83A, family with sequence similarity 83, member A; LDHA, lactate dehydrogenase A; mRNA, messenger RNA; NC, negative control; siAR, SiARHGAP11A.

Figure 6 At the cellular level, knockdown of FAM83A and ARHGAP11A affects mRNA and protein levels of LDHA, as well as glycolysis levels. (A) mRNA. (B,C) Protein. (D,E) Differences in changes in glucose and lactate content among different ARHGAP11A and FAM83A expression groups. *, P<0.05; **, P<0.01. ARHGAP11A, Rho GTPase-activating protein 11A; FAM83A, family with sequence similarity 83, member A; LDHA, lactate dehydrogenase A; mRNA, messenger RNA; NC, negative control; siAR, SiARHGAP11A.

Establishment of risk model based on ARHGAP11A and FAM83A

A univariate Cox regression analysis was conducted to identify the key genes related to prognosis, and ARHGAP11A and FAM83A were found to be closely related to the prognosis of patients with LUAD and PAAD (Figure 7A). Combined multivariate Cox regression analyses of ARHGAP11A and FAM83A were then conducted to establish a risk model, and significant differences in survival were found between the high- and low-risk groups (Figure 7B,7C). In the combined survival analysis of ARHGAP11A and FAM83A, the group with high expression of both genes had the worst prognosis (Figure 7D). Finally, ROC curves were used to analyze the risk model for the three-year survival period and the AUC values for each time period were generally greater than 0.6 (Figure 7E).

Figure 7 Risk models were established based on ARHGAP11A and FAM83A. (A) Univariate and (B) multivariate Cox regression analyses of ARHGAP11A and FAM83A in LUAD and PAAD. (C) Survival analysis of high- and low-risk groups. (D) Joint survival analysis of ARHGAP11A and FAM83A. (E) Three-year ROC curve analysis of risk models. ARHGAP11A, Rho GTPase-activating protein 11A; AUC, area under the curve; CI, confidence interval; FAM83A, family with sequence similarity 83, member A; LUAD, lung adenocarcinoma; PAAD, pancreatic adenocarcinoma.

Correlation between risk model and clinical and pathological parameters

In LUAD, male sex, TNM stage III–IV, N2–3, T3–4, and M1 status had a higher risk score (Figure 8A), while in PAAD, Grade 3–4 had a higher risk score (Figure 8B). Univariate and multivariate Cox regression analyses revealed that the risk score was a better independent prognostic factor than the other pathological parameters (Figure 8C,8D). A subsequent ROC curve analysis of the risk score and pathological parameters revealed that the AUC value of the risk score was the highest (Figure 8E).

Figure 8 The correlation between the risk models and pathological parameters. (A,B) Differences in risk scores under different pathological parameters. (C,D) Univariate and multivariate Cox regression analyses of risk scores and pathological parameters. (E) ROC curve analysis of risk scores and pathological parameters. AUC, area under the curve; CI, confidence interval; LUAD, lung adenocarcinoma; M, metastasis; N, node; PAAD, pancreatic adenocarcinoma; ROC, receiver operating characteristic; T, tumor.

ARHGAP11A and FAM83A further affects tumor proliferation

In the CCK-8 experiment, after the knock down of ARHGAP11A and FAM83A, the proliferation level of the tumor cells gradually decreased from 48–72 h (Figure 9A). In the plate cloning experiment, the knock down of the target gene resulted in a significant decrease in the number of cloning spots after 7 days of cell proliferation (Figure 9B).

Figure 9 The knock down of ARHGAP11A and FAM83A affected the proliferation of tumor cells. (A) CCK-8. (B) Tablet cloning (crystal staining, scale, 1:1). *, P<0.05; **, P<0.01; ***, P<0.001. ARHGAP11A, Rho GTPase-activating protein 11A; CCK-8, Cell Counting Kit-8; FAM83A, family with sequence similarity 83, member A; NC, negative control; ns, not significant; siAR, SiARHGAP11A; siFM, SiFAM83A.

Cell flow cytometry, apoptosis, and cell-cycle experiments

After the knock down of ARHGAP11A and FAM83A, the apoptosis rate of the tumor cells significantly increased, stagnating in the G1 phase (Figure 10).

Figure 10 The knock down of ARHGAP11A and FAM83A affected the apoptosis and cell cycle of tumor cells. (A) Apoptosis. (B) Cell cycle. *, P<0.05; **, P<0.01. ARHGAP11A, Rho GTPase-activating protein 11A; FAM83A, family with sequence similarity 83, member A; FITC, fluorescein isothiocyanate; LL, lower left; LR, lower right; NC, negative control; ns, not significant; PE, phycoerythrin; siAR, SiARHGAP11A; siFM, SiFAM83A; UL, upper left; UR, upper right quadrant.

The effects of ARHGAP11A and FAM83A on tumor migration and invasion

In the Transwell experiment, the knock down of ARHGAP11A resulted in a significant decrease in the number of migrating and invading tumor cells (Figure 11A,11B). In the scratch test, the knock down group also showed a significant decrease in cell migration and healing levels (Figure 11C,11D).

Figure 11 The knock down of ARHGAP11A and FAM83A affected tumor migration and invasion. (A,B) Transwell experiment (crystal staining, scale: 50 μm). (C,D) Scratch experiment. (scale: 200 μm). *, P<0.05; **, P<0.01; ***, P<0.001. ARHGAP11A, Rho GTPase-activating protein 11A; FAM83A, family with sequence similarity 83, member A; NC, negative control; siAR, SiARHGAP11A; siFM, SiFAM83A.

The effects of ARHGAP11 and FAM83A on mitochondrial membrane potential

After the knock down of ARHGAP11A and FAM83A, the mitochondrial membrane potential of the tumor cells significantly decreased as assessed by flow cytometry and immunofluorescence (Figure 12).

Figure 12 The knock down of ARHGAP11A and FAM83A affected tumor cell membrane potential. (A,B) Flow cytometry. (C,D) Multi-color immunofluorescence (scale: 100 µm). **, P<0.01; ***, P<0.001. ARHGAP11A, Rho GTPase-activating protein 11A; FAM83A, family with sequence similarity 83, member A; FITC, fluorescein isothiocyanate; JC, 5,5',6,6'-tetrachloro-1,1',3,3'-tetraethylbenzimidazolylcarbocyanine iodide; NC, negative control; siAR, SiARHGAP11A; siFM, SiFAM83A.

Discussion

Although LUAD and PAAD occur in different anatomical locations, they remain among the most lethal tumors worldwide. Following advances in research, treatment strategies have expanded to include surgery, chemotherapy, radiation therapy, targeted therapy, and immunotherapy. However, the five-year survival rate of cancer patients is still relatively low. The underlying reasons may be that tumors have the substantial heterogeneity, biological complexity, therapeutic drug resistance, and the lack of effective early diagnostic methods, these factors all pose significant challenges to the treatment and prevention of tumors. Similarly, tumor treatment and its clinical application impose a substantial economic burden on individual patients. Thus, novel prognostic targets and early diagnostic markers for various tumors need to be identified to aid in treatment and disease prevention (1-11,40).

In recent studies, ARHGAP11A and FAM83A have been shown to have an important effect on the occurrence and development of lung cancer, colon cancer, bladder cancer, liver cancer, pancreatic cancer, and other tumors (18-31). However, the mutual regulatory mechanisms and the potential for the joint diagnostic application of ARHGAP11A and FAM83A in tumors remain unclear.

In this study, transcriptomic data from 32 types of tumors were used to explore the correlation between ARHGAP11A and FAM83A. We found that the correlation was the strongest in LUAD and PAAD, and the expression trends of ARHGAP11A and FAM83A were consistent in these two types of tumors.

LUAD and pancreatic cancer tissues were also collected from the Fudan University Shanghai Cancer Center for multi-color immunofluorescence experiments. ARHGAP11A and FAM83A were found to be highly expressed in the tumor tissues, and the correlation between them was high. These findings suggest that ARHGAP11A and FAM83A may have a regulatory relationship.

To further investigate the regulatory relationship between ARHGAP11A and FAM83A in LUAD and PAAD, we conducted an enrichment analysis based on the Hallmark gene set using the GSEA software. The pathways co-enriched with ARHGAP11A and FAM83A included the MTORC1, MYCY-TARGETS_V1, and GLYCOLYSIS signaling pathways. According to relevant literature, mTORC1, MYC-TARGETS_V1, and the GLYCOLYSIS signaling pathways enhance tumor progression and immune escape through multiple molecular mechanisms (32-34,41-43). Notably, ARHGAP11A and FAM83A are closely related to the malignant development of tumors.

We further identified IntersectingGenes by analyzing gene sets that exhibited high correlation with both ARHGAP11A and FAM83A. Additionally, intersecting these IntersectingGenes with the CMSIGS gene sets revealed a common key pathway protein, LDHA. LDHA is a core rate-limiting enzyme in glycolysis. It promotes the conversion of pyruvate to lactate, creates an acidic environment for tumors, and plays an important role in tumor proliferation, drug resistance, invasiveness, and immune escape (35-37).

The subsequent knock down of ARHGAP11A resulted in a significant decrease in FAM83A and LDHA at the mRNA and protein levels. The knock down of FAM83A resulted in a significant decrease in LDHA expression. The knock down of ARHGP11A and FAM83A effectively inhibited glucose consumption and lactate production in tumor cells. Thus, these finding suggest that ARHGAP11A may inhibit the expression of LDHA through FAM83A, thereby reducing the glycolytic ability of tumor cells.

Next, we established a prognostic model based on ARHGAP11A and FAM83A. First, a univariate Cox regression analysis was conducted to identify the prognostic genes. ARHGAP11A and FAM83A were found to be of great significance in the prognosis of LUAD and PAAD. Subsequently, a risk model based on ARHGAP11A and FAM83A was established using multivariate Cox regression analyses. The high- and low-risk groups showed significant survival differences. The combined survival analysis of ARHGAP11A and FAM83A revealed that the patients with high ARHGAP11A and FAM83A expression had the worst prognosis. The ROC curve analysis showed that the AUC values of the three-year risk model were greater than 0.6 for all three time periods, indicating that the risk model had relatively accurate prognostic assessment value.

Relevant clinical and pathological parameters were also collected, and it was found that the LUAD and PAAD patients in the high-risk group were in a stage of pathological progression. Univariate and multivariate Cox regression analyses of the risk score and clinical and pathological parameters were conducted, and it was found that the risk score was a superior independent prognostic factor compared to other pathological factors. Notably, the AUC value of the risk score was also the highest, further suggesting that the risk score had higher prognostic assessment value than the other pathological parameters. In summary, the combined application of ARHGAP11A and FAM83A plays an important role in the prognosis and diagnosis of patients.

We then knocked down ARHGAP11A and FAM83A, and observed a general reduction in tumor cell proliferation, cell-cycle progression, anti-apoptotic ability, mitochondrial membrane potential, migration, and invasion, further suggesting that ARHGAP11A affects tumor development by regulating FAM83A.

This study integrated extensive tumor datasets, bioinformatics analyses, and experimental validations to show that ARHGAP11A affects the occurrence and development of LUAD and PAAD by regulating the expression of FAM83A. However, the specific regulatory mechanisms underlying glycolysis remain incompletely understood. In future experiments, we intend to examine the relevant regulatory mechanisms one by one.

Conclusions

ARHGAP11A affects the occurrence and development of LUAD and PAAD by regulating the expression of FAM83A, and has an important effect on the treatment and prognosis of LUAD and PAAD.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1961/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1961/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1961/prf

Funding: This work was supported by the Reserve Youth Program of Future Plan, Shanghai Medical Innovation and Development Foundation (Nos. WL-HBQN-2022014 and WL-HBQN-2022021K).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1961/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (050432-4-2018*), and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: L. Huleatt)